Analytics: Turning data into dollars

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December 2011 Forward Focus Insurance issues and insights from Howard Mills Analytics: Turning data into dollars

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This issue of Forward Focus features the following topics: Analytics – Facts vs. faith Analytics – Getting started Analytics – Advances Advanced analytics – Hindsight, insight and foresight Advanced analytics – The potential for profit

Transcript of Analytics: Turning data into dollars

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December 2011

Forward FocusInsurance issues and insights from Howard Mills

Analytics: Turning data into dollars

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As with insurance, in baseball small changes can mean big differences. Getting a hit 3.5 times in each 10 at-bats over the course of a season makes a hitter in Major League Baseball almost unbelievably good. Getting a hit four times in each 10 at-bats makes a hitter immortal, a feat last accomplished by the "Splendid Splinter," Ted Williams, in 1941.

That may be why baseball and insurance both rely on aggregating statistics, because when a season is 162 games long, performance in any one game means less than performance throughout the universe of games. But aggregating statistics is not enough, self-described sabermetrician1 Bill James told baseball more than 30 years ago. Properly analyzing the data, including how individual performances affect team performance, is even more important in deciphering why teams win or lose.

In the real world, as both baseball fans and insurance executives know, success depends not on having a superstar or two, but on maximizing the effort of the entire team. Ted Williams is still worshipped in numerous bars along the Fenway, but even with Williams in the lineup, the Red Sox did not win a World Series.

For baseball, like insurance, is a team game, where each individual segment – power hitting, pitching, fielding; pricing, underwriting, claims management, customer service, distribution, client acquisition – contributes to the whole, and improving each segment’s contribution and its integration into the whole can transform an average team into champions.

To its credit, that among the most traditional of teams in among the most traditional of sports, the Boston Red Sox embraced analytics when it hired James. Coincidentally or not, the Sox then won its first World Series in 86 years.

Why? For a hint, let’s look at just one segment of the baseball equation – hitting. Even among major leaguers — the best of the best in their trade — there is a huge difference between an average player (one who hits, say, a respectable but not spectacular .250, versus a potential all-star .300 hitter, and beyond that to a potential Hall of Famer hitting in the .340-.350 range.

Yet quantifying that reveals how small that difference actually is. In the course of an average season, a major leaguer may get 650 at-bats. Getting 163 hits means he is a .250 hitter, sitting near the bottom of the order. Getting just 33 more hits over the course of that 162 game season makes him a .300 hitter, starring in commercials and with jerseys selling out in sporting goods stores nationwide. In practical terms, getting a speedy player to bunt for 20 more hits a year, or a pull hitter to learn to hit against the defensive shift to collect 20 more hits in a season gets the player and his team halfway to stardom.

Analytics is even more important for the insurance industry. The use of analytics in the industry is as old as the first actuary, but with the mountains of data now available and the seemingly ever-increasing amount of computing power that can be used to sort and analyze that data, making meaningful use of that information, finding the patterns in its swirling sands, could help move an insurer from good to great, from contender to champion.

While most insurance companies may be playing in the major leagues, only a few points (in loss ratio, expense ratio, underwriting ratio, mortality, retention rate, etc.) makes the difference between average and all star. Being aware of those numbers and what they mean in practical terms can make the difference between a 105 combined ratio and a 95, with substantial bottom line (net income) results.

Lorin Hitt of the University of Pennsylvania and the Massachusetts Institute of Technology's Erik Brynjolfsson, and Heekyung Kim in a recent academic study sought to quantify the effect of analytics on corporate performance. They noted: “We find that firms that adopt DDD (data driven decision-making) have output and productivity that is 5-6% higher than what would be expected given their other investments and information technology usage.”2

Finding the small changes that add up to that big difference is the goal and the result of the proper use of analytics.

Analytics – An introduction

1 Bill James defined sabermetrics as “the search for objective knowledge about baseball.” It is based on the acronym SABR, the Society for American Baseball Research.

2 Brynjolfsson, Erik, Hitt, Lorin M. and Kim, Heekyung Hellen, Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? (April 22, 2011). Available at SSRN: http://ssrn.com/abstract=1819486

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Analytics – Facts versus faith

Analytics involves sifting through mountains of data to uncover grains or patterns of truth. "Truth" is an often elusive quarry, and the best use of analytics is sometimes the most difficult. Sifting through apparent contradictions and inconsistencies in search of unity and congruency is the goal of business analytics, but the process is not easy. That, however, is precisely why analytics is so valuable to the well-run company. Using analytics to help discover the hidden truths and the process of moving from operating on rear-view mirror hindsight to front-window foresight can offer distinct competitive advantages to first movers.

