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1 | Digital Accelerators to transform the insurance business

Digital Accelerators to transform the insurance business


Introduction

In today’s technological context, in which consumers are protagonists in the business and control the commercial relationships, companies are experiencing unprecedented pressure to provide a unique offering and to avoid the commoditisation of their products.

The insurance sector is highly aware and affected by this new paradigm. The emergence of new players such as: InsurTechs—or even companies from other sectors such as Tesla that has launched a whole life insurance for its cars sold in Asia—; the new ways of establishing business relationships with customers (smartcontracts, microinsurance, collaborative economy, etc.); the regulatory changes (GDPR, Solvency 2, MIFID2 or IDD); the price war and the diversity of products driven by new technologies are forcing the sector to reinvent itself.

At Minsait, we are directly involved in this transformation, working hand in hand with the main companies in the insurance sector. We collaborate with our clients, aligning their operations with the new business requirements and customer expectations. As partners of the main providers of digital solutions and services, we help our clients anticipate the trends that will be decisive in the transformation of insurance companies.

This study gives account of the experiences and opinions of different insurance and technology experts. With it, we want to share our vision of the 5 key aspects and trends insurance companies should consider for the exploitation and evolution of their back office—understood as a determining factor to transform not only their operations but also their business models.

Minsait


01

05

0302

04

Executive Summary

Bibliography

Our vision

Context

Conclusions


01. Executive Summary

This technological disruption significantly affects the operations of the insurance sector, traditionally characterised by its rigid contractual conditions, highly technical language and the bureaucracy associated with the life cycle of the long-lasting products upon which their IT systems were built.

In the road to sectorial innovation/disruption, choosing the right technologies to adopt as well as how and when to implement them will be decisive. Being mindful of that, we believe that the transformation of insurance companies should be based on 5 fundamental lines of action:

• Create new business models, from the use of new devices and technologies to collaborations with third parties supported by elements such as platforms, verified networks (blockchain), etc.

• Evolve towards an Open IT model that supports these new business models and allows the legacy systems to evolve thanks to microservices, apification and orientation towards public, private and hybrid cloud models.

• Manage the data integrally throughout the application ecosystem, taking into account the cross nature of the data as a continuous asset throughout the value chain.

• Robotise processes from the collection of external and internal data sources necessary for business

processes to the control and monitoring thereof, simplifying the integration between systems, improving efficiency and quality and reducing costs from back to front office.

• Accompany the transformation with new ways of working combining Agile methodologies and LEAN operations with specialisation, transformation and efficiency from a Centre of Excellence (CoE).

• The application of these 5 lines to the operations of insurance companies will contribute to mitigating operational risks, achieve relevant improvements along the entire value chain, increase quality, productivity and accelerate the business to achieve that operations become a competitive advantage.

Technological disruption is having a profound impact on the operations of companies. Technology has passed from being an operation enabler to being the basis for creating business


02. Context

The rapid transformation that industries are undergoing and a general change in how they relate to customers is driving the fast adoption of new technologies by all activity sectors. Terms such as Robotic Process Automation (RPA), Master Data Management (MDM) or Natural Language Processing (NLP)— still incipient in some organisations—will be the foundations their operations and also their business models will be built upon. An example of this is the blockchain technology, initially created to provide support for cryptocurrencies but on which, today, several cases of use are being tested to give rise to future business models (digital identity, supply chain traceability, etc.).

This technological disruption is also affecting the insurance industry. The rapid expansion of the IoT, the clearer viability of driverless vehicles or the investment increase in InsurTechs (which can constantly reinvent their value proposition for specific phases of the value chain without the weight of legacy systems) encourage the rapid development of new products and services by new competitors. All this puts increasing pressure on a sector traditionally characterised by rigid contractual conditions, technical language and the bureaucracy associated with the service life of long-term products on which the legacy IT systems were built.

Therefore, these external impacts (customer, competition and regulatory) and internal impacts (complexity of systems and processes) force the sector to rethink the structure of its operations along its value chain.

