Chubb 4D Power of Predictive Analytics The State-of-the-art Progresses

Claims Management

Predictive data analytics is coming out of the shadows to change the course of claims management A new approach Chubb 4D provides the tools and expertise to capture and analyze both structured and unstructured claims data The former is what the industry is used to ndash the traditional line-item views of claims as they progress The latter comprises the vital information that does not fit neatly into the rows and columns of a traditional spreadsheet or database such as claim adjuster notes

Why is predictive analytics important to claims management Because it finds relationships in data that achieve a more complete picture of a claim guiding better decisions around its management

This remarkable functionality is now at hand to achieve unparalleled efficiencies and cost-effectiveness when managing claims for workers compensation casualty bodily injury employment practices liability and other financial risks

3

Chubb 4D Power of Predictive Analytics

The Modeling of Claims The typical claim involves an enormous volume of disparate data that accumulates as the claim progresses Take the example of a workers compensation claim The data runs the gamut from the actual claim filing itself to the action plan of the claims examiner the medical file of the injured or ill worker his or her specific personal and economic demographics job tenure and medical history the different medical services medications and physical therapy that were prescribed for the individual the various transactional amounts paid to date and ongoing progress reports on the individualrsquos condition to cite just a few data sets This vast volume of information compiles on a continuous basis over time Making sense of it all for decision-making purposes is extremely challenging given the sheer complexity of the data

Predictive analytics alters this paradigm offering the means to distill and assess Sharper Focus on detailed line-item claims information Predictive Analytics In the hands of insurers and third party

Chubb 4D captures traditional claims administrators such analytical structured data as well as tools can for instance identify unstructured data in its advanced unrecognized potential claims severity analytics models Structured data and the relevant contributing factors includes the traditional information Having this information in hand a claims that is collected as part of the claim professional can take deliberate actions filing and investigation Unstructured to reduce or mitigate the financial losses data focuses on the notes that are generated by the claim routinely written by claims adjusters while the claim remains open By To identify and prescribe specific analyzing both data forms claims actions that can positively affect claims adjusters and supervisors will outcomes insurers need to undertake achieve enhanced visibility into robust claims modeling This has been the driving factors of a particular challenging in the past because the claim This knowledge can then tools available for such modeling had be aggregated across the larger limitations Unable to capture and assess set of similar claims to develop unstructured claims data traditional programs based on more insightful claims models issued predictions based and deliberate actions that target exclusively on structured data With more reducing claim costs of the story now being told the insurerrsquos

ability to reduce the financial impact of a claim is more effective than ever before

4

Chubb 4D Power of Predictive Analytics

Making use of the model Information is everything in business But unless it is given to applicable decision-makers on a timely basis for purposeful actions information becomes stale and of little utility Even worse it may direct bad decisions In the context of predictive analytics the intelligence provided by a model regarding a claimrsquos relative severity is only as useful as the manner in which this information is received and acted upon Obviously the whole point of predictive analytics is to apprise claim teams and company risk managers of something before it occurs

To make good on this value proposition Sharper Focus on Action requires the establishment of two

processes ndash one ensuring that the right For claims data to have value as people receive the intelligence produced actionable information it must be by the model in a timely manner and accessible to prompt dialogue among another requiring specific deliberate those involved in the claims process actions to be taken based on the details Although a model may capture reams Predictive models just inform people must of structured and unstructured intervene to seize upon this information to data these intricate data sets must improve overall program performance be distilled into a comprehensible collection of information To simplify client understanding Chubb 4D produces a model score illustrating the relative severity of a claim a percentage chance of a claim breaching a financial threshold or retention level depending on the model and program The tool then documents the top factors feeding into these scores

5

6

Chubb 4D Power of Predictive Analytics

-

Chubb 4D Power of Predictive Analytics

Leveraging The Information For predictive analytics to perform as intended there must be consistency in execution Execution requires the organization to embrace predictive modeling Actions based on the models must be well defined and supported by technology and procedures that capture and re-enforce the action

Prior planning is critical Simply adding responsibilities to the many tasks that claims teams must complete as part of their caseloads may not be effective as it creates the risk that proper mitigating procedures will not be performed to their conclusion The solution for these deliberate actions to be meaningful and not disruptive is to shift the workload of their claims teams appropriately enhancing their efficiency and effectiveness through their use of the model

Sharper Focus on Insight

Predictive analytics will identify claim characteristics that drive exposure These characteristics coupled with claims handling experience create the opportunity to change the course of a claim To test the efficacy of the actions implemented a before after impact assessment serves as a measurement tool Otherwise how else can program stakeholders be sure that the actions that were taken actually achieved the desired effects Say certain interventions are proposed to reduce the duration of a particular claim One way to test this hypothesis is to go back in time and evaluate the interventions against previous claim experience In other words how does the intervention group of claims compare to the claims that would have been intervened on in the past had the model been in place An analogy to this past-present analysis is the insight that a pharmaceutical trial captures through the use of a placebo and an actual drug but instead of the two approaches running at the same time the placebo group is based on historical experience

7

8

Chubb 4D Power of Predictive Analytics

Why Unstructured Data is Vital The industry has long relied on structured data to make business decisions But unstructured data like claim adjuster notes can be an equally important source of claims intelligence The difficulty in the past has been the capture and organization of this fast-growing source of information With Chubb 4D this is no longer the case

From a claims standpoint trying to predict an outcome without including unstructured data is like putting together a puzzle with missing pieces This is not to imply that structured data has less value than its unstructured cousin Information on a claimantrsquos age injury type and occupation are critical elements in predicting the outcome of the claim But a far richer story can be told when mining and adding relevant unstructured data to the statistical analyses

Such data for instance may include information indicating that a workers compensation claimant recently put on a lot of weight or is under severely stressful conditions at home or at work Maybe the claimant a short time ago started taking a prescription medication that is unrelated to the claim but has claim-related ramifications The medication could influence the treatment plan return-to-work options and claim duration Perhaps the claimant recently separated or divorced a spouse This may affect the physical and emotional support he or she was to receive at home with resulting claims implications Not all of the above examples will be present on enough claims to include in a predictive model but the goal is to identify as many as possible and test each as to they relate to claim outcomes

Unstructured data has vital import to the management of a claim Since this form of information is not static and varies across claims the ability to mine the text across the data set is critical

By combining this dynamic intelligence with more structured data the accuracy of the predictive analytics is enhanced Furthermore the knowledge gleaned from the combined data sets may be used to assist claims teams to ask more in-depth questions of claimants in the future

Sharper Focus on Data

Often buried within a claim adjusterrsquos notes are nuggets of information that can guide better treatment of the claimant or suggest actions that might lower associated claim costs Adjusters routinely compile these notes from the initial investigation of the claim through subsequent medical reports legal notifications and conversations with the employer and claimant This unstructured data for example may indicate that a claimant continually comments about a high level of pain With Chubb 4D claims teams can discern how many times the word ldquopainrdquo appears in the notes Algorithms ndash a set of problem-solving rules or instructions ndash inserted in the model can determine the relationship between the number of times the word appears and the likely severity of the claim Similarly the notes may disclose a claimantrsquos diabetic condition (or other health-related issue) unknown at the time of the claim filing but voluntarily offered up by the claimant in conversation with the adjuster These insights are vital to evolving management strategies and improving a claimrsquos outcome

Chubb 4D Power of Predictive Analytics

A Balancing Act The capacity to mine process and analyze both structured and unstructured data together enhances the predictability of data analytics In other words the more clues the better the ability to deduce an outcome But there is risk in not carefully weighing the value and import of each piece of information before inputting it in the model Overdependence on text for instance or undervaluing such structured information as the type of injury or the claimantrsquos age can result in inferior deductions The goal is to continuously sift the wheat from the chaff

To do this claims teams must be entrusted to capture the same specific high-quality information ndash both structured and unstructured ndash on a consistent basis Otherwise the reliability of the projections will be undependable Claims teams also must establish post-model actions to reduce claims duration andor enhance claimant return-toshywork goals and then scrutinize the effectiveness of these actions on a routine basis Merely running the model is not enough to foster positive change actions and their impact must be consistently measured and monitored

Sharper Focus on Measurement

A major modeling pitfall is measurement as an afterthought Frequently this is caused by a rush to implement the model which results in a failure to record relevant data concerning the actions that were taken over time to reduce claim duration and severity For modeling to be effective actions must be translated into metrics and then monitored to ensure their consistent application Prior to implementing the model insurers need to establish clear processes and metrics as part of planning Otherwise they are flying blind hoping their deliberate actions achieve the desired outcomes

