the future of deciding

© 2016 Fair Isaac Corporation. All rights reserved. 1

White PAPeR

How Technology and Big Data Will Make Tomorrow’s Decisions Different from Today’s

By Dr. Stuart Wells

A proliferation of data and the invention of new technologies are combining to change the very way people make decisions. These forces are also changing the very nature of the decisions we make.

The way we decide

is changing.

Every conscious act begins with a decision.

Get up or stay in bed? Regular or decaf? Hold ’em or fold ’em?

As individuals, we make a lot of decisions. In fact, we spend nearly every waking moment deciding. We make conscious decisions an estimated 35,000 times a day. Just deciding what food to eat, cook or buy amounts to 220 decisions daily.1

And when we interact with others—in families, in businesses, in societies—the complexity of these decisions is multiplied. Variables mount. New influences are brought to bear. When you think about it, it’s a wonder we manage to get anything done at all.

Now the act of deciding is changing. Today we have access to all kinds of data. While this can inform our decisions, it can also make deciding more complicated. Fortunately, new technologies, online services and mobile apps have emerged to make decisions easier by giving us tools to analyze our options. Reviews on Amazon

1 “35,000 Decisions: The Great Choices of Strategic Leaders,” Dr. Joel Hoomans, Leading Edge Journal, Roberts Wesleyan College, March 20, 2015, accessed Feb. 8, 2016, at http://go.roberts.edu/leadingedge/the-great-choices-of-strategic-leaders

WHITE PAPERThe Future of Deciding


and apps such as TripAdvisor and Yelp! have revolutionized the decision process for consumers, creating a more analytical decision process. This has become so ingrained, in fact, that 7 out of 10 consumers say they check reviews before deciding to purchase a product or service. And among millennials, that number rises to more than 8 in 10, which suggests that in the future, our daily lives will be filled with more analytical deciding, not less.2

We are seeing this play out in our daily work lives as well. For example, Human Capital Management (HCM) platforms use analytics to inform executives where they are understaffed (which could trigger an increase in absences) or which departments are paying employees less than industry-standard wages (a red flag that those workers may be prime poaching targets for competitors).

Analytics in demand.

As decisions grow more complex, businesses and government agencies are making even greater use of analytics on a vast scale.

To conduct enterprise-class analytics, many large organizations rely on specialists known as data scientists. The analytics pioneers who founded my company 60 years ago were among the early professional data scientists. They developed sophisticated ways to model complex operational decisions with the goal of improving the outcomes of those decisions.

These pioneers invented the first credit score—a globally recognized measure of credit risk—which helped banks and lenders decide who to lend money to and what interest rate to charge consumers. They also helped insurers determine the risk of loss, which is crucial to setting premiums that are affordable for customers but still protect underwriters from devastating payouts. In one case, they even helped an energy company decide whether or not an oil rig could be safely towed.

2 “Nearly 70% of Consumers Rely on Online Reviews Before Making a Purchase,” Ashlee Kieler, Consumerist, June 3, 2015, accessed Feb. 5, 2016, at https://consumerist.com/2015/06/03/nearly-70-of-consumers-rely-on-online-reviews-before-making-a-purchase/

In the future, our daily lives will be filled with more analytical deciding, not less.



The problematic role of Big Data.

Unlike their predecessors from decades ago, today’s experts now have at their disposal Big Data—the growing mass of information created by applications, devices, sensors, point-of-sale systems, social networks and more.

Used properly, Big Data can help a business decide when to launch a new product, at what price and in which geographical regions. Or it can help reveal previously hidden risks associated with a loan or investment. For companies wanting to glean insight that will shape their decisions and lead to more sales, lower expenses and improved margins, the possibilities of Big Data are endless.