However, the evolution of operations cannot be limited to developing or adopting all the technologies that arise in this innovation/disruption path, and the Business Cases based on traditional variables are not always valid as decision drivers (e.g. in the case of innovation). All decisions must therefore be carefully weighed, because

they do not only affect operations but also the overall company strategy, which may involve coexistence with legacy systems. In that sense, asking the necessary questions about what technologies, and how and when to implement them will be decisive in adapting to the new competitive environment.

Operations are being profoundly impacted by the rapid increase use of technology in the new and traditional business


03. Our vision

Insurance companies face the challenge of adapting all their operations to align them with regulatory requirements and with a digital environment that implies working with a new paradigm of costs and times, developing new products with different models of risk calibration and adopting new business models.

To be successful in transforming operations in this new environment we consider 5 fundamental accelerators (building blocks) to mitigate operational risk, increase quality and productivity and accelerate the business to achieve competitive advantage.

To be successful in transforming operations in this new environment we consider 5 fundamental accelerators

Minsait's strategic vision

New business models Open IT

End- to-end data

management

Robotisation of processes

Transformation of work models

Challengers

Leveraged on co-investment models and the creation of collaboration platforms

Based on microservices and apification for different cloud models (public, private and hybrid)

Data driven organization donde el dato es un asset continuo a lo largo de la cadena de valor

From the automation in the collection of data to the operational optimization from back to front

Changing the way of working to respond to the operational transformation

Keyaspects

Move from the competition between traditional and insurtech to a coopetition vision

Scalable architectures to respond to changes in demand without the need to oversize the structure

Establish a Data Management Policy that covers its quality and transversality and complies with the law

Incorporate the RPAs as a "digital workforce", from discovery to operation

Combining Agile, LEAN and specialization methodologies, managed from a Center of Excellence (CoE),

5 Building Blocks to accelerate the transformation of the insurance sector


The insurance sector must adapt its operations towards a new consumption model: easy access to purchase, conceptual simplicity and immediacy.

In this sense, on the one hand, traditional insurance companies increasingly focus on strengthening their innovation areas to evolve their current business models as well as to explore opportunities outside of their core business.

On the other hand, the InsurTech phenomenon is relatively comfortable in the present ecosystem because it is not subject to the limitations of legacy, costs and innovation rhythms. This allows them to aspire to disintermediate the traditional business or capture some phase of the value chain, appropriating the value provided to customers.

This clash of visions is beginning to take place in different channels, especially in the case of brokers where various start-ups have begun to absorb part of the mediated channel. If they succeed in their objective, traditional insurance companies will see a reduction of their negotiation margin, and would be forced to optimise their acquisition and administration ratios.

However, this threat of disintermediation finds its main barrier in the sector legislation (and safeguards for traditional insurance companies). For example, data sharing is currently limited by the Data Protection legislation (i.e., the bancassurance channel cannot access the bank customer file and if they need to share information they have to do so by sharing anonymous files and analyses).

Given the existing regulatory limitation (with the expectation of increasing), a scenario of coexistence between traditional insurance companies and InsurTech is foreseen. This collaboration is taking place thanks to co-innovation models (Venture Capital, As a Service) and the generation of new collaborative platforms.

Co-innovation models

Insurance companies are implementing trend observatories to maintain the pulse of innovation and transformation, and the alliances with Venture Capital companies are often becoming key accelerators to innovate in new business models.

Another co-innovation model successfully used by Minsait is the development of strategic alliances between insurance companies and consultants. This collaboration model is co-funded and explores new business models that encourage joint growth.

An accelerator of this co-innovation model is the use of an Expert Team working in cloud experimentation environments based on disruptive technologies and Open Source. The combination between the team and the business knowledge of insurance companies allows us to focus on value testing and pilots, without worrying about the technological aspects.