Tools like predictive analytics are only as powerful as the precise processes surrounding their use

9

Chubb 4D Power of Predictive Analytics

The Bottom Line While the science of data analytics continues to improve predictive modeling is not a replacement for experience Seasoned claims professionals and risk managers will always be relied upon to evaluate the mathematical conclusions produced by the models and base their actions on this guidance and their seasoned knowledge

The reason is ndash like people ndash predictive models cannot know everything There will always be nuances subtle shifts in direction or data that has not been captured in the model requiring careful consideration and judgment People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions

Extracting the right information from a model also requires inputting the right data The same can be said for getting the right information at the right time into the hands of the right people Predictive models must produce meaningful analyses that are easily comprehensible and accessible to all relevant decision-makers Finally the information provided should be as straightforward as the actual analytics is complex A report or screen with dozens of complicated variables will not be acted on An approach that filters out the noise and fine-tunes the key variables that have or support a causal relationship with a claimrsquos outcome has a better chance of being read digested and acted upon

Sharper Focus on Chubb 4D

So why consider products embedded with Chubb 4D as your trusted partner in advanced claims analytics We have made substantial investments in this area that continue We have put together a team of nearly 40 professionals who are dedicated to modeling and continue to investigate and deploy the necessary tools to translate data into information And we see great value in our tools helping our customers reduce costs and increase efficiencies

10

11

Chubb 4D Power of Predictive Analytics

rsquo

Contact Us

Scott Henck SVP Claims O 9089032557

wwwchubbcom

Chubb is the marketing name used to refer to subsidiaries of Chubb Limited providing insurance and related services For a list of these subsidiaries please visit our website at wwwchubbcom Insurance provided by Chubb American Insurance Company and its US based Chubb underwriting company affiliates All products may not be available in all states This communication contains product summaries only Coverage is subject to the language of the policies as actually issued Surplus lines insurance sold only through licensed surplus lines producers Chubb is the world s largest publicly traded property and casualty insurance group With operations in 54 countries Chubb provides commercial and personal property and casualty insurance personal accident and supplemental health insurance reinsurance and life insurance to adverse group of clients Chubb Limited the parent company of Chubb is listed on the New York Stock Exchange (NYSE CB) and is a component of the SampP 500 index Copyright copy2016 Form 617546 (Rev 616)

Predictive data analytics is coming out of the shadows to change the course of claims management A new approach Chubb 4D provides the tools and expertise to capture and analyze both structured and unstructured claims data The former is what the industry is used to ndash the traditional line-item views of claims as they progress The latter comprises the vital information that does not fit neatly into the rows and columns of a traditional spreadsheet or database such as claim adjuster notes

Why is predictive analytics important to claims management Because it finds relationships in data that achieve a more complete picture of a claim guiding better decisions around its management

This remarkable functionality is now at hand to achieve unparalleled efficiencies and cost-effectiveness when managing claims for workers compensation casualty bodily injury employment practices liability and other financial risks

3

Chubb 4D Power of Predictive Analytics

The Modeling of Claims The typical claim involves an enormous volume of disparate data that accumulates as the claim progresses Take the example of a workers compensation claim The data runs the gamut from the actual claim filing itself to the action plan of the claims examiner the medical file of the injured or ill worker his or her specific personal and economic demographics job tenure and medical history the different medical services medications and physical therapy that were prescribed for the individual the various transactional amounts paid to date and ongoing progress reports on the individualrsquos condition to cite just a few data sets This vast volume of information compiles on a continuous basis over time Making sense of it all for decision-making purposes is extremely challenging given the sheer complexity of the data

Predictive analytics alters this paradigm offering the means to distill and assess Sharper Focus on detailed line-item claims information Predictive Analytics In the hands of insurers and third party

Chubb 4D captures traditional claims administrators such analytical structured data as well as tools can for instance identify unstructured data in its advanced unrecognized potential claims severity analytics models Structured data and the relevant contributing factors includes the traditional information Having this information in hand a claims that is collected as part of the claim professional can take deliberate actions filing and investigation Unstructured to reduce or mitigate the financial losses data focuses on the notes that are generated by the claim routinely written by claims adjusters while the claim remains open By To identify and prescribe specific analyzing both data forms claims actions that can positively affect claims adjusters and supervisors will outcomes insurers need to undertake achieve enhanced visibility into robust claims modeling This has been the driving factors of a particular challenging in the past because the claim This knowledge can then tools available for such modeling had be aggregated across the larger limitations Unable to capture and assess set of similar claims to develop unstructured claims data traditional programs based on more insightful claims models issued predictions based and deliberate actions that target exclusively on structured data With more reducing claim costs of the story now being told the insurerrsquos

ability to reduce the financial impact of a claim is more effective than ever before

4

Chubb 4D Power of Predictive Analytics

Making use of the model Information is everything in business But unless it is given to applicable decision-makers on a timely basis for purposeful actions information becomes stale and of little utility Even worse it may direct bad decisions In the context of predictive analytics the intelligence provided by a model regarding a claimrsquos relative severity is only as useful as the manner in which this information is received and acted upon Obviously the whole point of predictive analytics is to apprise claim teams and company risk managers of something before it occurs

To make good on this value proposition Sharper Focus on Action requires the establishment of two

processes ndash one ensuring that the right For claims data to have value as people receive the intelligence produced actionable information it must be by the model in a timely manner and accessible to prompt dialogue among another requiring specific deliberate those involved in the claims process actions to be taken based on the details Although a model may capture reams Predictive models just inform people must of structured and unstructured intervene to seize upon this information to data these intricate data sets must improve overall program performance be distilled into a comprehensible collection of information To simplify client understanding Chubb 4D produces a model score illustrating the relative severity of a claim a percentage chance of a claim breaching a financial threshold or retention level depending on the model and program The tool then documents the top factors feeding into these scores

5

6

Chubb 4D Power of Predictive Analytics

-

Chubb 4D Power of Predictive Analytics

Leveraging The Information For predictive analytics to perform as intended there must be consistency in execution Execution requires the organization to embrace predictive modeling Actions based on the models must be well defined and supported by technology and procedures that capture and re-enforce the action

Prior planning is critical Simply adding responsibilities to the many tasks that claims teams must complete as part of their caseloads may not be effective as it creates the risk that proper mitigating procedures will not be performed to their conclusion The solution for these deliberate actions to be meaningful and not disruptive is to shift the workload of their claims teams appropriately enhancing their efficiency and effectiveness through their use of the model

Sharper Focus on Insight

Predictive analytics will identify claim characteristics that drive exposure These characteristics coupled with claims handling experience create the opportunity to change the course of a claim To test the efficacy of the actions implemented a before after impact assessment serves as a measurement tool Otherwise how else can program stakeholders be sure that the actions that were taken actually achieved the desired effects Say certain interventions are proposed to reduce the duration of a particular claim One way to test this hypothesis is to go back in time and evaluate the interventions against previous claim experience In other words how does the intervention group of claims compare to the claims that would have been intervened on in the past had the model been in place An analogy to this past-present analysis is the insight that a pharmaceutical trial captures through the use of a placebo and an actual drug but instead of the two approaches running at the same time the placebo group is based on historical experience

7

8

Chubb 4D Power of Predictive Analytics

Why Unstructured Data is Vital The industry has long relied on structured data to make business decisions But unstructured data like claim adjuster notes can be an equally important source of claims intelligence The difficulty in the past has been the capture and organization of this fast-growing source of information With Chubb 4D this is no longer the case

From a claims standpoint trying to predict an outcome without including unstructured data is like putting together a puzzle with missing pieces This is not to imply that structured data has less value than its unstructured cousin Information on a claimantrsquos age injury type and occupation are critical elements in predicting the outcome of the claim But a far richer story can be told when mining and adding relevant unstructured data to the statistical analyses

Such data for instance may include information indicating that a workers compensation claimant recently put on a lot of weight or is under severely stressful conditions at home or at work Maybe the claimant a short time ago started taking a prescription medication that is unrelated to the claim but has claim-related ramifications The medication could influence the treatment plan return-to-work options and claim duration Perhaps the claimant recently separated or divorced a spouse This may affect the physical and emotional support he or she was to receive at home with resulting claims implications Not all of the above examples will be present on enough claims to include in a predictive model but the goal is to identify as many as possible and test each as to they relate to claim outcomes

Unstructured data has vital import to the management of a claim Since this form of information is not static and varies across claims the ability to mine the text across the data set is critical