Analytics is not new. An early example of its use by insurers came about 250 years ago in the person of Richard Price, an 18th century mathematician and nonconformist minister who was in effect the world’s first actuary. He consulted for the Equitable in London, estimating mortality to better price annuities — a 250-year-old example of “analytics at work.”

Clearly, the first insurer to figure out a way to more accurately price annuities had an advantage over competitors. Similarly, in the age of big data, far-sighted organizations can have unprecedented potential opportunities to gain competitive advantage through the use of analytics.

Actually, analytics is more important than ever given the increasing amount of available data. The most effective decision-making should be supported by the leading available information, but when humans make the decisions, access to information alone may not be enough. Bias intrudes.

Ever wonder why in the face of seemingly incontrovertible evidence to the contrary, some people still hold fast to the belief that the moon landing was a giant hoax? Or how two people can listen to the same news item and end up with completely opposite interpretations? One big reason may be what is called confirmation bias.

Confirmation bias is why liberals tend get their news from one source while conservatives tend to get their news from another source. We accept information supportive to our existing beliefs as correct, while other information is discarded or downgraded. The end result is that even with, or perhaps because of, the vast amount of data available, we tend to select data supporting our prior views, reinforcing our biases.

Not surprisingly, this bias can affect decision-making. Though we may think we won’t discard evidence that doesn’t favor the status quo or our beliefs, research shows that we seek and interpret information in ways that confirm not just favored hypotheses but established beliefs, even if we have no vested interest to maintain those beliefs.

Other factors can bias decision-making. One is the primacy effect. That says the first information you get in a process tends to be weighted more heavily than information you get later. Another issue is belief persistence. This is the phenomenon whereby once a belief is formed, the believer holds to that belief even in the presence of fairly compelling evidence it is wrong or over-weighted, as with moon landing skeptics or people who believe sharks are pervasive at every beach.

So, for example, when senior management of a multiline insurance company is looking at converting part of its property-casualty customer base into life clients, how might these intrude? If the senior executives have all seen underwriting decisions for life policies made individually and with medical exams, they may not even consider that similar results in terms of the health demographics of their life-client base could be achieved at less cost through predictive analytics that may largely dispense with the expense and marketing concerns associated with medical underwriting.

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Let’s go back to baseball and Bill James for a real life example.

In his 2003 book Moneyball, Michael Lewis related how the Oakland A’s general manager Billy Beane was able to take his cash-strapped team to the top of the American League through the use of statistical analysis.3

Beane’s challenge was that wealthier teams, such as the New York Yankees, could outbid the A’s when scouting for new talent. Beane addressed this challenge with a crucial insight: baseball scouts often use flawed reasoning and fallible “gut feelings” or “professional judgment” when selecting baseball players. Beane realized that by using a more objective approach, he could identify excellent players ignored by the richer teams and lure them to the A’s at bargain salaries.

For example, James had constructed a formula that could predict the number of runs a hitter is expected to create as a function of his on-base percentage. Taking his cue from James, Beane hired quantitatively minded Paul DePodesta to statistically analyze players’ performances. One of Beane’s and DePodesta’s findings was that college baseball players went on to perform better than high school recruits. Based on this finding, Beane decided to let the richer teams spend their time recruiting players out of high school while he and DePodesta used their statistical analyses to select the excellent college players ignored by the scouts.

In short, Beane realized that the market for baseball players was inefficient because it was dominated by scouts making decisions based on historical intuition and lore rather than objective, data-driven analyses.

As University of Chicago professors Cass Sunstein and Richard Thaler point out, the problem is not that professionals are foolish or uneducated, it is that we are human. Out of necessity, we depend on irrational expectations, fallible intuitions, mental heuristics, and tribal wisdom when processing information to make decisions. As Sunstein and Thaler write, “even when the stakes are high, rational behavior does not always emerge. It takes time and effort to switch from simple intuitions to careful assessments of evidence.”4

Analytics, with its reliance on objective data, can help make that change more easily achievable.

3 Michael Lewis, Moneyball: The Art of Winning an Unfair Game (W. W. Norton & Company, 2003)4 Richard H. Thaler and Cass R. Sunstein, “Who’s on First,” The New Republic, September 1, 2003

<http://www.law.uchicago.edu/news/susntein/2003/moneyball.html>.

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As used in this document, “Deloitte” means Deloitte & Touche LLP, Deloitte Consulting LLP, Deloitte Tax LLP, which are separate subsidiaries of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.

Analytics – Advances

Though the use of analytics is by no means new, the advanced analytics offered by Deloitte Consulting LLP ("Deloitte") is a function of our time.