New business models

Sandbox as a Service components

Team expert Disruptive technologies

Experimentation

Sandbox as a Service provides the client with the right capabilities, assets, platforms, vision and experience, which can be applied in Plug & Play mode based on the specific needs of the client

+ +


New platforms

Today all organisations want to participate, and even lead, the creation of new business models using new technology platforms to support their operations.

However, this exercise will put to the test their flexibility to change, a particularly limited flexibility in environments where regulations and data can be sensitive, which implies balancing the rigidity of legacy with the criteria imposed by new technologies.

To achieve this balance, controlled and collaborative environments must be created in order to run pilot tests on new business models. In that sense, the integration in digital platforms of multi-sector services, the Blockchain leverage and the generation of new products presents a wide range of possibilities.

Blockchain

Blockchain opens the door to a new level of relationship and collaboration between insurance companies, where new scenarios are proposed with new actors, giving way to a new framework of Coopetition in which the operators stop competing and cooperate (e.g. sharing information about delinquency, fraud, etc.)

Blockchain will be the lever to generate this collaboration between former competitors and other new agents with whom previously there was no collaborative approach due to the existing frictions in both business and operations.

A clear example is Alastria: a cross-sector consortium at a national level, created to jointly analyse and investigate the aspects related to the blockchain, responding to the status quo in this context.

Because of this coopetition panorama, new insurance models are appearing, such as Smart Contracts, which allow, for example, to automatically compensating flight delays/cancellations without needing to file claim.

Multi-sector services platforms

The role of insurance companies finds a new way to complement their traditional business in digital platforms of personalised services, in which they can connect with companies from different industries to offer products jointly.

These platforms allow the development of new collaboration models between companies from different sectors that were unthinkable until a few years ago, for example, allowing customers to use airline miles to purchase travel insurance. Thus, any industry can boost the sale of insurance or become a new distributor.


It allows (in those atomisable processes), to create services or components in a fragmented way with less cost than in a more monolithic architecture, and thus avoids affecting the entire transaction with each change in business.

Micro-services architectures gain efficiency by admitting the reuse of common components from one process to another, investing time in developing only those specific to the branch or the product, as well as segmenting core services from those that are outsourceable, a fragmentation unthinkable in a more traditional architecture.

However, it must be considered that a correct approach to the micro-services model requires a government office. This office must retain control over the components catalogue (in which the technical information and scope at the business level are defined for each of them) to guarantee a scaling model in which the components are not corrupted with their reuse—and where each development responds to a strategy of architecture evolution according to the requirements of each business area of the insurer.

Beyond allowing agile adaptation to business demands and technical transparency, this pattern of software design in components or services also facilitates regulatory compliance (for example PSD2) thanks to the possibilities of Apification. It is a new paradigm that makes services available to be "consumed" by external stakeholders, making it possible for the insurance company to define the consumption model of the services to share, and secure and manage what is shared and what is not. For example, establishing use restrictions, raising different quotes and contracts based on the data consumed by the different stakeholders, etc.

Apification is a relatively simple process and responds (or must respond) to a standardisation scheme. It is a more efficient model because it allows eliminating the manual transmission of information (data) between organisations, contributing to facilitating operations throughout the value chain.

Despite it being a requirement in some environments (to share data and make them public), Apification can become an issue if there is a rigid architecture that does not allow scaling under certain contexts—which may cause systems to crash when faced with a certain volume of requests. The core business should not suffer after having “apified” the systems. It is necessary to find a point of compatibility or coexistence between the legacy systems and the new models, assuring an adequate service level in the traditional backend while the new models are being commissioned.

In this sense, a cloud architecture model allows for scaling up at times of greatest demand without the need to oversize the infrastructure of insurance companies.

Beyond scalability, and opposed to an on-premise model where any change (e.g. adaptation to GDPR) must be taken on board from the insurance company itself, the cloud model has the advantage of delegating these adaptations to third parties who store their data, avoiding the use of large developer teams instead of focusing their resources on business.