By combining this dynamic intelligence with more structured data the accuracy of the predictive analytics is enhanced Furthermore the knowledge gleaned from the combined data sets may be used to assist claims teams to ask more in-depth questions of claimants in the future

Sharper Focus on Data

Often buried within a claim adjusterrsquos notes are nuggets of information that can guide better treatment of the claimant or suggest actions that might lower associated claim costs Adjusters routinely compile these notes from the initial investigation of the claim through subsequent medical reports legal notifications and conversations with the employer and claimant This unstructured data for example may indicate that a claimant continually comments about a high level of pain With Chubb 4D claims teams can discern how many times the word ldquopainrdquo appears in the notes Algorithms ndash a set of problem-solving rules or instructions ndash inserted in the model can determine the relationship between the number of times the word appears and the likely severity of the claim Similarly the notes may disclose a claimantrsquos diabetic condition (or other health-related issue) unknown at the time of the claim filing but voluntarily offered up by the claimant in conversation with the adjuster These insights are vital to evolving management strategies and improving a claimrsquos outcome

Chubb 4D Power of Predictive Analytics

A Balancing Act The capacity to mine process and analyze both structured and unstructured data together enhances the predictability of data analytics In other words the more clues the better the ability to deduce an outcome But there is risk in not carefully weighing the value and import of each piece of information before inputting it in the model Overdependence on text for instance or undervaluing such structured information as the type of injury or the claimantrsquos age can result in inferior deductions The goal is to continuously sift the wheat from the chaff

To do this claims teams must be entrusted to capture the same specific high-quality information ndash both structured and unstructured ndash on a consistent basis Otherwise the reliability of the projections will be undependable Claims teams also must establish post-model actions to reduce claims duration andor enhance claimant return-toshywork goals and then scrutinize the effectiveness of these actions on a routine basis Merely running the model is not enough to foster positive change actions and their impact must be consistently measured and monitored

Sharper Focus on Measurement

A major modeling pitfall is measurement as an afterthought Frequently this is caused by a rush to implement the model which results in a failure to record relevant data concerning the actions that were taken over time to reduce claim duration and severity For modeling to be effective actions must be translated into metrics and then monitored to ensure their consistent application Prior to implementing the model insurers need to establish clear processes and metrics as part of planning Otherwise they are flying blind hoping their deliberate actions achieve the desired outcomes

Tools like predictive analytics are only as powerful as the precise processes surrounding their use

9

Chubb 4D Power of Predictive Analytics

The Bottom Line While the science of data analytics continues to improve predictive modeling is not a replacement for experience Seasoned claims professionals and risk managers will always be relied upon to evaluate the mathematical conclusions produced by the models and base their actions on this guidance and their seasoned knowledge

The reason is ndash like people ndash predictive models cannot know everything There will always be nuances subtle shifts in direction or data that has not been captured in the model requiring careful consideration and judgment People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions

Extracting the right information from a model also requires inputting the right data The same can be said for getting the right information at the right time into the hands of the right people Predictive models must produce meaningful analyses that are easily comprehensible and accessible to all relevant decision-makers Finally the information provided should be as straightforward as the actual analytics is complex A report or screen with dozens of complicated variables will not be acted on An approach that filters out the noise and fine-tunes the key variables that have or support a causal relationship with a claimrsquos outcome has a better chance of being read digested and acted upon

Sharper Focus on Chubb 4D

So why consider products embedded with Chubb 4D as your trusted partner in advanced claims analytics We have made substantial investments in this area that continue We have put together a team of nearly 40 professionals who are dedicated to modeling and continue to investigate and deploy the necessary tools to translate data into information And we see great value in our tools helping our customers reduce costs and increase efficiencies

10

11

Chubb 4D Power of Predictive Analytics

rsquo

Contact Us

Scott Henck SVP Claims O 9089032557

wwwchubbcom

Chubb is the marketing name used to refer to subsidiaries of Chubb Limited providing insurance and related services For a list of these subsidiaries please visit our website at wwwchubbcom Insurance provided by Chubb American Insurance Company and its US based Chubb underwriting company affiliates All products may not be available in all states This communication contains product summaries only Coverage is subject to the language of the policies as actually issued Surplus lines insurance sold only through licensed surplus lines producers Chubb is the world s largest publicly traded property and casualty insurance group With operations in 54 countries Chubb provides commercial and personal property and casualty insurance personal accident and supplemental health insurance reinsurance and life insurance to adverse group of clients Chubb Limited the parent company of Chubb is listed on the New York Stock Exchange (NYSE CB) and is a component of the SampP 500 index Copyright copy2016 Form 617546 (Rev 616)

Chubb 4D Power of Predictive Analytics

The Modeling of Claims The typical claim involves an enormous volume of disparate data that accumulates as the claim progresses Take the example of a workers compensation claim The data runs the gamut from the actual claim filing itself to the action plan of the claims examiner the medical file of the injured or ill worker his or her specific personal and economic demographics job tenure and medical history the different medical services medications and physical therapy that were prescribed for the individual the various transactional amounts paid to date and ongoing progress reports on the individualrsquos condition to cite just a few data sets This vast volume of information compiles on a continuous basis over time Making sense of it all for decision-making purposes is extremely challenging given the sheer complexity of the data

Predictive analytics alters this paradigm offering the means to distill and assess Sharper Focus on detailed line-item claims information Predictive Analytics In the hands of insurers and third party

Chubb 4D captures traditional claims administrators such analytical structured data as well as tools can for instance identify unstructured data in its advanced unrecognized potential claims severity analytics models Structured data and the relevant contributing factors includes the traditional information Having this information in hand a claims that is collected as part of the claim professional can take deliberate actions filing and investigation Unstructured to reduce or mitigate the financial losses data focuses on the notes that are generated by the claim routinely written by claims adjusters while the claim remains open By To identify and prescribe specific analyzing both data forms claims actions that can positively affect claims adjusters and supervisors will outcomes insurers need to undertake achieve enhanced visibility into robust claims modeling This has been the driving factors of a particular challenging in the past because the claim This knowledge can then tools available for such modeling had be aggregated across the larger limitations Unable to capture and assess set of similar claims to develop unstructured claims data traditional programs based on more insightful claims models issued predictions based and deliberate actions that target exclusively on structured data With more reducing claim costs of the story now being told the insurerrsquos

ability to reduce the financial impact of a claim is more effective than ever before

4

Chubb 4D Power of Predictive Analytics

Making use of the model Information is everything in business But unless it is given to applicable decision-makers on a timely basis for purposeful actions information becomes stale and of little utility Even worse it may direct bad decisions In the context of predictive analytics the intelligence provided by a model regarding a claimrsquos relative severity is only as useful as the manner in which this information is received and acted upon Obviously the whole point of predictive analytics is to apprise claim teams and company risk managers of something before it occurs

To make good on this value proposition Sharper Focus on Action requires the establishment of two

processes ndash one ensuring that the right For claims data to have value as people receive the intelligence produced actionable information it must be by the model in a timely manner and accessible to prompt dialogue among another requiring specific deliberate those involved in the claims process actions to be taken based on the details Although a model may capture reams Predictive models just inform people must of structured and unstructured intervene to seize upon this information to data these intricate data sets must improve overall program performance be distilled into a comprehensible collection of information To simplify client understanding Chubb 4D produces a model score illustrating the relative severity of a claim a percentage chance of a claim breaching a financial threshold or retention level depending on the model and program The tool then documents the top factors feeding into these scores

5

6

Chubb 4D Power of Predictive Analytics

-

Chubb 4D Power of Predictive Analytics

Leveraging The Information For predictive analytics to perform as intended there must be consistency in execution Execution requires the organization to embrace predictive modeling Actions based on the models must be well defined and supported by technology and procedures that capture and re-enforce the action

Prior planning is critical Simply adding responsibilities to the many tasks that claims teams must complete as part of their caseloads may not be effective as it creates the risk that proper mitigating procedures will not be performed to their conclusion The solution for these deliberate actions to be meaningful and not disruptive is to shift the workload of their claims teams appropriately enhancing their efficiency and effectiveness through their use of the model