It’s little wonder, then, that as decision-making grows more sophisticated, Big Data is becoming central to the way organizations work. According to a study by 451 Research, 95 percent of IT decision-makers expect Big Data volumes and the number of data sources they use to grow.3 But even as Big Data proliferates, organizations report they aren’t well equipped to use it. More than 8 in 10 (85 percent) of organizations tell Gartner they aren’t able to exploit Big Data for competitive advantage.4

This leaves an enormous gap between expectations and reality: If Big Data really is the key to making crucial decisions, why are we falling short?

Obviously, this isn’t for lack of data. These organizations either lack the appropriate tools and analytics, or they aren’t equipped to separate the signal (the info they really need to make a decision) from the noise (everything else). They try to “boil the ocean,” swimming about in too much data without a systemic approach that would enable them to eliminate irrelevant or misleading hypothetical scenarios. It’s extremely difficult to pull value from Big Data if you don’t know the question you’re trying to answer.

Trying to boil the ocean leads to other problems. Nassim Taleb, author of The Black Swan: The Impact of the Highly Improbable, has observed that given a dataset large enough, decision-makers can find all kinds of dubious statistical relationships as they attempt to find a needle in a haystack that quite literally grows larger by the minute. They are, in a sense, “fooled by randomness” as they cherry-pick data and spurious correlations that lead them down the wrong path.5

The risk becomes even more pronounced when you realize that all data stems from a series of previous decisions. Take the data a bank has on its customers’ loan performance. That performance has been influenced by decisions the bank

3 “The State of Enterprise Data Quality 2016,” Carl Lehmann, Krishna Roy, Bob Winter, 451 Research Advisory, January 2016, accessed Feb. 5, 2016, at http://siliconangle.com/files/2016/01/Blazent_State_of_Data_Quality_Management_2016.pdf

4 “Only 15% of Fortune 500 Companies Use Big Data Analytics to Look Beyond the ‘Known-Knowns’ – Why?” Andy Johnston, Forbes, June 24, 2015, accessed on April 15, 2016, at http://www.forbes.com/sites/teradata/2015/06/24/why-only-15-of-the-fortune-500-uses-big-data-analytics-to-look-beyond-the-known-knowns/#675ba5451bc9

5 “Beware the Big Errors of Big Data,” Nassim M. Taleb, Wired, Feb. 8, 2014, accessed April 24, 2016, at http://www.wired.com/2013/02/big-data-means-big-errors-people/



made on the terms each customer received, when to raise or lower their credit limit, when and how hard to lean on them when they fall behind in payments, etc. Then there is the fact that their customer sample is the result of decisions made about which applicants to approve. All those decisions are reflected in the data, and any projections drawn from the data need to reflect this.

It’s no wonder companies have such trouble pulling value from Big Data. Without the right tools or a sound understanding of analytic principles, they often only achieve accidental discoveries and insights—stupendous strokes of luck that no one could possibly see coming. A case in point: Pfizer’s drug UK92480 was a washout during trials to treat angina, or chest pain, back in the ‘90s. Just as Pfizer was ready to give up on it, trial participants began reporting the most curious side effects. This accidental finding led UK92480 to become one of the biggest commercial drug success stories in history. Today we know it as Viagra.6

The possibility of these fortuitous accidents has encouraged businesses to save every scrap of data they have. But we can’t and shouldn’t count on accidental blockbuster discoveries—they are exceedingly rare, if well publicized. Instead we need to be purposeful about collecting and using data, and find a way to focus more of our resources on the productive work we need to do.

Information needs decision science to help those close to the action to make smart, informed decisions. In the era of Big Data, this is the only prescription for success.

Decision science, deep learning and streaming analytics: where Big Data leads to results.

Big Data only becomes useful when it enables us to make better decisions, so it’s imperative that decision-makers have what they need to understand the data they rely on. This is where decision science plays a crucial role.

6 “Viagra and other drugs discovered by accident,” Emma Jay, BBC News, Jan. 20, 2010, accessed Feb. 3, 2016, at http://news.bbc.co.uk/2/hi/health/8466118.stm

Big Data only becomes useful when it enables us to make better decisions.