Open IT

The rethinking of the insurance company business model requires the evolution towards less watertight and monolithic architectures to sustain it. For this, a back-end architecture based on micro-services (as a natural evolution of the organization in services) can pose an agile and scalable alternative


Greater agility in adapting to business changes

Lower cost in the creation of new services

Greater efficiency, reusing components from one process to another

Management from a Governance Office to:

• Guarantee the scaling

• Meet the business requirements

Greater facility for normative compliance (e.g. PSD2)

Capacity of management and securisation of the information exposed

Greater operational efficiency, eliminating manual processes

Maintenance of the service level to clients from the traditional backend regardless of the volume of requests

Greater scalability when demand peaks occur

Greater agility in adapting to business changes

Lower infrastructure cost

Execution of each application in the optimal platform for it (on premise, public cloud or private cloud) depending on the current regulation, the cost and the criticality for the business

Advantages

Considerations

• Although the cloud is an opportunity to reduce the high infrastructure costs of insurance companies, the strict regulations make the legal security of information to be a critical factor. In some cases, for example, country-specific regulations regulate the geographical location of the data centres.

• Despite this, cloud services are becoming an integral part of all IT strategies. According to Forbes (1), more than 60% of the investment in IT infrastructure in 2018 is cloud-based, and the profit of the global Cloud market will grow by 17% per year (CAGR) until 2020. The most common strategies will be multi-cloud and hybrid cloud. The use of the hybrid cloud tripled in the last year, and has become the preferred strategy for 73% of insurance companies, compared to 18% private cloud and 9% public cloud (2).

• On-premise data centres will continue to be part of the companies' IT domain, but the needs of

applications and the demands of businesses will ultimately determine where the workloads are processed.

• Therefore, a flexible strategy must be defined to fit the best of both worlds at the right time. This strategy must focus on meeting the needs of the applications and providing an agile response to business requirements. It must also provide each application with the optimal platform for its execution, simplify its provision and facilitate the inherent flexible capabilities of the cloud model.

• When it comes to deciding between public or private cloud hosting, the question is no longer whether or not the applications and services that support the business can or should run in a cloud model, but how to establish where these applications and services should run on.

Evolution principles of traditional ecosystems towards new OPEN IT Architecture models

(1) Forbes. Roundup of Cloud Computing Forecasts, 2017 [online], April 2017https://www.forbes.com/sites/louiscolumbus/2017/04/29/roundup-of-cloud-computing-forecasts-2017/#4450775d31e8 [Consulted: 20/2/2018]

(2) Forbes. 2017 State of Cloud Adoption and Security. [online], April 2017 https://www.forbes.com/sites/louiscolumbus/2017/04/23/2017-state-of-cloud-adoption-and-security/#b0fa29618483 [Consulted: 20/2/2018]

Microservices Apification Cloudification


Some of the criteria to consider:

• Security. Based on the type of information managed by the applications.

• Cost. Associated with the efforts to evolve towards a hybrid model from both the operation and the migration process.

• Compliance/regulations. Attending the need to ensure compliance with established regulations and standards.

• Cloud management. Based on the tools used for Cloud management.

• Support for legacy environments. Considering the platforms supported by the applications.

• Communications. Existing mechanisms to connect to the Cloud.

These criteria (and those specific to the scope of each insurance company) will make it possible to determine where the different workloads should be executed:

• Applications/services whose nature (legacy environments), criticality for the business (because of being core business applications) or regulatory requirements, will be executed in an on-premise controlled environment, ensuring the necessary levels of availability and contingency.

• Applications/service, which, due to the type of application, do not have specific requirements to run in an on-premise environment, so they can be executed in a public or private cloud environment,

or new applications that can take advantage of agility and elasticity offering a cloud model for rapid development and deployment.

The applications evolve and change in terms of their level of business criticality. The objective is that they reside where the insurance company's needs are best met, minimising the risk and guaranteeing the levels of continuity and legislation/regulation required.

From an organisational point of view, it is important to adapt the company to a bimodal service provision model that requires:

• Adapting to the new management and operation of Cloud services.