Sharper Focus on Insight

Predictive analytics will identify claim characteristics that drive exposure These characteristics coupled with claims handling experience create the opportunity to change the course of a claim To test the efficacy of the actions implemented a before after impact assessment serves as a measurement tool Otherwise how else can program stakeholders be sure that the actions that were taken actually achieved the desired effects Say certain interventions are proposed to reduce the duration of a particular claim One way to test this hypothesis is to go back in time and evaluate the interventions against previous claim experience In other words how does the intervention group of claims compare to the claims that would have been intervened on in the past had the model been in place An analogy to this past-present analysis is the insight that a pharmaceutical trial captures through the use of a placebo and an actual drug but instead of the two approaches running at the same time the placebo group is based on historical experience

7

8

Chubb 4D Power of Predictive Analytics

Why Unstructured Data is Vital The industry has long relied on structured data to make business decisions But unstructured data like claim adjuster notes can be an equally important source of claims intelligence The difficulty in the past has been the capture and organization of this fast-growing source of information With Chubb 4D this is no longer the case

From a claims standpoint trying to predict an outcome without including unstructured data is like putting together a puzzle with missing pieces This is not to imply that structured data has less value than its unstructured cousin Information on a claimantrsquos age injury type and occupation are critical elements in predicting the outcome of the claim But a far richer story can be told when mining and adding relevant unstructured data to the statistical analyses

Such data for instance may include information indicating that a workers compensation claimant recently put on a lot of weight or is under severely stressful conditions at home or at work Maybe the claimant a short time ago started taking a prescription medication that is unrelated to the claim but has claim-related ramifications The medication could influence the treatment plan return-to-work options and claim duration Perhaps the claimant recently separated or divorced a spouse This may affect the physical and emotional support he or she was to receive at home with resulting claims implications Not all of the above examples will be present on enough claims to include in a predictive model but the goal is to identify as many as possible and test each as to they relate to claim outcomes

Unstructured data has vital import to the management of a claim Since this form of information is not static and varies across claims the ability to mine the text across the data set is critical

By combining this dynamic intelligence with more structured data the accuracy of the predictive analytics is enhanced Furthermore the knowledge gleaned from the combined data sets may be used to assist claims teams to ask more in-depth questions of claimants in the future

Sharper Focus on Data

Often buried within a claim adjusterrsquos notes are nuggets of information that can guide better treatment of the claimant or suggest actions that might lower associated claim costs Adjusters routinely compile these notes from the initial investigation of the claim through subsequent medical reports legal notifications and conversations with the employer and claimant This unstructured data for example may indicate that a claimant continually comments about a high level of pain With Chubb 4D claims teams can discern how many times the word ldquopainrdquo appears in the notes Algorithms ndash a set of problem-solving rules or instructions ndash inserted in the model can determine the relationship between the number of times the word appears and the likely severity of the claim Similarly the notes may disclose a claimantrsquos diabetic condition (or other health-related issue) unknown at the time of the claim filing but voluntarily offered up by the claimant in conversation with the adjuster These insights are vital to evolving management strategies and improving a claimrsquos outcome

Chubb 4D Power of Predictive Analytics

A Balancing Act The capacity to mine process and analyze both structured and unstructured data together enhances the predictability of data analytics In other words the more clues the better the ability to deduce an outcome But there is risk in not carefully weighing the value and import of each piece of information before inputting it in the model Overdependence on text for instance or undervaluing such structured information as the type of injury or the claimantrsquos age can result in inferior deductions The goal is to continuously sift the wheat from the chaff

To do this claims teams must be entrusted to capture the same specific high-quality information ndash both structured and unstructured ndash on a consistent basis Otherwise the reliability of the projections will be undependable Claims teams also must establish post-model actions to reduce claims duration andor enhance claimant return-toshywork goals and then scrutinize the effectiveness of these actions on a routine basis Merely running the model is not enough to foster positive change actions and their impact must be consistently measured and monitored

Sharper Focus on Measurement

A major modeling pitfall is measurement as an afterthought Frequently this is caused by a rush to implement the model which results in a failure to record relevant data concerning the actions that were taken over time to reduce claim duration and severity For modeling to be effective actions must be translated into metrics and then monitored to ensure their consistent application Prior to implementing the model insurers need to establish clear processes and metrics as part of planning Otherwise they are flying blind hoping their deliberate actions achieve the desired outcomes

Tools like predictive analytics are only as powerful as the precise processes surrounding their use

9

Chubb 4D Power of Predictive Analytics

The Bottom Line While the science of data analytics continues to improve predictive modeling is not a replacement for experience Seasoned claims professionals and risk managers will always be relied upon to evaluate the mathematical conclusions produced by the models and base their actions on this guidance and their seasoned knowledge

The reason is ndash like people ndash predictive models cannot know everything There will always be nuances subtle shifts in direction or data that has not been captured in the model requiring careful consideration and judgment People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions

Extracting the right information from a model also requires inputting the right data The same can be said for getting the right information at the right time into the hands of the right people Predictive models must produce meaningful analyses that are easily comprehensible and accessible to all relevant decision-makers Finally the information provided should be as straightforward as the actual analytics is complex A report or screen with dozens of complicated variables will not be acted on An approach that filters out the noise and fine-tunes the key variables that have or support a causal relationship with a claimrsquos outcome has a better chance of being read digested and acted upon

Sharper Focus on Chubb 4D

So why consider products embedded with Chubb 4D as your trusted partner in advanced claims analytics We have made substantial investments in this area that continue We have put together a team of nearly 40 professionals who are dedicated to modeling and continue to investigate and deploy the necessary tools to translate data into information And we see great value in our tools helping our customers reduce costs and increase efficiencies

10

11

Chubb 4D Power of Predictive Analytics

rsquo

Contact Us

Scott Henck SVP Claims O 9089032557

wwwchubbcom

Chubb is the marketing name used to refer to subsidiaries of Chubb Limited providing insurance and related services For a list of these subsidiaries please visit our website at wwwchubbcom Insurance provided by Chubb American Insurance Company and its US based Chubb underwriting company affiliates All products may not be available in all states This communication contains product summaries only Coverage is subject to the language of the policies as actually issued Surplus lines insurance sold only through licensed surplus lines producers Chubb is the world s largest publicly traded property and casualty insurance group With operations in 54 countries Chubb provides commercial and personal property and casualty insurance personal accident and supplemental health insurance reinsurance and life insurance to adverse group of clients Chubb Limited the parent company of Chubb is listed on the New York Stock Exchange (NYSE CB) and is a component of the SampP 500 index Copyright copy2016 Form 617546 (Rev 616)

Chubb 4D Power of Predictive Analytics

Making use of the model Information is everything in business But unless it is given to applicable decision-makers on a timely basis for purposeful actions information becomes stale and of little utility Even worse it may direct bad decisions In the context of predictive analytics the intelligence provided by a model regarding a claimrsquos relative severity is only as useful as the manner in which this information is received and acted upon Obviously the whole point of predictive analytics is to apprise claim teams and company risk managers of something before it occurs

To make good on this value proposition Sharper Focus on Action requires the establishment of two

processes ndash one ensuring that the right For claims data to have value as people receive the intelligence produced actionable information it must be by the model in a timely manner and accessible to prompt dialogue among another requiring specific deliberate those involved in the claims process actions to be taken based on the details Although a model may capture reams Predictive models just inform people must of structured and unstructured intervene to seize upon this information to data these intricate data sets must improve overall program performance be distilled into a comprehensible collection of information To simplify client understanding Chubb 4D produces a model score illustrating the relative severity of a claim a percentage chance of a claim breaching a financial threshold or retention level depending on the model and program The tool then documents the top factors feeding into these scores

5

6

Chubb 4D Power of Predictive Analytics

-

Chubb 4D Power of Predictive Analytics

Leveraging The Information For predictive analytics to perform as intended there must be consistency in execution Execution requires the organization to embrace predictive modeling Actions based on the models must be well defined and supported by technology and procedures that capture and re-enforce the action

Prior planning is critical Simply adding responsibilities to the many tasks that claims teams must complete as part of their caseloads may not be effective as it creates the risk that proper mitigating procedures will not be performed to their conclusion The solution for these deliberate actions to be meaningful and not disruptive is to shift the workload of their claims teams appropriately enhancing their efficiency and effectiveness through their use of the model

Sharper Focus on Insight

Predictive analytics will identify claim characteristics that drive exposure These characteristics coupled with claims handling experience create the opportunity to change the course of a claim To test the efficacy of the actions implemented a before after impact assessment serves as a measurement tool Otherwise how else can program stakeholders be sure that the actions that were taken actually achieved the desired effects Say certain interventions are proposed to reduce the duration of a particular claim One way to test this hypothesis is to go back in time and evaluate the interventions against previous claim experience In other words how does the intervention group of claims compare to the claims that would have been intervened on in the past had the model been in place An analogy to this past-present analysis is the insight that a pharmaceutical trial captures through the use of a placebo and an actual drug but instead of the two approaches running at the same time the placebo group is based on historical experience