One of the tools that helps extract value from Big Data is machine learning—algorithms and processes that enable complex analysis to evolve and improve as new scenarios are added. Machine learning approaches data analytics in much the same way as the cortex of the human brain does, so that analytics such as neural network models can continuously learn and adapt to new data. This happens in an instant when the human brain sees shapes, colors and lines and immediately understands them to represent someone we recognize. The decision: “I know this person.”

My company helps financial institutions, retailers and other businesses identify potential fraud by monitoring instances in which customers follow a set of clearly defined criteria that may be consistent with fraud (high-dollar transactions, frequent transactions per hour, etc.). We then identify relationships between those criteria to flag anomalous behavior and suspicious patterns, ultimately arriving at a fraud score—a metric businesses can use to respond to (or even prevent) costly acts of fraud.

Who should I target?Is preventive action possible?

What if the patterns change or continue?When and where will be the next attack?

What could happen if...?

What meaningful correlations exist in the data?What exactly is the problem?

Who is involved? Where?How often? How many?

VALUE

DIFFICULTY

PRESCRIPTIVE ANALYTICSHow do you make it happen?

Optimization, Planning

PREDICTIVE ANALYTICSWhat will happen?

Forecasts, Machine Learning, Stimulation

DIAGNOSTIC ANALYTICSWhy did it happen?

Queries, Data Mining, Statistical Analysis

DESCRIPTIVE ANALYTICSWhat happened?

Reports, Alerts, Mapping

As complexity increases,

analytics grows more valuable

Not all analytics for decision-making are created equal. Different types of analytics can be useful for specific aspects of decision-making. And the more complex the decision—with more variables involved—the more analytics types you’ll need to engage in your process.

Descriptive – Use this to produce reports, alerts and maps to describe what has happened in the past.

Diagnostic – Queries, data mining and statistical analyses all combine to help identify and explain why something happened.

Predictive – These analytics use current data and pattern recognition to project future outcomes — for instance, the likelihood that an individual will respond to an offer, or repay a loan.

Prescriptive – Optimization takes all the known data and projections and uses it to conclude what actions to take to achieve a specific outcome.



To produce a fraud score for a specific transaction, the analytics look at the purported cardholder’s transaction history and frequency, the types of goods or services purchased, payment history, possible correlation with other fraudulent transactions and more. The process employs many types of analytics (including machine learning and neural networks) to determine the likelihood that a transaction is fraudulent, which the merchant or card issuer can use to decide: Should I accept this payment, or not?

CASE INFORMATION

AUTH REQUEST

EXPERT RULES EXECUTE

Rules Definition

CONFIGURATION WORKSTATION

Neural Network Scoring Engine

7.

Case Management

Database

AuthorizationSystem

1.

Expert Rules Base

3.

Cardholder Profiles

PAYMENT AND NON-MONETARY TRANSACTIONS

8.

ANALYSTWORKSTATION

CASE CREATION RULES EXECUTE6.

Postings

Payment System

Non-Monetary

System

Case Creation Module

Expert Authorization

Response Module

AUTH REQUEST AND SCORE

5.

2.

TRANSACTION AND SCORE

AUTH RECOMMENDATION4.



To truly unlock the full potential of

Big Data, simplify and democratize

the use of decision science.

What will it take to further unlock the true value of Big Data? Certainly, advances in decision science are crucial. But we also must address the severe shortage of data scientists. A McKinsey study predicts that by 2018 only 200,000 data scientists will be available to fill more than 490,000 jobs. Even if we utterly retrofitted our entire educational system tomorrow, we still couldn’t produce enough data scientists to keep up with demand.

This means we can’t hire our way out of the problem. A far more scalable approach would be to codify some of what data scientists do and propagate it throughout an enterprise. An answer to this has arrived in the form of sophisticated software that embodies the fundamental principles and processes of decision science. Combined with the power of the network effect—many diverse brains and viewpoints collaborating on a problem—the right software and decision science disciplines will allow us to reduce (though not eliminate) the shortage of data scientists. It will do this by simplifying the very process of making data-driven decisions, and then democratizing the use of decision science. To simplify decision-making, we need to establish automated and repeatable processes that help individuals, no matter where they are, to participate in decisions.