• Maintaining critical systems that will continue to operate as usual in the traditional environment.

In this sense, the biggest benefit of the hybrid cloud model is the power to access the most suitable solution for each of the applications between the different platforms that form the hybrid environment, being able to change that assignment over time. It also provides the general benefits of a cloud environment: agility, elasticity, a better adjustment of the applications requirements, etc.

The execution of the transformation approach of the current IT ecosystem towards a context based on new architecture models - with focus on the exhibition of services to allow optimising operations and generating new business models - requires a complete IT strategy with business vision. The starting point must therefore be a Master and Systems Plan that directs the systems' transformation and evolution.

Evolution roadmap of traditional ecosystems towards new models of OPEN IT Architecture

Ongoing IT evolution Engagement

0

IT Workshop 1

Understanding of the Model

and Business Processes

2

Analysis of the IT Architecture

Model (As-Is)3

Systems Map Evaluation

Segmentation of the Systems map

Business / IT Impact Assessment

Definition of the To-be IT Architecture

Definition of the IT Transformation Plan

Hybrid models

Open Business

Open ITAPI

Micro Services

DataHub

DevOps

Support

Execution of the Transformation PoC

Validation POC impact (IT)

Education and training

Gathering of information

about the Current Systems Map

4

5

6

7

8

9

10

11

12


Such transversality presents great opportunities at both business and operational levels, which, until now, seem to be limited by the silo-based work culture of the different units.

Giving value to the information obtained from onboarding and customer service processes in different channels would improve the quality perceived by customers and would boost the "smart" cross-selling/up-selling business in these processes.

One of the levers to help unlock this limitation in the capture and use of information is to have a comprehensive data policy at company level that covers

the main functions necessary for quality, regulation and transversality (appointing specific persons or units responsible).

The advantages of data management include:

• Data democratisation to facilitate it´s exploitation• Capacity endowment for "data discovery”• Possibility of information "activation"

End-to-end data management

Insurance companies (especially the traditional ones, given their extensive experience in the market) have an almost infinite universe of data scattered across the entire value chain of their applications ecosystem

Data Management Policy

01 03

02

Dimensions that a comprehensive Data Management Policy must cover

Regulation Transversality

Data quality


(3) GDPR Portal [online]. https://www.eugdpr.org [Consulted: 9/3/2018]

Quality

One of the historical problems of the insurance world has been the "data quality" due to its high level of mediation, which implies a more limited control of the data, especially in its introduction.

In this sense, the processes to standardise and purge the information leveraged on capture systems are best practices in the market. An example of this is the digitisation of ID documentation in customer onboarding processes.

As regards IT systems, MDM solutions are becoming increasingly in demand when it comes to defining data management across the company and allowing for incremental use, taking into account both the planning of how to implement it and the tools needed to exploit it.

This data unification and mainstreaming through MDM facilitates advanced data exploitation through BigData. This data exploitation opens up a world of customer-intelligence possibilities that the CRM would provide, as a “user” of the information.

For example, Salesforce and IBM Watson have created mechanisms by which an insurance company can send customer communications before events occur to prevent claims (sending notifications of, for example, extreme weather conditions with precision to prevent material and personal injury).

All the data quality treatment, both of capture systems and MDM and BigData, requires considerable investment. Before initiating an investment process, especially in the case of smaller insurance companies, it is essential to develop a strategic vision and prioritise data quality initiatives to avoid large investments that ignore the potential impact generated or the correct methodology for its exploitation.

Data mainstreaming

The implementation of an MDM system as the single source of data or the simplification of data storage systems would have no effect on data management if it were not for the existence of a crosscutting team overseeing the correct definition of the meaning of


each variable for the business, including the existing implications in each branch.