7

8

Chubb 4D Power of Predictive Analytics

Why Unstructured Data is Vital The industry has long relied on structured data to make business decisions But unstructured data like claim adjuster notes can be an equally important source of claims intelligence The difficulty in the past has been the capture and organization of this fast-growing source of information With Chubb 4D this is no longer the case

From a claims standpoint trying to predict an outcome without including unstructured data is like putting together a puzzle with missing pieces This is not to imply that structured data has less value than its unstructured cousin Information on a claimantrsquos age injury type and occupation are critical elements in predicting the outcome of the claim But a far richer story can be told when mining and adding relevant unstructured data to the statistical analyses

Such data for instance may include information indicating that a workers compensation claimant recently put on a lot of weight or is under severely stressful conditions at home or at work Maybe the claimant a short time ago started taking a prescription medication that is unrelated to the claim but has claim-related ramifications The medication could influence the treatment plan return-to-work options and claim duration Perhaps the claimant recently separated or divorced a spouse This may affect the physical and emotional support he or she was to receive at home with resulting claims implications Not all of the above examples will be present on enough claims to include in a predictive model but the goal is to identify as many as possible and test each as to they relate to claim outcomes

Unstructured data has vital import to the management of a claim Since this form of information is not static and varies across claims the ability to mine the text across the data set is critical

By combining this dynamic intelligence with more structured data the accuracy of the predictive analytics is enhanced Furthermore the knowledge gleaned from the combined data sets may be used to assist claims teams to ask more in-depth questions of claimants in the future

Sharper Focus on Data

Often buried within a claim adjusterrsquos notes are nuggets of information that can guide better treatment of the claimant or suggest actions that might lower associated claim costs Adjusters routinely compile these notes from the initial investigation of the claim through subsequent medical reports legal notifications and conversations with the employer and claimant This unstructured data for example may indicate that a claimant continually comments about a high level of pain With Chubb 4D claims teams can discern how many times the word ldquopainrdquo appears in the notes Algorithms ndash a set of problem-solving rules or instructions ndash inserted in the model can determine the relationship between the number of times the word appears and the likely severity of the claim Similarly the notes may disclose a claimantrsquos diabetic condition (or other health-related issue) unknown at the time of the claim filing but voluntarily offered up by the claimant in conversation with the adjuster These insights are vital to evolving management strategies and improving a claimrsquos outcome

Chubb 4D Power of Predictive Analytics

A Balancing Act The capacity to mine process and analyze both structured and unstructured data together enhances the predictability of data analytics In other words the more clues the better the ability to deduce an outcome But there is risk in not carefully weighing the value and import of each piece of information before inputting it in the model Overdependence on text for instance or undervaluing such structured information as the type of injury or the claimantrsquos age can result in inferior deductions The goal is to continuously sift the wheat from the chaff

To do this claims teams must be entrusted to capture the same specific high-quality information ndash both structured and unstructured ndash on a consistent basis Otherwise the reliability of the projections will be undependable Claims teams also must establish post-model actions to reduce claims duration andor enhance claimant return-toshywork goals and then scrutinize the effectiveness of these actions on a routine basis Merely running the model is not enough to foster positive change actions and their impact must be consistently measured and monitored

Sharper Focus on Measurement

A major modeling pitfall is measurement as an afterthought Frequently this is caused by a rush to implement the model which results in a failure to record relevant data concerning the actions that were taken over time to reduce claim duration and severity For modeling to be effective actions must be translated into metrics and then monitored to ensure their consistent application Prior to implementing the model insurers need to establish clear processes and metrics as part of planning Otherwise they are flying blind hoping their deliberate actions achieve the desired outcomes

Tools like predictive analytics are only as powerful as the precise processes surrounding their use

9

Chubb 4D Power of Predictive Analytics

The Bottom Line While the science of data analytics continues to improve predictive modeling is not a replacement for experience Seasoned claims professionals and risk managers will always be relied upon to evaluate the mathematical conclusions produced by the models and base their actions on this guidance and their seasoned knowledge

The reason is ndash like people ndash predictive models cannot know everything There will always be nuances subtle shifts in direction or data that has not been captured in the model requiring careful consideration and judgment People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions

Extracting the right information from a model also requires inputting the right data The same can be said for getting the right information at the right time into the hands of the right people Predictive models must produce meaningful analyses that are easily comprehensible and accessible to all relevant decision-makers Finally the information provided should be as straightforward as the actual analytics is complex A report or screen with dozens of complicated variables will not be acted on An approach that filters out the noise and fine-tunes the key variables that have or support a causal relationship with a claimrsquos outcome has a better chance of being read digested and acted upon

Sharper Focus on Chubb 4D

So why consider products embedded with Chubb 4D as your trusted partner in advanced claims analytics We have made substantial investments in this area that continue We have put together a team of nearly 40 professionals who are dedicated to modeling and continue to investigate and deploy the necessary tools to translate data into information And we see great value in our tools helping our customers reduce costs and increase efficiencies

10

11

Chubb 4D Power of Predictive Analytics

rsquo

Contact Us

Scott Henck SVP Claims O 9089032557

wwwchubbcom

Chubb is the marketing name used to refer to subsidiaries of Chubb Limited providing insurance and related services For a list of these subsidiaries please visit our website at wwwchubbcom Insurance provided by Chubb American Insurance Company and its US based Chubb underwriting company affiliates All products may not be available in all states This communication contains product summaries only Coverage is subject to the language of the policies as actually issued Surplus lines insurance sold only through licensed surplus lines producers Chubb is the world s largest publicly traded property and casualty insurance group With operations in 54 countries Chubb provides commercial and personal property and casualty insurance personal accident and supplemental health insurance reinsurance and life insurance to adverse group of clients Chubb Limited the parent company of Chubb is listed on the New York Stock Exchange (NYSE CB) and is a component of the SampP 500 index Copyright copy2016 Form 617546 (Rev 616)

6

Chubb 4D Power of Predictive Analytics

-

Chubb 4D Power of Predictive Analytics

Leveraging The Information For predictive analytics to perform as intended there must be consistency in execution Execution requires the organization to embrace predictive modeling Actions based on the models must be well defined and supported by technology and procedures that capture and re-enforce the action

Prior planning is critical Simply adding responsibilities to the many tasks that claims teams must complete as part of their caseloads may not be effective as it creates the risk that proper mitigating procedures will not be performed to their conclusion The solution for these deliberate actions to be meaningful and not disruptive is to shift the workload of their claims teams appropriately enhancing their efficiency and effectiveness through their use of the model

Sharper Focus on Insight

Predictive analytics will identify claim characteristics that drive exposure These characteristics coupled with claims handling experience create the opportunity to change the course of a claim To test the efficacy of the actions implemented a before after impact assessment serves as a measurement tool Otherwise how else can program stakeholders be sure that the actions that were taken actually achieved the desired effects Say certain interventions are proposed to reduce the duration of a particular claim One way to test this hypothesis is to go back in time and evaluate the interventions against previous claim experience In other words how does the intervention group of claims compare to the claims that would have been intervened on in the past had the model been in place An analogy to this past-present analysis is the insight that a pharmaceutical trial captures through the use of a placebo and an actual drug but instead of the two approaches running at the same time the placebo group is based on historical experience

7

8

Chubb 4D Power of Predictive Analytics

Why Unstructured Data is Vital The industry has long relied on structured data to make business decisions But unstructured data like claim adjuster notes can be an equally important source of claims intelligence The difficulty in the past has been the capture and organization of this fast-growing source of information With Chubb 4D this is no longer the case

From a claims standpoint trying to predict an outcome without including unstructured data is like putting together a puzzle with missing pieces This is not to imply that structured data has less value than its unstructured cousin Information on a claimantrsquos age injury type and occupation are critical elements in predicting the outcome of the claim But a far richer story can be told when mining and adding relevant unstructured data to the statistical analyses

Such data for instance may include information indicating that a workers compensation claimant recently put on a lot of weight or is under severely stressful conditions at home or at work Maybe the claimant a short time ago started taking a prescription medication that is unrelated to the claim but has claim-related ramifications The medication could influence the treatment plan return-to-work options and claim duration Perhaps the claimant recently separated or divorced a spouse This may affect the physical and emotional support he or she was to receive at home with resulting claims implications Not all of the above examples will be present on enough claims to include in a predictive model but the goal is to identify as many as possible and test each as to they relate to claim outcomes