Advanced analytics involves the use of modern data mining, pattern matching, data visualization and predictive modeling tools to produce analyses and algorithms that help businesses make better decisions. With this foresight, analytics can help determine which events may have the most impact on the enterprise as a whole.

Predictive analytics is now coming into its own, both because of the findings of cognitive science and behavioral economics, and also because of a recent and rapid proliferation of huge databases, cheap computing power, and advances in data acquisition, aggregation, visualization, applied statistics and machine learning techniques.

Notable factors include:

•The data deluge: We now gather, store and transmit unimaginable quantities of data each day. The problem, as anyone facing an inbox full of email well knows, is that evaluating and responding to more information requires that scarcest of resources – time and attention. Analytics is increasingly regarded as a necessity to focus decision-makers’ attention on the predictive insights hidden in the depths of oceans of data.

•Algorithms and software: Increasingly powerful tools and methods for analyzing data and making predictions are being discovered and promulgated at an unprecedented rate.

•Increased awareness: Farsighted leaders in a variety of domains are increasingly aware of the competitive and operational advantages that analytics can bring.

Advances in information technology have dramatically magnified both the practical availability and business necessity of data and analytics tools and methods. As a result, novel applications of analytics are taking root at an impressive clip.

Information technology (IT) is also crucial to analytics for a second reason: The best algorithm in the world provides no value sitting on a shelf. It must be implemented and integrated into the technology infrastructure. A common goal of analytics projects is to create predictive models or other types of algorithms intended to improve critical business processes and/or help human experts make more effective decisions. In practice this means that once the algorithm has been developed and validated by a team of analysts, it should be implemented in the organization’s information systems and used to automatically generate business rules, recommendations or messages tailored to individual cases. Without an effective business and technical implementation plan in place, the Return on Investment (ROI) of such an analytics project is likely to be negative.

Because of its substantial IT component, people sometimes mistake analytics for a variety of IT, or a software implementation project. But this confuses the delivery vehicle with what is being delivered. Predictive models, collaborative filtering algorithms, business process optimizations, pricing solutions and analytically driven collections of business rules are generally not off-the-shelf software products. Rather, they are developed by data scientists with expertise in fields such as statistics, operations research, computer science and machine learning, linguistics, actuarial science, marketing science and psychology. The reason the general term “analytics” is such helpful shorthand is because of the huge variety of methods and applications that it encompasses. In short, while IT is indispensible to analytics, analytics projects should not be conceived as IT projects.

It cannot be said often enough that analytics is not an end in itself, but a management tool. Consider, for example, how a hiring manager makes a decision. The manager reviews the resumes received and interviews the applicants in whom the manager is interested in before making a final decision. During the process, the manager weighs the

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information from and about the candidates against the requirements of the position, using historical knowledge of fit, culture and traits shown by previously successful and unsuccessful candidates.

The manager generalizes from examples. In short, this is inductive reasoning.

Predictive modeling is a refinement of the very human process of inductive reasoning. It is not necessary for business leaders to understand the finer points of multivariate regression, classification and regression trees, artificial neural networks or support vector machines. It is important for them to understand that these are all tools that can “learn” from large databases of cases to arrive at general conclusions – in an analogous way that the hiring manager in our story learned from her and her colleagues’ experience.

For an example relevant to our hiring manager, predictive models are being built to help hiring managers make more effective hiring decisions. In detail, this involves constructing a database about a company’s current and previous employees in which high-performing employees are flagged. A predictive model is then built by optimally combining a set of leading indicators – predictive variables – of high performance. The model is built and validated on past data, but used to rate the applications of incoming job candidates. In this way, the model serves as a “scoring engine” used to triage resumes on the fly. Instead of laboriously manually reviewing hundreds or thousands of resumes coming from online job services, personnel can then focus on evaluating those candidates that the model identifies as potential top performers. The model doesn’t usurp the decision-making process, but can help anchor the decision in a predicatively optimal combination of inputs rather than in purely subjective judgments.

Any business process that calls on human decision-makers to repeatedly weigh multiple factors to arrive at decisions could more likely than not be improved through predictive analytics. Furthermore, if these decisions are central to a company’s core strategy (such as underwriting for an insurance company) much more is at stake than improvements in business process efficiencies. Analytics and predictive models can help companies win by exploiting market inefficiencies.

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Advanced analytics – Hindsight, insight and foresight

Think of analytics as a process, a way of taking data and using it to create a data-driven fact-based culture within an organization, like an insurance company, and that fact-based process can allow the company to see things in new ways using the foundation of the business, which of course is data. Analytics forms a continuum, going from the base of the continuum, the data itself, through the management of the data, the creation of intelligence from the data, business intelligence, and ultimately, leading to the top of the food chain: using advanced analytics, which involves the use of modern data mining, pattern matching, data visualization and predictive modeling tools to produce analyses and algorithms that help businesses make more effective decisions and can help it predict the future.