Key to this is scaling decision processes across an entire organization or population. Traditional analytics and decision management tools, however, are poor choices for this mission. They’re not easy to use, which makes it difficult to get business users to adopt them. Instead, users need tools that allow any stakeholder to participate in the decision-making process.

In a software world impacted heavily by the “Apple effect”—where even complex solutions such as tax preparation software are designed to be intuitive and “idiot proof”—analytics and decision management tools must be so straightforward that they require little to no training. They must be customizable through dashboards so certain managers see only the information and assets that are helpful to them. They should even be able to visualize relationships between different variables that could affect the outcome of the decision.

This isn’t to suggest that marketing or risk managers will replace data scientists. That’s ridiculous. With the right technologies and software in place, stakeholders across an enterprise can participate where it makes sense— however, to collaborate with others who have a stake in the outcome of the decision.



When we simplify and democratize decision-making and even decision science, we breed accountability, buy-in and innovation. In his TED Talk, Steven Johnson notes that innovation occurs when people of diverse viewpoints and backgrounds come together “to have new, interesting, unpredictable collisions.” He likens it to the early coffee houses where great thinkers gathered and conjured up bold new ideas such as stock markets.7

The challenge for businesses is that collaborating on decisions isn’t always easy. Today’s sprawling enterprise is not a coffee house. Colleagues are separated by distance, sometimes time zones. They can’t possibly be expected to keep track of every factor that influences key decisions, or to track those decisions once they produce results so they can use that information to make better decisions next time.

Just imagine if they could.

To improve decisions, make a

best practice of deciding.

Making decisions across an enterprise, a government or a planet involves more than using well-designed software to analyze data. It requires the creation of a decision environment that can be replicated in multiple situations.

7 “Steven Johnson: Where good ideas come from,” TED Global 2010, July 2010, accessed Feb. 5, 2016, at http://www.ted.com/talks/steven_johnson_where_good_ideas_come_from?language=en

It might seem easy to conflate data science with decision science. They aren’t the same, however.

Data scientists apply different mathematical and data mining techniques to extract insights or build predictive models that help solve problems. These insights or models can be very helpful in shaping a decision or driving decisions, and as such they form a fundamental building block of decision science.

Decision scientists are data scientists that go further to maximize application of these data science insights/models to drive maximum business benefit or impose constraints on application

of models. This type of data scientist focuses on business outcomes and constraints and how to appropriately fold the data insights/predictive scores of models into decisions to meet the business objectives.

A classic decision science approach adheres to processes that should be recognizable to anyone familiar with the scientific method, including systematic observation, assessment, experimentation and the formulation or modification of hypotheses. Often this involves continual refinement of models or more data science to help drive better outcomes or apply constraints.

Without these decision scientists, organizations will find it more difficult to apply data science to meeting business objectives. Decision scientists excel in operationalization of the data scientists insight/models and meeting business objectives.

You could think of it this way: Using the principles of the scientific method, decision scientists effectively supervise, validate and continually refine the decisions around data scientists’ models, to maximize business outcomes given their expertise on business processes and their constraints.

When data science becomes decision science



Most organizations fail at this because they simply don’t have a decision methodology in place. Decisions are just made, and things happen as a result. Do stakeholders ever reflect on the effectiveness of that decision? Or are the factors that led to a decision, and its result, buried in complexity?

Earlier I talked about establishing repeatable and simple processes for deciding. Achieving this requires us to establish a best practice for deciding—a methodology that works in any situation, and that allows stakeholders to reflect on and learn from the decisions they make.

This methodology involves six key stages:

1. Codify the decision process and domain expertise so both can be easily examined, repeated and shared.

2. Record the decision and the factors and data that led to it.

3. Model the analytics used to make decisions, with models that can be managed and repurposed.

4. Optimize the models as business conditions and data change to ensure they deliver the results you want.

5. Adapt models so they can be applied to multiple decision scenarios.

6. improve decisions by measuring results, evaluating successes (including the use of champion/challenger testing to compare accepted processes against alternatives) and optimizing further.