Unstructured data has vital import to the management of a claim Since this form of information is not static and varies across claims the ability to mine the text across the data set is critical

By combining this dynamic intelligence with more structured data the accuracy of the predictive analytics is enhanced Furthermore the knowledge gleaned from the combined data sets may be used to assist claims teams to ask more in-depth questions of claimants in the future

Sharper Focus on Data

Often buried within a claim adjusterrsquos notes are nuggets of information that can guide better treatment of the claimant or suggest actions that might lower associated claim costs Adjusters routinely compile these notes from the initial investigation of the claim through subsequent medical reports legal notifications and conversations with the employer and claimant This unstructured data for example may indicate that a claimant continually comments about a high level of pain With Chubb 4D claims teams can discern how many times the word ldquopainrdquo appears in the notes Algorithms ndash a set of problem-solving rules or instructions ndash inserted in the model can determine the relationship between the number of times the word appears and the likely severity of the claim Similarly the notes may disclose a claimantrsquos diabetic condition (or other health-related issue) unknown at the time of the claim filing but voluntarily offered up by the claimant in conversation with the adjuster These insights are vital to evolving management strategies and improving a claimrsquos outcome

Chubb 4D Power of Predictive Analytics

A Balancing Act The capacity to mine process and analyze both structured and unstructured data together enhances the predictability of data analytics In other words the more clues the better the ability to deduce an outcome But there is risk in not carefully weighing the value and import of each piece of information before inputting it in the model Overdependence on text for instance or undervaluing such structured information as the type of injury or the claimantrsquos age can result in inferior deductions The goal is to continuously sift the wheat from the chaff

To do this claims teams must be entrusted to capture the same specific high-quality information ndash both structured and unstructured ndash on a consistent basis Otherwise the reliability of the projections will be undependable Claims teams also must establish post-model actions to reduce claims duration andor enhance claimant return-toshywork goals and then scrutinize the effectiveness of these actions on a routine basis Merely running the model is not enough to foster positive change actions and their impact must be consistently measured and monitored

Sharper Focus on Measurement

A major modeling pitfall is measurement as an afterthought Frequently this is caused by a rush to implement the model which results in a failure to record relevant data concerning the actions that were taken over time to reduce claim duration and severity For modeling to be effective actions must be translated into metrics and then monitored to ensure their consistent application Prior to implementing the model insurers need to establish clear processes and metrics as part of planning Otherwise they are flying blind hoping their deliberate actions achieve the desired outcomes

Tools like predictive analytics are only as powerful as the precise processes surrounding their use

9

Chubb 4D Power of Predictive Analytics

The Bottom Line While the science of data analytics continues to improve predictive modeling is not a replacement for experience Seasoned claims professionals and risk managers will always be relied upon to evaluate the mathematical conclusions produced by the models and base their actions on this guidance and their seasoned knowledge

The reason is ndash like people ndash predictive models cannot know everything There will always be nuances subtle shifts in direction or data that has not been captured in the model requiring careful consideration and judgment People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions

Extracting the right information from a model also requires inputting the right data The same can be said for getting the right information at the right time into the hands of the right people Predictive models must produce meaningful analyses that are easily comprehensible and accessible to all relevant decision-makers Finally the information provided should be as straightforward as the actual analytics is complex A report or screen with dozens of complicated variables will not be acted on An approach that filters out the noise and fine-tunes the key variables that have or support a causal relationship with a claimrsquos outcome has a better chance of being read digested and acted upon

Sharper Focus on Chubb 4D

So why consider products embedded with Chubb 4D as your trusted partner in advanced claims analytics We have made substantial investments in this area that continue We have put together a team of nearly 40 professionals who are dedicated to modeling and continue to investigate and deploy the necessary tools to translate data into information And we see great value in our tools helping our customers reduce costs and increase efficiencies

10

11

Chubb 4D Power of Predictive Analytics

rsquo

Contact Us

Scott Henck SVP Claims O 9089032557

wwwchubbcom

Chubb is the marketing name used to refer to subsidiaries of Chubb Limited providing insurance and related services For a list of these subsidiaries please visit our website at wwwchubbcom Insurance provided by Chubb American Insurance Company and its US based Chubb underwriting company affiliates All products may not be available in all states This communication contains product summaries only Coverage is subject to the language of the policies as actually issued Surplus lines insurance sold only through licensed surplus lines producers Chubb is the world s largest publicly traded property and casualty insurance group With operations in 54 countries Chubb provides commercial and personal property and casualty insurance personal accident and supplemental health insurance reinsurance and life insurance to adverse group of clients Chubb Limited the parent company of Chubb is listed on the New York Stock Exchange (NYSE CB) and is a component of the SampP 500 index Copyright copy2016 Form 617546 (Rev 616)

-

Chubb 4D Power of Predictive Analytics

Leveraging The Information For predictive analytics to perform as intended there must be consistency in execution Execution requires the organization to embrace predictive modeling Actions based on the models must be well defined and supported by technology and procedures that capture and re-enforce the action

Prior planning is critical Simply adding responsibilities to the many tasks that claims teams must complete as part of their caseloads may not be effective as it creates the risk that proper mitigating procedures will not be performed to their conclusion The solution for these deliberate actions to be meaningful and not disruptive is to shift the workload of their claims teams appropriately enhancing their efficiency and effectiveness through their use of the model

Sharper Focus on Insight

Predictive analytics will identify claim characteristics that drive exposure These characteristics coupled with claims handling experience create the opportunity to change the course of a claim To test the efficacy of the actions implemented a before after impact assessment serves as a measurement tool Otherwise how else can program stakeholders be sure that the actions that were taken actually achieved the desired effects Say certain interventions are proposed to reduce the duration of a particular claim One way to test this hypothesis is to go back in time and evaluate the interventions against previous claim experience In other words how does the intervention group of claims compare to the claims that would have been intervened on in the past had the model been in place An analogy to this past-present analysis is the insight that a pharmaceutical trial captures through the use of a placebo and an actual drug but instead of the two approaches running at the same time the placebo group is based on historical experience

7

8

Chubb 4D Power of Predictive Analytics

Why Unstructured Data is Vital The industry has long relied on structured data to make business decisions But unstructured data like claim adjuster notes can be an equally important source of claims intelligence The difficulty in the past has been the capture and organization of this fast-growing source of information With Chubb 4D this is no longer the case

From a claims standpoint trying to predict an outcome without including unstructured data is like putting together a puzzle with missing pieces This is not to imply that structured data has less value than its unstructured cousin Information on a claimantrsquos age injury type and occupation are critical elements in predicting the outcome of the claim But a far richer story can be told when mining and adding relevant unstructured data to the statistical analyses

Such data for instance may include information indicating that a workers compensation claimant recently put on a lot of weight or is under severely stressful conditions at home or at work Maybe the claimant a short time ago started taking a prescription medication that is unrelated to the claim but has claim-related ramifications The medication could influence the treatment plan return-to-work options and claim duration Perhaps the claimant recently separated or divorced a spouse This may affect the physical and emotional support he or she was to receive at home with resulting claims implications Not all of the above examples will be present on enough claims to include in a predictive model but the goal is to identify as many as possible and test each as to they relate to claim outcomes

Unstructured data has vital import to the management of a claim Since this form of information is not static and varies across claims the ability to mine the text across the data set is critical

By combining this dynamic intelligence with more structured data the accuracy of the predictive analytics is enhanced Furthermore the knowledge gleaned from the combined data sets may be used to assist claims teams to ask more in-depth questions of claimants in the future

Sharper Focus on Data

Often buried within a claim adjusterrsquos notes are nuggets of information that can guide better treatment of the claimant or suggest actions that might lower associated claim costs Adjusters routinely compile these notes from the initial investigation of the claim through subsequent medical reports legal notifications and conversations with the employer and claimant This unstructured data for example may indicate that a claimant continually comments about a high level of pain With Chubb 4D claims teams can discern how many times the word ldquopainrdquo appears in the notes Algorithms ndash a set of problem-solving rules or instructions ndash inserted in the model can determine the relationship between the number of times the word appears and the likely severity of the claim Similarly the notes may disclose a claimantrsquos diabetic condition (or other health-related issue) unknown at the time of the claim filing but voluntarily offered up by the claimant in conversation with the adjuster These insights are vital to evolving management strategies and improving a claimrsquos outcome