That continuum we label as hindsight, insight, and foresight.

Why do we need advanced analytics? One answer is as simple as survival. In today’s insurance marketplace, increased regulatory scrutiny is one of the factors driving the demand for more effective risk management. With the exponentially increasing amounts of data streaming into insurers, advanced tools may be necessary to actively monitor and manage risk enterprise-wide.

But analytics is a tool for more than mere survival. Used properly, it helps organizations use data more effectively to inform decision making, pursue business strategy and enhance performance. It not only allows insurers to look at what is happening but to prepare for what will happen (scenario planning) and help bring about what an insurer wants to happen, through tools like predictive modeling.

With business analytics capabilities in place, organizations can expect to be able to:•Gain deeper, more relevant business insights to inform

decision making

•Bring advanced analytics techniques, such as predictive analysis and regression modeling, within reach of a wider cross-section of the organization

• Apply analytics to effectively address industry challenges

•Strengthen data governance at each level of the organization

•Effectively anticipate and respond to significant business challenges as they emerge

•Reduce costs through more accurate, data-driven decision-making

•Use automation to help reduce latency

•Use analytic capabilities and outcomes to change management efforts

•Create a culture that thrives on fact-based decisions

•Achieve more consistent, objective and prospective business decisions

•Effectively respond to and manage risks

Analytics is not a substitute for good management, but one of its most effective tools. Embedding analytics capabilities and outputs into processes throughout an insurer helps drive a culture of discipline and accountability.

At a time when insurance companies are faced with tight margins, low investment returns, slow growth rates and pricing pressures, analytics offers a compelling competitive advantage. For example, claims are the single largest spend for a typical property-casualty insurer. Typically, up to 80 percent of each earned premium dollar is “claimed by claims” as pay-out and related expenses. Insurers who can equitably settle claims while reducing claims costs by just one percentage point will likely realize a huge benefit.

But claims management may be challenging even for the most adept company. There are multiple processes and a myriad of platforms. Complex duplicative functions are performed, often with outdated technology. Organizational misalignment is not uncommon among critical elements of the value chain.

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Misalignment may show up in troublesome inefficiencies and erratic service. Frequently, too much time is spent on claims-related tasks. There is vulnerability to fraud, which too often goes undetected, and questionable losses spanning service channels. Claims financial leakage is commonplace and a giant industry problem.

Analytics, using specialized techniques and computing tools to analyze data, can help create a better understanding of the drivers of loss and expense. Analysis of claim characteristics from a company’s own claim files, as well as external data sources, such as government agencies and private vendors, help paint a fuller picture. For instance, a claimant’s financial position, recreational interests, and family responsibilities can influence the duration and cost of a claim.

Such characteristics are then analyzed within the context of an insurer’s current claims framework and dynamics. Which claimants are most likely to fall into the highest-cost segment? Which claims have a higher propensity for fraud? Where are the significant areas of exposure? And, overall, what gaps exist between current processes, and the best and most cost-effective industry practices?

The solution may include predictive models, segmentation profiles, or management scorecards for critical initiatives. Through analytics, information is transformed into insight, and then into savings.

Case studyDevelop a breakthrough predictive modelClientTop tier national multi-line Property and Casualty (P&C) carrier

ObjectiveTo build on strong results in claims operation by constructing an innovative approach based on predictive modeling, to materially decrease loss costs.

Action•Acquire, load, and cleanse over 10 years of data from more than 20 internal and

external sources.

•Analyze 1,400 risk characteristics to assess predictive power in determining relative claim severity.

•Develop a proprietary methodology to distil more than 6,000 injury-diagnosis codes into fewer than 50 categories (groupers).

•Analyze and report on correlation of claim characteristics to outcomes by different views (geography, industry, etc.).

•Use real-time claim segmentation to optimize resource assignment and referral to specialty resources such as nurses and Special Investigations Unit.

•Utilize model outputs to accelerate leading practices and including standardization of certain processes through business rules.

•Use model outputs to improve performance metrics and quantify results.

We helped the client to achieve the following benefits:•Projectedreductioninlosscostsofeightpercentonanannualrecurringbasis.

•Anextraordinarypredictivemodelwhichimprovedaccuracy.

•Realtimeandbatchscoringofclaims.

•“Open”modulardesignallowseasyadaptabilitytomultipleapplications.

•Integrationintooperations,requiredacceptance,andbusinessimpact.