You may notice these six steps address the shortcomings that lead to failed decision environments. That’s no accident: With this decision environment in place, organizations can improve the outcomes of their decisions, and more people can engage at crucial points in the deciding process.

The software is willing.

It’s not necessary to build an environment like this solely from scratch. (In fact, attempting to do so would be onerous.) Groundbreaking decision modeling and management solutions, which are built upon decades of analytics experience, are ideal for supporting a structured, repeatable process for deciding. These solutions are emerging now in part because they can be delivered from the cloud. This puts enormous resources at virtually any company’s disposal, no matter their size or the extent of their on-premises IT investment.

Using these solutions, decision-makers across many functions and lines of business can:

• Determine what they need to make the decision and at what point they’ll consider it complete

• Understand the decision in the context of related processes, systems and events

• Visualize information that would otherwise be difficult or impossible to understand through text and numbers alone

• Apply this approach to other decision scenarios



These aren’t just “maybe someday” software features. They’re available today. FICO® Decision Management Suite 2.0 delivers these capabilities through a single, unified user interface that makes it easier for stakeholders to orchestrate the way business decisions are put into operation. An innovative new tool, known as FICO® Strategy Director, helps users structure the decision flow across the six steps we defined earlier. In addition, organizations can record and store decisions as assets, so they can be reused, modified and improved over time. The suite also gives them the infrastructure to manage, audit, report and update decision logic with predictive models.

This is where decision-enabling software is headed. Already today, a subject matter expert—a risk manager, for instance—can model a business decision and execute it without a single call or email to IT. This was unthinkable years ago.

An ability to innovate depends in large part on a willingness to learn from experience. Over the past 60 years, FICO has learned a tremendous amount from our customers, who have been at the forefront of analytics-based decision-making.

So what are the keys to delivering a cost-effective and easy way to create and deploy analytically powered decision management applications? Five fundamental principles stand out. Every one stems from real-world practice.

1. Capturing subject matter expertise. Nearly every organization

benefits from gathering and writing down the expertise and perspectives of subject matter experts, codifying these perspectives, and then incorporating them into a decision process. But most organizations either aren’t equipped to do so, or they aren’t even aware it’s possible.

2. Accelerating intelligent solution creation. Only 16 percent of the average

IT budget goes to fund innovation, with the rest devoted to maintaining legacy platforms built to solve yesterday’s problems. This can delay the creation of innovative decision management applications for months and sometimes years. With Rapid Application Development (RAD) technology, the time needed to create intelligent solutions can be shortened to a few weeks.

3. Speeding insight to execution. It seems a given that the sooner

organizations can deliver new applications, the better. But experience has taught us that speeding the time it takes to update, modify, improve and learn analytic models, and then put those new models and insights into production, can be even more important. Organizations, therefore, are discovering that change management isn’t just an important attribute of a successful decision management solution—it’s a hallmark.

4. Building institutional memory. When decision-makers are promoted or

leave, they tend to take with them knowledge that only they possess. And once they’re gone, their replacements don’t know why certain decisions were made or what specific logic drove certain actions. They lack institutional memory—an asset whose value is only realized after months or even years have passed. The ability to capture that knowledge and then create a real knowledge management infrastructure has become mission critical.

5. increasing analytic accessibility. Experience has taught us that providing

an open framework to easily add powerful and advanced analytics is both unique and tremendously valuable. In fact, it may be a game changer for the analytics industry at large because it holds the key to democratizing analytics and decision science. How? By quickly and easily leveraging powerful and flexible analytic IP—for anyone, in any capacity—to quickly plug in analytic modules into a business flow or decision orchestration.

Learning from experience to build a successful decision management platform



A lesson from Toyota.