Chubb 4D Power of Predictive Analytics

A Balancing Act The capacity to mine process and analyze both structured and unstructured data together enhances the predictability of data analytics In other words the more clues the better the ability to deduce an outcome But there is risk in not carefully weighing the value and import of each piece of information before inputting it in the model Overdependence on text for instance or undervaluing such structured information as the type of injury or the claimantrsquos age can result in inferior deductions The goal is to continuously sift the wheat from the chaff

To do this claims teams must be entrusted to capture the same specific high-quality information ndash both structured and unstructured ndash on a consistent basis Otherwise the reliability of the projections will be undependable Claims teams also must establish post-model actions to reduce claims duration andor enhance claimant return-toshywork goals and then scrutinize the effectiveness of these actions on a routine basis Merely running the model is not enough to foster positive change actions and their impact must be consistently measured and monitored

Sharper Focus on Measurement

A major modeling pitfall is measurement as an afterthought Frequently this is caused by a rush to implement the model which results in a failure to record relevant data concerning the actions that were taken over time to reduce claim duration and severity For modeling to be effective actions must be translated into metrics and then monitored to ensure their consistent application Prior to implementing the model insurers need to establish clear processes and metrics as part of planning Otherwise they are flying blind hoping their deliberate actions achieve the desired outcomes

Tools like predictive analytics are only as powerful as the precise processes surrounding their use

9

Chubb 4D Power of Predictive Analytics

The Bottom Line While the science of data analytics continues to improve predictive modeling is not a replacement for experience Seasoned claims professionals and risk managers will always be relied upon to evaluate the mathematical conclusions produced by the models and base their actions on this guidance and their seasoned knowledge

The reason is ndash like people ndash predictive models cannot know everything There will always be nuances subtle shifts in direction or data that has not been captured in the model requiring careful consideration and judgment People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions

Extracting the right information from a model also requires inputting the right data The same can be said for getting the right information at the right time into the hands of the right people Predictive models must produce meaningful analyses that are easily comprehensible and accessible to all relevant decision-makers Finally the information provided should be as straightforward as the actual analytics is complex A report or screen with dozens of complicated variables will not be acted on An approach that filters out the noise and fine-tunes the key variables that have or support a causal relationship with a claimrsquos outcome has a better chance of being read digested and acted upon

Sharper Focus on Chubb 4D

So why consider products embedded with Chubb 4D as your trusted partner in advanced claims analytics We have made substantial investments in this area that continue We have put together a team of nearly 40 professionals who are dedicated to modeling and continue to investigate and deploy the necessary tools to translate data into information And we see great value in our tools helping our customers reduce costs and increase efficiencies

10

11

Chubb 4D Power of Predictive Analytics

rsquo

Contact Us

Scott Henck SVP Claims O 9089032557

wwwchubbcom

Chubb is the marketing name used to refer to subsidiaries of Chubb Limited providing insurance and related services For a list of these subsidiaries please visit our website at wwwchubbcom Insurance provided by Chubb American Insurance Company and its US based Chubb underwriting company affiliates All products may not be available in all states This communication contains product summaries only Coverage is subject to the language of the policies as actually issued Surplus lines insurance sold only through licensed surplus lines producers Chubb is the world s largest publicly traded property and casualty insurance group With operations in 54 countries Chubb provides commercial and personal property and casualty insurance personal accident and supplemental health insurance reinsurance and life insurance to adverse group of clients Chubb Limited the parent company of Chubb is listed on the New York Stock Exchange (NYSE CB) and is a component of the SampP 500 index Copyright copy2016 Form 617546 (Rev 616)

8

Chubb 4D Power of Predictive Analytics

Why Unstructured Data is Vital The industry has long relied on structured data to make business decisions But unstructured data like claim adjuster notes can be an equally important source of claims intelligence The difficulty in the past has been the capture and organization of this fast-growing source of information With Chubb 4D this is no longer the case

From a claims standpoint trying to predict an outcome without including unstructured data is like putting together a puzzle with missing pieces This is not to imply that structured data has less value than its unstructured cousin Information on a claimantrsquos age injury type and occupation are critical elements in predicting the outcome of the claim But a far richer story can be told when mining and adding relevant unstructured data to the statistical analyses

Such data for instance may include information indicating that a workers compensation claimant recently put on a lot of weight or is under severely stressful conditions at home or at work Maybe the claimant a short time ago started taking a prescription medication that is unrelated to the claim but has claim-related ramifications The medication could influence the treatment plan return-to-work options and claim duration Perhaps the claimant recently separated or divorced a spouse This may affect the physical and emotional support he or she was to receive at home with resulting claims implications Not all of the above examples will be present on enough claims to include in a predictive model but the goal is to identify as many as possible and test each as to they relate to claim outcomes

Unstructured data has vital import to the management of a claim Since this form of information is not static and varies across claims the ability to mine the text across the data set is critical

By combining this dynamic intelligence with more structured data the accuracy of the predictive analytics is enhanced Furthermore the knowledge gleaned from the combined data sets may be used to assist claims teams to ask more in-depth questions of claimants in the future

Sharper Focus on Data

Often buried within a claim adjusterrsquos notes are nuggets of information that can guide better treatment of the claimant or suggest actions that might lower associated claim costs Adjusters routinely compile these notes from the initial investigation of the claim through subsequent medical reports legal notifications and conversations with the employer and claimant This unstructured data for example may indicate that a claimant continually comments about a high level of pain With Chubb 4D claims teams can discern how many times the word ldquopainrdquo appears in the notes Algorithms ndash a set of problem-solving rules or instructions ndash inserted in the model can determine the relationship between the number of times the word appears and the likely severity of the claim Similarly the notes may disclose a claimantrsquos diabetic condition (or other health-related issue) unknown at the time of the claim filing but voluntarily offered up by the claimant in conversation with the adjuster These insights are vital to evolving management strategies and improving a claimrsquos outcome

Chubb 4D Power of Predictive Analytics

A Balancing Act The capacity to mine process and analyze both structured and unstructured data together enhances the predictability of data analytics In other words the more clues the better the ability to deduce an outcome But there is risk in not carefully weighing the value and import of each piece of information before inputting it in the model Overdependence on text for instance or undervaluing such structured information as the type of injury or the claimantrsquos age can result in inferior deductions The goal is to continuously sift the wheat from the chaff

To do this claims teams must be entrusted to capture the same specific high-quality information ndash both structured and unstructured ndash on a consistent basis Otherwise the reliability of the projections will be undependable Claims teams also must establish post-model actions to reduce claims duration andor enhance claimant return-toshywork goals and then scrutinize the effectiveness of these actions on a routine basis Merely running the model is not enough to foster positive change actions and their impact must be consistently measured and monitored

Sharper Focus on Measurement

A major modeling pitfall is measurement as an afterthought Frequently this is caused by a rush to implement the model which results in a failure to record relevant data concerning the actions that were taken over time to reduce claim duration and severity For modeling to be effective actions must be translated into metrics and then monitored to ensure their consistent application Prior to implementing the model insurers need to establish clear processes and metrics as part of planning Otherwise they are flying blind hoping their deliberate actions achieve the desired outcomes

Tools like predictive analytics are only as powerful as the precise processes surrounding their use

9

Chubb 4D Power of Predictive Analytics

The Bottom Line While the science of data analytics continues to improve predictive modeling is not a replacement for experience Seasoned claims professionals and risk managers will always be relied upon to evaluate the mathematical conclusions produced by the models and base their actions on this guidance and their seasoned knowledge

The reason is ndash like people ndash predictive models cannot know everything There will always be nuances subtle shifts in direction or data that has not been captured in the model requiring careful consideration and judgment People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions

Extracting the right information from a model also requires inputting the right data The same can be said for getting the right information at the right time into the hands of the right people Predictive models must produce meaningful analyses that are easily comprehensible and accessible to all relevant decision-makers Finally the information provided should be as straightforward as the actual analytics is complex A report or screen with dozens of complicated variables will not be acted on An approach that filters out the noise and fine-tunes the key variables that have or support a causal relationship with a claimrsquos outcome has a better chance of being read digested and acted upon

Sharper Focus on Chubb 4D

So why consider products embedded with Chubb 4D as your trusted partner in advanced claims analytics We have made substantial investments in this area that continue We have put together a team of nearly 40 professionals who are dedicated to modeling and continue to investigate and deploy the necessary tools to translate data into information And we see great value in our tools helping our customers reduce costs and increase efficiencies