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Analytics and life insurers

Life insurance is a special case, some industry insiders say, less able to benefit from the use of advanced analytics because it relies so heavily on medical underwriting. Regulators who are quite comfortable with approving the use of big data in P&C underwriting are often leery of allowing advanced analytics in an area so fraught with political peril. That helps explain why life insurance companies have traditionally been behind P&C companies in the adoption of advanced analytics, but those arguments are slowly falling as life insurers begin to adopt and realize the benefits of predictive analytics for both top line and bottom line growth.

How persuasive is the argument in favor of using predictive analytics? A Wall Street Journal article in November 2010 analyzing work done by Deloitte summarizes the evidence:

“In one of the biggest tests, the U.S. arm of British insurer Aviva PLC looked at 60,000 recent insurance applicants. It found that a new, 'predictive modeling' system, based partly on consumer-marketing data, was 'persuasive' in its ability to mimic traditional techniques…This kind of analysis, proponents argue, could lower insurance costs and eliminate an off-putting aspect of the insurance sale for some people.”5

Consider the context, which the Journal article also provides: “The industry is grappling with how to get policies into the hands of middle-class families more cost-effectively. Sales of life policies to individuals are down 45% since the mid-1980s.”6

Life insurers have been faced with a problem. While high-net-worth individuals with good financial advisors may be well informed on the many virtues of adequate life insurance, middle- and low-income families who generally need life insurance even more may be much less aware

of its importance. The cost of converting these prospects into customers remains stubbornly high, even with advances such as basic automated underwriting, and initial acquisition costs may be entirely wasted if the prospect balks at the normal medical underwriting.

How does predictive analytics change this? In the case cited by the Journal, Deloitte helped its client to combine publicly available data with that already gathered by the insurance company to enhance the workflow method by which applicant medical testing is performed. The Journal noted the company’s comparison of the results based on predictive modeling and traditional techniques: “'The use of third-party data was persuasive across the board in all cases,' said John Currier, chief actuary for Aviva USA.”7

So the accuracy of an approach using predictive analytics is similar to that of the traditional approach. What about the cost? The Journal article explains: “Deloitte says insurers could save $125 per applicant by eliminating many conventional medical requirements. Under Deloitte's predictive model, the cost to achieve similar results would be $5, Deloitte says. The total underwriting costs for a policy range from $250 to $1,000, insurers say.”8

But even the raw numbers may understate the case.

What if, for example, a multiline company wants to convert part of its non-life base to life customers? Predictive analytics could be used to identify those customers most likely to respond to cross-sell offers, and thus, reduce marketing costs. Once these potential clients are identified, the same analytics could then allow for a faster, more streamlined process from the customer’s viewpoint. It could exclude unnecessary medical tests, thus removing one more possible customer-deterring step in the process.

5 Insurers Test Data Profiles to Identify Risky Clients by Leslie Scism and Mark Maremont, Wall Street Journal, November 19, 20106 Ibid7 Inside Deloitte's Life-Insurance Assessment Technology by Leslie Scism And Mark Maremont, The Wall Street Journal, November 19, 20108 Insurers Test Data Profiles to Identify Risky Clients by Leslie Scism and Mark Maremont, Wall Street Journal, November 19, 2010

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As with most advances, there are accompanying concerns. For regulators, privacy and unfair discrimination are often cited as the primary issues.

Decisions to grant and price life insurance policies are, by their very nature, discriminatory. A 40-year-old race car driver who smokes three packs a day and goes bungee jumping on weekends will likely pay higher premiums than his non-smoking, non-drinking accountant twin. The question is whether this discrimination is unfair, and as the test referenced in the Journal demonstrated, predictive analytics resulted in similar outcomes to conventional underwriting workflow and medical test processes.

The data used are derived from public databases, and while it is true that there is a tremendous amount of data available on individuals, it is also true that individuals have a significant degree of control over what data they provide, and younger consumers in particular have often chosen to share that data in return for incentives ranging from publicity on social media sites to supermarket discounts.

But perhaps the most instructive example comes from recent history. Many groups raised the same concerns when it was first proposed that credit histories be used in auto and home insurance underwriting. What does how you pay your bills have to do with how many claims you file, people asked? But the evidence then, as it is now, was incontrovertible: people with better credit histories have fewer claims.

The truth of the data overcame doubts then, as it likely will now. But also part of the lesson from the use of credit histories in P&C personal lines underwriting is that those P&C companies became even more comfortable with the use of predictive data over the years. That could be one reason P&C companies are generally more advanced in their use of predictive analytics today.

Companies that may not have much history with advanced analytics need not feel disadvantaged, however. Starting where you are is a basic tenet of introducing analytics into the organizational decision-making process.