In that sense, the main concern of insurance companies lies in having comprehensive data governance. This implies not only having the adequate profiles for data management but also establishing a defined set of roles and functions to perform this management, making it possible to…

• … enhance the role of the Chief Data Officer (CDO) in data quality assurance,

• … support the Data Protection Officer (DPO) in ensuring legal compliance,

• … create an analytical government that ensures homogeneous and transversal data management, as well as being observers of what happens outside of the company.

For these new roles and functions, accessibility and sponsorship at the management level will be key factors in executing their mission. It is not important whether these functions are performed in a centralised or federated manner. What is important is that there is a shared communications channel that is effective and autonomous in the areas it is integrated in.

Regulation

The understanding and improvement of data supposes a great cost for the Insurers, especially within the current regulatory context. Regulations such as Solvency II establish a series of data quality criteria, which the General Directorate of Insurance and Pension Funds itself is validating through mystery shopping of Data Marts and information traceability. As from May 2018, the General Data Protection Regulation (GDPR) regulation requires having a completely transparent data governance model (1). This obliges insurance companies to have specialised teams in data legislation, whose non-compliance can involve fines of up to 10% of an insurance company's total billing.

Therefore, establishing a Data Management Policy that takes into account quality, transversality and regulatory requirements is key for insurance companies to have a vision of exploitation along the value chain, enabling them to enrich and transform processes at business an operational level.


The automation of processes using RPAs (Robotic Process Automation or robots that emulate user behaviour in business applications) can provide great value in the end to end data management in three major aspects:

Automating data collection from external sources or from any source that is difficult to integrate:

Robotisation can become another channel to obtain data, using robots by crawling in internal or external sources to obtain the necessary data in business processes in an automated way, or even generating data from the company's own processes.

In this way, not only efficiency is improved (e.g. example, automating the completeness of files that previously required dozens of data from various applications and systems), but also the risk model, product scoring, or even the analytical and predictive model can be improved thanks to the information collected and processed.

Controlling and monitoring processes:

Robotisation can improve the control and monitoring of processes, contributing to the simplification of internal reporting. In this way, a robot can be dedicated to constantly executing a process and if any incident occurs, it can issue an alert to report the malfunction before a customer does, also allowing the improvement in decision-making in real time.

Simplifying integration between systems:

Robotisation allows to non-intrusively solve the linking of data transfer between both legacy and new systems. This integration was previously carried out through integration BUS, customised developments, etc., which are complicated and expensive and did not always guarantee optimal results.

In addition to being a complement for data management, robotisation has other great benefits applied in the back and front processes:

• Efficiency improvement

A high percentage of insurance company backoffice tasks consist of repetitive tasks and of low added-value for the business. With the objective of minimising proprietary resources destined to this type of activities, some companies outsource part of their operations to BPOs, who dedicate personnel exclusively to carrying them out. However, at present, the existence of digital solutions enables the insurance companies themselves to automate their processes without having to resort to third parties, being able to obtain similar efficiency levels or even higher than those obtained thanks to outsourcing.

Not only does robotisation make it possible to switch resources from doing repetitive tasks to others of greater value, it also optimises the work of high value profiles linked to decision roles, resulting in significant economic savings. Thus, beyond the automation of low-value deterministic tasks (A+B=C), the combination of tools such as document managers or RPAs—along with other elements such as Artificial Vision or AI—allows automating tasks that require the interpretation of results, imitating human capacity in decision-oriented roles that require high profile/cost more linked to the business.

• Reduction of customer response times

The back-end actions carried out must be in line with the potential impact generated in the front-end operations. While efficiency and cost savings are often the basis for deciding to carry out robotisation projects, we should not forget the objective of gaining customer loyalty thanks to the quality of the service received.

Thus, robotisation of operations can allow, for example, the resolution of a customer incident in less time, reducing the Average Time of Operation (ATO) in each call or the number of persons involved in the process.