10

11

Chubb 4D Power of Predictive Analytics

rsquo

Contact Us

Scott Henck SVP Claims O 9089032557

wwwchubbcom

Chubb is the marketing name used to refer to subsidiaries of Chubb Limited providing insurance and related services For a list of these subsidiaries please visit our website at wwwchubbcom Insurance provided by Chubb American Insurance Company and its US based Chubb underwriting company affiliates All products may not be available in all states This communication contains product summaries only Coverage is subject to the language of the policies as actually issued Surplus lines insurance sold only through licensed surplus lines producers Chubb is the world s largest publicly traded property and casualty insurance group With operations in 54 countries Chubb provides commercial and personal property and casualty insurance personal accident and supplemental health insurance reinsurance and life insurance to adverse group of clients Chubb Limited the parent company of Chubb is listed on the New York Stock Exchange (NYSE CB) and is a component of the SampP 500 index Copyright copy2016 Form 617546 (Rev 616)

Chubb 4D Power of Predictive Analytics

A Balancing Act The capacity to mine process and analyze both structured and unstructured data together enhances the predictability of data analytics In other words the more clues the better the ability to deduce an outcome But there is risk in not carefully weighing the value and import of each piece of information before inputting it in the model Overdependence on text for instance or undervaluing such structured information as the type of injury or the claimantrsquos age can result in inferior deductions The goal is to continuously sift the wheat from the chaff

To do this claims teams must be entrusted to capture the same specific high-quality information ndash both structured and unstructured ndash on a consistent basis Otherwise the reliability of the projections will be undependable Claims teams also must establish post-model actions to reduce claims duration andor enhance claimant return-toshywork goals and then scrutinize the effectiveness of these actions on a routine basis Merely running the model is not enough to foster positive change actions and their impact must be consistently measured and monitored

Sharper Focus on Measurement

A major modeling pitfall is measurement as an afterthought Frequently this is caused by a rush to implement the model which results in a failure to record relevant data concerning the actions that were taken over time to reduce claim duration and severity For modeling to be effective actions must be translated into metrics and then monitored to ensure their consistent application Prior to implementing the model insurers need to establish clear processes and metrics as part of planning Otherwise they are flying blind hoping their deliberate actions achieve the desired outcomes

Tools like predictive analytics are only as powerful as the precise processes surrounding their use

9

Chubb 4D Power of Predictive Analytics

The Bottom Line While the science of data analytics continues to improve predictive modeling is not a replacement for experience Seasoned claims professionals and risk managers will always be relied upon to evaluate the mathematical conclusions produced by the models and base their actions on this guidance and their seasoned knowledge

The reason is ndash like people ndash predictive models cannot know everything There will always be nuances subtle shifts in direction or data that has not been captured in the model requiring careful consideration and judgment People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions

Extracting the right information from a model also requires inputting the right data The same can be said for getting the right information at the right time into the hands of the right people Predictive models must produce meaningful analyses that are easily comprehensible and accessible to all relevant decision-makers Finally the information provided should be as straightforward as the actual analytics is complex A report or screen with dozens of complicated variables will not be acted on An approach that filters out the noise and fine-tunes the key variables that have or support a causal relationship with a claimrsquos outcome has a better chance of being read digested and acted upon

Sharper Focus on Chubb 4D

So why consider products embedded with Chubb 4D as your trusted partner in advanced claims analytics We have made substantial investments in this area that continue We have put together a team of nearly 40 professionals who are dedicated to modeling and continue to investigate and deploy the necessary tools to translate data into information And we see great value in our tools helping our customers reduce costs and increase efficiencies

10

11

Chubb 4D Power of Predictive Analytics

rsquo

Contact Us

Scott Henck SVP Claims O 9089032557

wwwchubbcom

Chubb is the marketing name used to refer to subsidiaries of Chubb Limited providing insurance and related services For a list of these subsidiaries please visit our website at wwwchubbcom Insurance provided by Chubb American Insurance Company and its US based Chubb underwriting company affiliates All products may not be available in all states This communication contains product summaries only Coverage is subject to the language of the policies as actually issued Surplus lines insurance sold only through licensed surplus lines producers Chubb is the world s largest publicly traded property and casualty insurance group With operations in 54 countries Chubb provides commercial and personal property and casualty insurance personal accident and supplemental health insurance reinsurance and life insurance to adverse group of clients Chubb Limited the parent company of Chubb is listed on the New York Stock Exchange (NYSE CB) and is a component of the SampP 500 index Copyright copy2016 Form 617546 (Rev 616)

Chubb 4D Power of Predictive Analytics

The Bottom Line While the science of data analytics continues to improve predictive modeling is not a replacement for experience Seasoned claims professionals and risk managers will always be relied upon to evaluate the mathematical conclusions produced by the models and base their actions on this guidance and their seasoned knowledge

The reason is ndash like people ndash predictive models cannot know everything There will always be nuances subtle shifts in direction or data that has not been captured in the model requiring careful consideration and judgment People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions

Extracting the right information from a model also requires inputting the right data The same can be said for getting the right information at the right time into the hands of the right people Predictive models must produce meaningful analyses that are easily comprehensible and accessible to all relevant decision-makers Finally the information provided should be as straightforward as the actual analytics is complex A report or screen with dozens of complicated variables will not be acted on An approach that filters out the noise and fine-tunes the key variables that have or support a causal relationship with a claimrsquos outcome has a better chance of being read digested and acted upon

Sharper Focus on Chubb 4D

So why consider products embedded with Chubb 4D as your trusted partner in advanced claims analytics We have made substantial investments in this area that continue We have put together a team of nearly 40 professionals who are dedicated to modeling and continue to investigate and deploy the necessary tools to translate data into information And we see great value in our tools helping our customers reduce costs and increase efficiencies

10

11

Chubb 4D Power of Predictive Analytics

rsquo

Contact Us

Scott Henck SVP Claims O 9089032557

wwwchubbcom

Chubb is the marketing name used to refer to subsidiaries of Chubb Limited providing insurance and related services For a list of these subsidiaries please visit our website at wwwchubbcom Insurance provided by Chubb American Insurance Company and its US based Chubb underwriting company affiliates All products may not be available in all states This communication contains product summaries only Coverage is subject to the language of the policies as actually issued Surplus lines insurance sold only through licensed surplus lines producers Chubb is the world s largest publicly traded property and casualty insurance group With operations in 54 countries Chubb provides commercial and personal property and casualty insurance personal accident and supplemental health insurance reinsurance and life insurance to adverse group of clients Chubb Limited the parent company of Chubb is listed on the New York Stock Exchange (NYSE CB) and is a component of the SampP 500 index Copyright copy2016 Form 617546 (Rev 616)

11

Chubb 4D Power of Predictive Analytics

rsquo

Contact Us

Scott Henck SVP Claims O 9089032557

wwwchubbcom

Chubb is the marketing name used to refer to subsidiaries of Chubb Limited providing insurance and related services For a list of these subsidiaries please visit our website at wwwchubbcom Insurance provided by Chubb American Insurance Company and its US based Chubb underwriting company affiliates All products may not be available in all states This communication contains product summaries only Coverage is subject to the language of the policies as actually issued Surplus lines insurance sold only through licensed surplus lines producers Chubb is the world s largest publicly traded property and casualty insurance group With operations in 54 countries Chubb provides commercial and personal property and casualty insurance personal accident and supplemental health insurance reinsurance and life insurance to adverse group of clients Chubb Limited the parent company of Chubb is listed on the New York Stock Exchange (NYSE CB) and is a component of the SampP 500 index Copyright copy2016 Form 617546 (Rev 616)

rsquo

Contact Us

Scott Henck SVP Claims O 9089032557

wwwchubbcom

Chubb is the marketing name used to refer to subsidiaries of Chubb Limited providing insurance and related services For a list of these subsidiaries please visit our website at wwwchubbcom Insurance provided by Chubb American Insurance Company and its US based Chubb underwriting company affiliates All products may not be available in all states This communication contains product summaries only Coverage is subject to the language of the policies as actually issued Surplus lines insurance sold only through licensed surplus lines producers Chubb is the world s largest publicly traded property and casualty insurance group With operations in 54 countries Chubb provides commercial and personal property and casualty insurance personal accident and supplemental health insurance reinsurance and life insurance to adverse group of clients Chubb Limited the parent company of Chubb is listed on the New York Stock Exchange (NYSE CB) and is a component of the SampP 500 index Copyright copy2016 Form 617546 (Rev 616)