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Analytics – Getting started

Hundreds of terabytes of information are now produced each day in forms as diverse as unstructured text, transactional records, Internet clicks, digital multimedia, telematics, and Radio Frequency Identification and geospatial Global Positioning Systems signals. Analytically adept organizations are able to use this data to make refined operational decisions more economically, objectively, consistently and in greater quantities than ever before.

Organizations not so adept may be at risk of drowning in this data and falling behind competitively. Today’s business landscape has therefore changed in ways that can put analytics “have-nots” at a substantial disadvantage relative to analytics “haves.”

Delivering business analyticsThe process of delivering analytics business results is one of continuous improvement. Starting anywhere in the analytics cycle, an organization can begin to address its specific needs.

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Business results

Applied analyticsIssuesWhat business problem are you trying to solve?

ActionsHow do we look to the future and build analytic insights directly into business processes? Understanding

What is currently happening or has happened related to our business and why? What should we do about it?

FactsWhat data can be leveraged to understand the business and improve performance?

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Performance improvement

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1 2 3 4

Current business

performance

What a business is able to

achieve with analytics

What a business could achieve with

analytics

Analytical theoretical

performance

Behavioral economics supports the argument that companies tend to operate to a certain equilibrium level. That level may be suboptimal due to various constraints, including political, emotional, and economic ones. The graphic to the right illustrates the ability of companies to maximize performance with the aid of advanced analytics. Jar four represents maximum performance. This is the Holy Grail, what is theoretically achievable in the best of all possible worlds. While this is the goal, in practice this level of achievement is possible only in a frictionless environment, not in the real world. The real world begins with jar one, where most businesses operate using available tools to improve performance. Jar two represents the performance level achieved by many organizations with progressive leadership and a good grasp of analytics. The distance from jar one to jar two is covered through sacrifice, soul-searching and change management. It is a considerable improvement over previous functioning levels for most businesses and represents dramatic progress, but still falls short of what a business could achieve with deeper soul-searching and bold moves beyond its comfort zone. That delta between jars two and three is where leading companies use analytics to differentiate themselves from their peer group. In a fluid business atmosphere, this is an exercise of continuous improvement for any company pushing for the top. Companies that can find creative, innovative ways to challenge confounding factors will be the winners, with the best chance of surviving long term. The narrow distance between the level of the coins in jar two and those in jar four provides asymptotic opportunity – further achievement until a practical stopping point is reached. Real world constraints will likely always prevent attainment of the analytic theoretical performance level.

Introducing analytics, however, is not always easy. To be effective, analytics cannot be regarded as a one-off project, but as a core competency that should be developed over time.

Business analytics is a strategic tool. Decisions will have to be made about what areas merit the necessary investment, and how/who will measure return. Analytics challenges

Advanced Analytics – The potential for profit

the way seasoned professionals go about their jobs. We frequently encounter organizations that are resistant to the changes that analytics initiatives bring.

Necessary for its success is the presence of three core resources: human capital, social capital and IT capital. Executive-level sponsorship is important. Introducing business analytics is an enterprise-wide cross-boundary initiative requiring a culture change that normally is most effectively spearheaded from the C-Suite.

Based on our experience, these are the critical factors achieving the desired results:

•Consistent and forceful executive sponsorship

•Education/communication/market and competitive intelligence

•Connection of effort to corporate strategy and Key Performance Indicators and value initiatives

•Mandatory focus on organizational change management

•Identifying non-technical “influencers” within the organization

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Analytics applied – Key operating principlesStart where you areAssess your current capabilities and get a clear picture of the gap between what your organization can do – and what it needs to do. Focus improvements first on low-hanging fruit.

Crunchy questions Get great at using highly specific questions to drive your analytics pursuits. Some exploratory work – coming up with answers to unasked questions – will always have a place, but it generally makes sense to start with a focus on solving specific problems.

Signal strengthBeing able to detect and respond to signals – especially faint signals – better and faster is key to competitive advantage in analytics.

Accelerating insightsAutomate delivery of the information people need to do their work – and automate responses whenever possible – so that action is taken with more certainty and at the lowest possible cost.

User engagement and visualizationOutputs of analysis must be pushed deep into the heart of the business, delivering the insights people need – in whatever forms they need – to make better decisions.

Fact-driven culture Embed analytics capabilities and outputs into processes throughout the enterprise. Drive a culture discipline and accountability.

Right fit analyticsMatch statistical and analytics techniques to the job at hand. Overpowered solutions waste time and money. Underpowered solutions can miss important insights. Buy what you need and use what you buy.

What capabilities are required in order to implement these analytic-driven improvements?

Capabilities

Data and technology

• Provide the ability to pull and integrate data from multiple, disparate systems.

• Do not assume that data is correct. Confirm that data sets have the integrity and quality required before running analysis.