• Improvement of customer response quality

An example of the use of robotisation in this sense is the guided assistance to customer service agents,

Robotisation of processes

Not only does robotization allow dedicating resources previously assigned to repetitive tasks to others of greater value for the business, but it also improves the efficiencyof the work made by high-value profiles linked to decision-making roles, having a significant impact on economic savings


Robotisation implementation stages

guiding the user through one or several applications/platforms and allowing integration of contextual information with indications of the activity to be resolved in each call. This use of robotisation allows the agent to offer customers specific information about their case in real time, as well as to give a homogeneous response according to the company's quality standards regardless of the case and the client to be dealt with.

When deploying robots to obtain all these advantages, it must be stressed that uncontrolled robotisation can create an ungovernable jungle of systems.

Therefore, the processes to be automated must be validated prior to their robotisation, analysing them in-depth. As a consequence, automation must begin with a consultancy that analyses the different processes and defines the action plan for robotisation.

This maturation process begins with a discovery phase of this new technology and success stories in companies similar in size, strategy or sector through events, papers and meetings. Upon making the decision to invest in this new technology, companies enter the evaluation phase.

On the business side, it is important to have the support of the top management—be it from the company, from a specific area or from a division—to identify the processes to be automated and to bet economically for a new technology within the organisation. On the technology side, evaluating both integrators and robotisation solutions is key to the success of the RPA initiative. A common element of companies that have successfully launched an RPA initiative is starting the PoCs format to evaluate suppliers and solutions.

The next stage of automation maturity is Intelligent Robotisation, or iRPA, in which traditional robotisation is combined with cognitive engines (Artificial Intelligence). In this way, the intelligent robot is able to interpret and make decisions to perform high-value tasks that a traditional RPA would be incapable of doing. There are already real experiences, like the automation of the treatment of complex contents, such as powers of attorney or mortgage deeds. The iRPA, which is already a reality, will be one of the keys to the creation of new business models.

Understand what RPA can and can't contribute to the company

Discover

Understand the RPA

Put robots into production by scaling up the initiative and managing human-robot coexistence

Operate

Scale the initiative

Operate robots in production

Evaluate the impact on the

company

CoE at full capacity

Change management

Identify sponsors

Identify RPA leadership

Identify opportunities

Create a Business Case

Realizar PoCs

Conduct PoCs

Select a tool Select provider

Identify areas and processes to invest in

Select solutions and partners

Evaluate

PoC strategy

Design and build the required human and technological capabilities

Implement

Adapt IT infrastructure

Implement first pilots

Train the team

CoE Launch

Define the Governance Model

Evaluate the ROI of the first

pilot tests

Identify new areas

When deploying robots to obtain all these advantages, it must be stressed that uncontrolled robotisation can create an ungovernable jungle of systems


The necessary transformation of insurers' operations must inevitably be accompanied by different ways of working. Traditional methodologies are not effective in attracting and retaining talent (necessary to advance in new products and services based on technological advances) nor are they focused on results.

In this sense, agile methodologies (increasingly implemented) allow this transformation to be carried out in a manner close to the business and validating the results, so that the final product is the best possible product.

Agile methodologies respond to both the transformation needs of the operation and the business requirements. Although in the beginning they were used as an IT implementation methodology, they enable (from a business perspective) to run agile tests on products and services to see how they behave and adapt the operation and the equipment accordingly. In addition, thanks to the agile methodology, it is possible to address the lack of definition of requirements—something common in this type of projects and that traditionally gave rise to

great deviations in temporal and economic planning. An agile methodology provides an exhaustive control of the project progress, which makes it possible to take the right action at any time (without having to wait for the end of a long process of development, discussion, etc.).

Additionally, work must also be done to achieve greater efficiency in productivity and quality, as well as a culture of operational excellence. That is why there is need for a LEAN approach that allows the implementation of a framework and tools for continuous improvement to create a culture of operative excellence.

Agile methodologies, LEAN operations, transformation and efficiency can be managed from a Centre of Excellence (CoE), whose main task is to improve Digital Operations as a "digital workforce" of the company's assets and implement and operate them so that a sustained return over time is obtained.