In the world of collections and recovery, decisions are made every day about when a relationship is salvageable—Is there a reasonable chance the customer can pay his or her debt?—or when it’s too far gone to save. At Toyota, collections agents were hobbled by an outdated decisions environment that had a lack of reliable, fresh data and an inability to connect related decisions.

Realizing that its model was forcing negative scenarios on too many customers who otherwise might be able to save their accounts, Toyota established the Collections Treatment Optimization (CTO) program. The Toyota CTO program integrates decision management, reporting and advanced analytics to transform the company’s approach to collections, making it more data-driven and scientific, vastly more personalized, and easily repeatable so the next point of decision is simple. The result: During its first year, the CTO program helped more than 1,600 customers avoid repossession and keep their cars, while preventing 10,000 customers from reaching a stage of delinquency that would affect their credit.

These technologies and best practices are changing how we decide. But they’re also doing so much more.

The very nature of the decisions

we make is changing, too.

Simplify and democratize decision science with widely applicable best practices and easy-to-use tools that enable collaboration throughout an organization—this is the formula not only for changing how decisions are made but, in some cases, for changing the very decisions we’re making.

It’s not hard to see what this means. In a world in which the components of decision models are as accessible as digital music is today, the scope of decisions is expanding rapidly and we’re seeing new ways to approaching old problems.

• Medical researchers in Ohio can collaborate with data scientists in Denmark to develop tools that help physicians in South America make different types of decisions that will help them identify, contain and eradicate a potential pandemic—perhaps the next Zika virus.

• Physicians can diagnose disease and prescribe treatment in ways they never could before. Steve Jobs paid $100,000 to have his genome sequenced in hopes of finding effective treatment, but Jobs, as usual, was ahead of everyone else and the science wasn’t ready for him. Things have changed. Francis Collins, director of the National Institutes of Health, predicts the cost of sequencing every person’s genome will cost just $40 per patient within a decade, making it possible for genome analysis to precede virtually any diagnosis or prescription.8 Add further

8 “Rick Smolan’s Buzzworthy Book Breathes Life into Big Data,” Jay Moye, Coca-Cola Journey, April 23, 2013, accessed on March 16, 2016, at http://www.coca-colacompany.com/stories/behind-the-bytes-rick-smolans-buzzworthy-book-breathes-life-into-big-data/



Big Data to this picture and create a codified decision process, and a pediatric oncologist in Houston can identify the specific cancer markers in a five-year-old leukemia patient’s genome and correlate that with global data on treatments and outcomes of other five-year-olds with similar markers. As a result, that doctor can decide which specific treatment should be most effective on that one patient—a perfect example of “extreme personalized medicine.”

• Passionate entrepreneurs and innovators can find the datasets they need to pursue their game-changing ideas and business plans. New Jersey physician Dr. Jeffrey Brenner used data analytics to cut rising healthcare costs. He examined a database of 600,000 hospital visits and discovered that just 1 percent of patients were responsible for 30 percent of hospital bills. After mapping that data to patient addresses, he founded a coalition of healthcare providers who dispatch caseworkers to tend to that 1 percent, thus keeping those patients out of the ER—and keeping their medical bills down.9 What’s stopping healthcare leaders from codifying this approach to adapt it to other decisions in healthcare and beyond?

New breakthroughs, new decisions, at your doorstep.

Organizations of all kinds are using entirely new decision methodologies and a new generation of tools that are easy to deploy—no longer requiring IT groups to hand-build their own decision science environment over several months.

The results are astounding, and they are changing the process of deciding across the broadest spectrum of industries and disciplines imaginable. At my own company, researchers are collaborating with customers to achieve multiple breakthroughs that enable new kinds of decisions:

PtSD treatment. Post-Traumatic Stress Disorder (PTSD) affects at least 20 percent of Iraq and Afghanistan veterans, with 22 veterans committing suicide every day.10

Doctors can decide on the most effective treatment for PTSD patients based on who they are, not what they have.