• Design capable databases/data cubes to help improve the outcomes/findings of the analysis.

Analytics and process

• Provide the ability to analyze and integrate external data, both qualitative as well as quantitative.

• Apply multiple analytic tools on the same issue in order to triangulate in on the real issue.

• Do not talk or analyze in averages — real benefits can only be realized by dealing at the most-granular level. Aggregate measures hide variance.

• Prioritize the implementation of improvement opportunities by value — the realization of tangible benefits early on can be the most-effective way to obtain buy in.

People and organization

• Strong executive sponsors that can drive cooperation and build consensus across stakeholders groups with conflicting interest (e.g., finance and sales).

• Involve resources who are experienced not only in the analytic techniques, but also in the business.

• Recognize that some results can be counter-intuitive to your audience, and that you will be fundamentally challenging years of accumulated conventional wisdom.

Source: Deloitte Consulting LLP

If frontline decision makers reject the model, failure is likely.

But the good news is that, in some ways, introducing analytics can be easier than many executives may think. Analytics is a tool for continuous improvement, and an organization can begin to address its needs anywhere in the cycle.

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Analytics – The results

The justification for an investment in analytics is determined by the return. Examples of the results achieved by our clients in various areas include:•Insurance underwriting and pricing: We and our

colleagues have helped hundreds of underwriters at more than 70 insurance companies build multivariate scoring models to better select and price insurance risks. These models find gaps in traditional risk assessment and underwriting methodologies, and thereby, provide novel ways for insurers to better distinguish between seemingly similar or identical risks. Our goal is not to replace human decision-makers, but rather to help them develop a tool that enables them to make more effective decisions. We have seen that underwriters consistently do a more effective job of selecting risks with predictive models in hand. The implications of these observations have been shown to be very valuable to insurance companies that have adopted analytical methods.

•Talent management: We have helped employers use psychometric data to more effectively predict employee performance. In one study, we found that employees with certain combinations of behavioral traits had twice the chance of being promoted, whereas employees lacking a different combination of traits had virtually no chance of being promoted. Workforce intelligence findings such as this are useful when making hiring and talent management decisions as well as evaluating employee attrition and other workforce metrics.

•Medical malpractice prediction: We have helped physicians – both general practitioners and specialists – build models to more effectively predict whether they are more likely to be sued for malpractice based on practice parameters and patient safety. We have found that, as with the talent-management example

above, behavioral as well as other factors are predictive. Predictive models using psychometric data can be used to selectively reach out to physicians for risk and practice-management issues and ultimately help lower the incidence of medical malpractice suits.

•Consumer business: We have helped companies use analytics to better understand their customers and sales patterns. While it is true that some companies make extensive use of their data to segment, target and cross-sell to their customers, we have found that many others use their data only to generate business metrics and fairly standard management reports. The situation is to a surprising degree similar to what we have found in the emerging field of workforce intelligence: the data exist but are not being used to refine decisions rooted in intuition and mental heuristics. Analytics and predictive models can therefore be brought to bear to exploit the resulting market inefficiencies.

•Claims and medical case management: Medical case management for workers’ compensation and disability cases has traditionally been managed primarily as a medical event. Thus, a case worker helps an injured worker return to his or her job through a prescribed medical treatment process. Rarely has the portfolio of cases been managed analytically with early identification of those cases that are likely to become high-severity or long duration. We have helped companies build models that combine medical (diagnoses and co-morbidities), biographic, demographic and psychographic information to more-effectively predict which cases are more likely to exceed industry standard norms for severity and duration. With improved case management tools like these, workers can be helped to return to work more efficiently and abusers of the system can be more easily identified.

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Insurance is, more than ever, a zero-sum game, with an essentially static risk pool and fixed or declining exposures to insure. To be effective in this environment, innovation and increased efficiency would be helpful. In many cases, that means moving the organization to be more fact-based and data-driven and focusing on more creative opportunities for improvement.

Case study: Underwriting financial benefitsA national P&C insurance carrier worked to create an automated underwriting platform. The company’s goals were to improve the underwriting quality and create low and/or no-touch processing to clarify appetite, speed up policy writing and reduce operational cost.

Planning • Handpicked team of cross-functional experts• Developed a multi-phased project approach and strategy

Strategy • Developed several new and renewal models• Created scoring systems where model results flow

through to a business rules processor• Orchestrated implementation to drive operational,

system and organizational change

Project delivery • Developed 25 models in serial fashion, placing first model into production within eight months

• Accelerated model development and deployment

Operational execution

• Processed and delivered scores through a multi-tiered technology platform

• Automated specific business actions: targeted non-renewals, precision and retention pricing

Benefits measured by client

• Models and resulting automated U/W platform provided the foundation for the U/W performance turnaround

• Improved business insights delivered• New policy sales rose 45% and

retention improved 6.5% within a year• New commercial automobile policy

sales rose 35% and retention improved 2.5% within 32 months

• 25% reduction in new business processing time

• Five-point combined ratio improvement across the entire small commercial lines book

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Analytics – In short

The world has changed. Again.

