Tag Archives: predictive modeling

3 Ways to Measure Models’ Effectiveness

Most insurers are using some form of predictive modeling, but it can be difficult to know if it will remain effective over time. Evaluating a predictive model can be tricky because, while there are many ways data can be measured, there is no accepted standard. With the considerable investment that’s involved in predictive analytics, the C-suite understandably wants to hold certain yardsticks to the models and see if they are performing well, and to make sure every stakeholder is using it correctly. Having a forward-looking evaluation can make all the difference when making key decisions, especially if there is trust in the measuring mechanism.

Below are three new ways that insurers can evaluate the impact of predictive models, based on a model currently in production for a regional workers’ compensation insurer. The graphs below provide real-time insights that can help predictive modeling avoid becoming a black box, meaning that you can only see the output of the predictive model, not the input or how that output came to exist. The first two graphs separate out 10 equal portions of either premium or policy count, with each portion referred to as a “bin.”

1. Monitoring that a model is still current and accurate

You need to be able to regularly check if the model you have in production is still up-to-date and providing accurate scores. This graph illustrates the overall model lift on the book for a regional workers’ comp insurer in 2015 and 2016. The insurer’s model is generating a low score on business that’s running very profitably — the lower-risk bins 1, 2, 3 are approximately 30% better than average. Policies getting a score in the higher-risk bins 8, 9, 10 are all running at twice the average loss ratio. This provides a clear indication of what to target and what to avoid.

Bottom line: This model is still current and accurate.

See also: Top 6 Myths About Predictive Modeling  

2. Tracking the impact of a model on decision-making

To realize the benefits of analytics, your staff needs to leverage the insights to make more informed decisions that create improved results. This is a graph of “decision data” from Valen’s InsureRight Manage application. Orange represents policies that were declined, red is quoted and lost, green is quoted and bound and yellow represents non-renewals. It’s evident that declinations are low on the good business — less than 10% — and high on the other end, approaching 50% for bin 10. The insurer is not renewing policies in bins 9 and 10 and, most importantly, retaining more than 50% of business in bins 1, 2, 3.

Bottom line: Underwriters at this insurer are using the model to make more profitable risk selection and pricing decisions.

3. Measuring if the overall risk quality of a portfolio is improving with a model in production.

If you’ve established that your model is accurate and your people are using it, the next question is what kind of impact it’s making to the quality of your portfolio. Are we lowering the risk of our book of business? This view shows the insurer’s risk-selection trends, with an overview of how risk-selection decisions have been influenced by a model and the resulting change to the portfolio. The blue bars represent premium volume by month, and the orange line represents average risk score (i.e., loss ratio prediction) by month. Though there is some variability from month to month, the overall downward trend indicates improvement over the course of the year. There is a small uptick in December 2016, which provides an indication that further analysis is needed.

Bottom line: The risk quality of this portfolio is improving, though still requires careful monitoring.

See also: Survey: Predictive Modeling Lifts Profits  

Not only is it crucial to measure before an implementation takes place, it’s vital to do so both during and after, as well. Predictive modeling only works well if it is aligned with stated business goals, and knowing how to measure that is key to an insurer’s bottom line. With these three new ways to measure, insurers now will have different yardsticks to see whether it is successful and if they are using the actionable insights.

Confusion Reigns on Predictive Analytics

It seems everyone in workers’ compensation wants analytics. At the same time, a lot of confusion persists about what analytics is and what it can contribute. Expectations are sometimes unclear and often unrealistic. Part of the confusion is that analytics can exist in many forms.

Analytics is a term that encompasses a broad range of data mining and analysis activities. The most common form of analytics is straightforward data analysis and reporting. Other predominant forms are predictive modeling and predictive analytics.

Most people are already doing at least some form of analytics and portraying their results for their unique audiences. Analytics represented by graphic presentations are popular and often informative, but they do not change behavior and outcomes by themselves.

See Also: Analytics and Survival in the Data Age

Predictive modeling uses advanced mathematical tools such as various configurations of regression analysis or even more esoteric mathematical instruments. Predictive modeling looks for statistically valid probabilities about what the future holds within a given framework. In workers’ compensation, predictive modeling is used to forecast which claims will be the most problematic and costly from the outset of the claim. It is also the most sophisticated and usually the most costly predictive methodology.

Predictive analytics lies somewhere between data analysis and predictive modeling. It can be distinguished from predictive modeling in that it uses historic data to learn from experience what to expect in the future. It is based on the assumption that future behavior of an individual or situation will be similar to what has occurred in the past.

One of the best-known applications of predictive analytics is credit scoring, used throughout the financial services industry. Analysis of a customer’s credit history, payment history, loan application and other conditions is used to rank-order individuals by their likelihood of making future credit payments on time. Those with the highest scores are ranked highest and are the best risks. That is why a high credit risk score is important to purchasers and borrowers.