9 “’The Human Face of Big Data’ shows how tech changes lives,” Elizabeth Heichler, PC World, Dec. 2, 2012, accessed March 17, 2016, at http://www.pcworld.com/article/2017827/the-human-face-of-big-data-shows-how-tech-changes-lives.html

10 “Veterans statistics: PTSD, Depression, TBI, Suicide,” Veterans and PTSD. Sept. 20, 2015, accessed March 12, 2016, at http://www.veteransandptsd.com/PtSD-statistics.html



A veteran’s healthcare group is evaluating newly invented decision science technology that analyzes the text within global databases of treatments for PTSD and their outcomes. Using this technology, doctors can correlate demographic information, regional data and other variables to identify which treatment promises the greatest success with PTSD patients who match those characteristics. They then can decide on the most effective treatment for patients based on who they are, not what they have.

Grant spending. The federal government distributes $600 billion a year in grant awards to educational institutions, government agencies and other non-profits. To comply with grant spending conditions and remain eligible for future awards, institutions must monitor how that money is spent. Yet manually assessing every grant-funded purchase is no longer an option as transaction volumes grow and institutions face new rules aimed at strengthening oversight of federal funds to reduce fraud, waste and abuse. Amid this environment, Stanford University collaborated with FICO to develop a new solution designed to monitor all procurement spending and discretionary expenses—not just a sample set—and indicate when transactions fail to comply with procurement policies and regulations. The result is an efficient, data-driven approach that uses text analytics and other breakthroughs to identify any potentially frivolous use of federal grant dollars.

Medical payments. A large healthcare system aims to save hundreds of millions of dollars over the next five years by shifting from line item payments to bundled payments that represent the cost of a total package of care for a specific diagnosis or medical condition. Achieving this requires advanced Big Data analytics on a massive scale, along with the integration of separate databases relating to a patient’s entire medical experience, including patient history, test results, lab work, patient demographics, regional medical costs and more. Once completed, the organization can decide how much to pay for a specific set of services, which can differ from one patient to the next based on their relative risk of complications or likelihood of responding to treatment. This is a far more efficient approach than today’s pay-per-service medical payments model—and an entirely new way to decide on payments.

immigration. A government in Northern Europe is evaluating new text analytics technology to compare massive volumes of unstructured data from the interviews conducted when an immigrant applies for asylum. Interviews with the asylum seeker and his or her associates are recorded in multiple locations and formats (video, text, etc.). Traditionally, immigration officials review them manually in an attempt to decide if the history given by the applicant matches the information provided by associates and relatives. This new text analytics technology automates and accelerates that laborious process. By spotting potential discrepancies among the various accounts, officials can more quickly and confidently decide whether or not to grant asylum.

FICO is a registered trademark of Fair Isaac Corporation in the United States and in other countries. Other product and company names herein may be trademarks of their respective owners. © 2016 Fair Isaac Corporation. All rights reserved. 4300WP_EN 09/16 PDF

North AMericA +1 888 342 6336 [email protected]

For More iNForMAtioN www.fico.com www.fico.com/blogs

LAtiN AMericA & cAribbeAN +55 11 5189 8267 [email protected]

europe, MiddLe eAst & AFricA +44 (0) 207 940 8718 [email protected]

AsiA pAciFic +65 6422 7700 [email protected]

White pAperThe Future of Deciding

The bigger picture: Improving 245 trillion

decisions every day.

This is the promise of analytics-powered decisions. They present a world in which data, decision processes built around best practices, and innovative new software all help improve many of the 245 trillion decisions we human beings make every day. They offer a world in which 7 billion people enjoy greater productivity, more attainable achievement and a better quality of life.

The future of deciding will be determined by how we use these breakthroughs. What you decide to do with that future may be one of the most important decisions you’ll ever make.

D tu t ells is e e uti e i e esi ent n ie o u t n te nolo o fi e t FICO. Prior experience includes senior leadership roles with Sun Microsystems, Avaya and other top technology businesses.

the future of deciding

Data & Analytics