Transformation of the work models

Degrees of maturity of the CoE for the implementation of RPA initiatives

Establish Capabilities

Adoption

Organisation: support for the RPA initiative by the governing bodies

Capabilities: create the necessary capabilities to respond to RPA needs

Phase 1 CoE adoption

Replicate and Grow

Mature

Organisation: incorporate the RPA into the structure of the company

Capacities: define the optimal growth pyramid (internal vs. providers)

Phase 2 Mature CoE

Differential Performance

Leveraging

Organisation: RPA as a lever for the company's strategy

Capacities: take advantage of synergies and economies of scale with the experience gained

Phase 3 Industrialisation of the CoE

Leve

l of m

atur

ity

Time


Using Process Robotisation as an example, the CoE allows us to have an overall vision of the company in such a way that—by aligning the particular objectives of each implementation to the corporate ones—we can take advantage of the RPAs as a global competitive advantage for the entire company.

Thus, to achieve objectives such as increasing efficiency and productivity, greater agility when carrying out operations, greater control and IT security or the reduction of operational risks, the CoE must previously define them according to the corporate strategic vision, anticipate the impact of the expected benefits at the corporate level and understand how the RPAs can represent a market differentiation.

In addition, for this, the organisational design of the CoE must be in line with the company's design in such a way that it supports the delivery of automation capabilities and aligns with the company's organisational strategy and culture.

Therefore, the adoption of new work methodologies, as well as the creation of CoEs, plays a key role contributing not only to drive internal operational transformation (achieving efficiency objectives, quality improvement, etc.) but also to offer a better customer service thanks, for example, to the shorter time to market or to the improvement of their general experience in the use of insurance services.


04. Conclusions

Technology, which at present has gone from being an enabler to being the base on which to create business, is profoundly affecting the operations of companies.

This disruption significantly affects the insurance sector, where the right choice of technology and how to build and streamline it will help mitigate operational risk, achieve relevant improvements along the entire value chain, increase quality and productivity, and accelerate the business to achieve that the operations become a competitive advantage.

We at Minsait believe that the creation of new business models thanks to technology, the evolution of architecture towards new OPEN IT models, the end-to-end data management in all operations, the robotisation of processes, and the accompaniment of the transformation with new forms of work will be the 5 main pillars to succeed in this digital transformation of the insurance sector.

The right choice of technology and how to build and streamline it will help mitigate operational risk, achieve relevant improvements along the entire value chain


Bibliography

1. GDPR Portal (online) https://www.eugdpr.org Consulted: 9/3/2018

2. Forbes. Roundup of Cloud Computing Forecasts, 2017 (online) https://www.forbes.com/sites/louiscolumbus/2017/04/29/roundup-of-cloud-computing-forecasts-2017/#4450775d31e8 Consulted: 20/2/2018

3. Forbes. 2017 State of Cloud Adoption And Security (online) https://www.forbes.com/sites/louiscolumbus/2017/04/23/2017-state-of-cloud-adoption-and-security/#b0fa29618483 Consulted: 20/2/2018


Authors

Collaborations

Roberto [email protected], Digital Practices

Jaume [email protected] Manager, Financial Services and Insurance Consulting

Ignacio [email protected] Manager, Financial Services and Insurance Consulting

Jose Javier Laínez [email protected], Digital Practices

Alejandra García [email protected] Consultant, Digital Practices

Manuel Pascua [email protected], Financial Services and Insurance Consulting

Sergio Chico Hormigos [email protected]

Alberto López Santamaría aasantamarí[email protected]

Fernando Cuenca [email protected]

Jordi Ponsa Siñol [email protected]

Javier Martínez [email protected])

Julio Blanco Rosa [email protected]

Albert Guilló Muñoz [email protected]

María Russinol Mussons [email protected]

Fernando Font Valle [email protected]


Minsait is the Indra business unit that responds to the challenges which digital transformation poses for companies and institutions. Indra is one of the leading global consulting and technology companies and the technology partner for the key businesses of its clients around the world

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