When Karl Benz created the first automobile driven by an internal combustion engine, who knows what the local blacksmith thought. Business was still good. People would ride horses and ride in horse-drawn carriages forever, it must have seemed. Why wouldn’t they? They always had.

Analytics, moving from hindsight through insight to foresight, gives us the ability to profit from the past and profit in the future. It does require a strong commitment to organizational change. Given that typically the insurance industry has not only profited from conservative behavior in the past, but survived the financial crisis of recent times to a great extent because of it, the advantages or necessity of making such a change may not seem necessary to all.

But nothing stays the same. C-suite occupants who think they still have time to ponder instead of acting to embrace advanced analytics may do well to ask: How quickly did the CD vanquish vinyl? How long before the iPod became king of the hill, relegating the CD to an afterthought? What happens if you miss the inflection point? Who’ll get run over if you fall behind the curve?

One way to avoid this is to begin by asking those crunchy questions. Detailed though they may turn out to be in practice, in principle, the questions are straightforward:

•Whatvexingproblemsareyoutryingtosolve?

•Howwouldyousolvethemifyoucould?

•Whatkeepsyouupatnight?

•Whatwouldyoudowithasolution?

•HowwouldyoumeasuretheROI?

Analytics works when it is embedded in the business process, which requires a business implementation plan. That plan should have various components, from business and technology implementation, through organizational change, to performance management.

In many ways, the technology is the simplest part. Our leadership knowledge and experience at Deloitte allows us to see and plan for the pitfalls accompanying the end-to-end implementation of advanced analytics, and that experience has shown us that the first and often hardest part of implementing advanced analytics is the organizational change it requires.

Getting people and the organizational cultural paradigm to change is difficult at best. But identifying and managing the organizational change agents required to catalyze change is a necessary step. C-suite occupants should embrace the change, but it is just as important to persuade and empower the staff carrying out the change.

Some questions we ask:

•Have you created a strategy around analytics, including perhaps a chief analytics officer?

•Have you developed relationships with important business partners to pollinate ideas throughout the company?

Once the process has begun, its performance should be managed and monitored. No business process is static. Living processes evolve and change, and your use of analytics should adjust accordingly. Knowing what is working and what is not is a start. Knowing why some things are not working and how to fix them is imperative.

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Still, properly integrating advanced analytics into a company’s business process is not just a matter of will. There are externalities that militate against it. One major factor is that there is a tremendous talent shortage in analytics. It is almost a cliché that there are few American students interested in math and science to begin with. But add to that the need to find people who can translate math and science into business questions and answers and the difficulty with getting resources becomes even clearer. That, for many companies, means they need business partners to help make this work.

What, after all this, should an insurer expect to get?

Throughout the industry, many companies are by necessity on a treadmill of analysis and innovation. Falling behind or getting thrown off leaves room for a competitor to take your place. Advanced analytics can help you move to the front of the line.

In insurance, there are numerous questions advanced analytics can help answer. What, for example, is your company doing to understand the needs of its customers at their various life stages? How much do you know about their appetites for risk? How much education do they need on risk management?

How do you find the potential customers who “get it," or educate and change into potential customers those who don’t? What gaps are there in your product line? How do you begin to find new customers and create products that appeal to them? What price point?

How do you know which producers are really doing a great job? How do you move those doing a good job to great?

How do you hedge your risks using reinsurance?

These are just a few of the many questions that could be asked and answered with advanced analytics. But what is without question is that the opportunity cost of not using this most important capability is enormous. For as any professional baseball player will tell you, executing faster and better is how you win.

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Insights and contributions provided by:Jim GuszczaPhD, FCAS, MAAASenior Manager, National Predictive Analytics LeadDeloitte Consulting LLP+1 [email protected]

Contacts

Howard MillsDirector & Chief AdvisorInsurance Industry GroupDeloitte LLP+1 212 436 6752 [email protected]

John LuckerPrincipalGlobal Advanced Analytics & Modeling Market Offering LeaderDeloitte Consulting LLP+ 1 860 725 [email protected]

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This publication contains general information only and is based on the experiences and research of Deloitte practitioners. Deloitte is not, by means of this publication, rendering business, financial, investment, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte, its affiliates, and related entities shall not be responsible for any loss sustained by any person who relies on this publication.

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