Similarly, workers’ compensation claim data can be collected, integrated and analyzed from bill review, claims system, utilization review, pharmacy (PBM) and claim outcome information to score and rank-order treating physicians’ performance. Those with the highest rank are the most likely to move the injured worker to recovery more quickly and at the lowest cost.

Both predictive modeling and predictive analytics deal in probabilities regarding future behavior. Predictive modeling uses statistical methods, and predictive analytics looks at what was, is and, therefore, probably will be. For predictive analytics, it is important to identify relevant variables that can be found in the data and take action when those conditions or events occur in claims.

One way to find critical variables is to review industry research. For instance, research has shown that, when there is a gap between the date of injury and reporting or the first medical treatment, something is not right. That gap is an outlier in the data that predicts claim complexity.

Another way to identify key variables is to search the data to find the most costly cases and then look for consistent variables among them. Each book of business may have unique characteristics that can be identified in that manner.

Importantly, predictive analytics can be used concurrently throughout the course of the claim. The data is monitored electronically to continually search for outlier variables. When predictive outliers occur in the data, alerts can be sent to the appropriate person so that interventions are timely and more effective.

For example, to evaluate medical provider future performance, select data elements that describe past behavior. Look at past return-to-work patterns and indemnity costs associated with providers. If a provider has not typically returned injured workers to work in the past, chances are pretty good that behavior will continue.

For organizations looking to implement analytics, those who have already made the plunge suggest starting by taking stock of your organization’s current state. “The first thing you need to know is what is happening in your population,” says Rishi Sikka, M.D., senior vice president of clinical transformation for Advocate Health Care in Illinois. “Everyone wants to do all the sexy models and advanced analytics, but just understanding that current state, what is happening, is the first and the most important challenge.”

The accuracy and usability of results will depend greatly on the quality of the data analyzed. To get the best and most satisfying results from predictive analytics, cleanse the data by removing duplicate entries, data omissions and inaccuracies.

For powerful medical management informed by analytics, identify the variables that are most problematic for the organization and continually scan the data to find claims that contain them. Then send an alert. Structuring the outliers, monitoring the data to uncover claims containing them, alerting the right person and taking the right action is a powerful medical management strategy.

Workers’ Comp Market Trends

Senior executives from some of the top California workers’ compensation carriers identified emerging trends that are of great importance to California employers at the 2015 California Workers’ Compensation and Risk Conference.

Panelists were:

  • Moderator: Pamela Ferrandino, national casualty practice leader at Willis North America
  • Mike Mulray, chief underwriting officer at Everest National
  • John Dickey, regional president at Liberty Mutual
  • Mike Hessling, chief client officer at Gallagher Bassett
  • Glen-Roberts Pitruzzello, vice president of workers’ compensation, group benefits claim strategy and clinical operations, at the Hartford

The WCIRB projects the estimated average medical cost-per-claim will be the lowest since 2007. What do you think are the key drivers behind this improvement?

  • The independent medical review (IMR) process. IMRs are being upheld in favor of the employer – around 90% of the time. That is showing that evidence-based protocols are being followed.
  • Medical inflation has decreased. Effective utilization review (UR) programs have had a positive effect on controlling medical costs.

Will the greater use of generic drugs in the California drug formulary materially lower workers’ compensation costs?

  • Texas is a good example. We have not seen any major pushback from what Texas has done. Texas communicated to the providers, so they know how to work within the new environment. There are reports showing that the new system has been successful.
  • It could help decrease employers’ costs by 10% to 15%. There could be much broader implications to the claimant, as well. It is not just about the money, though; the upside is also the social benefit of avoiding addiction issues.

How do you see medical marijuana affecting employers?

There are three areas:

  1. Intoxication policies come into play when you have an employee using marijuana not related to a workers’ compensation claim.
  2. To date, New Mexico is the only state that ruled for compensable treatment, but the employee was already using marijuana prior to injury. This will likely continue to be challenged in various states.
  3. Medical advocacy will continue to prove effectiveness vs. the alternatives, like opioids.

What insights have you gleaned from predictive modeling?

  • We have had some great success from the claims standpoint. There have been some great advances in tools to help with predictive modeling over the past five to 10 years, like text mining, which allows modelers to look for keywords in cases that show a trend.
  • Predictive modeling can be used to see how to prevent claims from even happening. It is more effective to try to keep the claim from occurring, rather than controlling costs once a claim has occurred.
  • We are using predictive modeling more to drive early intervention in claims to reduce the costs, but we also are trying to see how we can use this information for risk control and reduce claims altogether.
  • Almost all predictive models have a level of false positives. We need to learn to filter out the white noise that is not providing useful information.

Collectively, do you think SB 863 improvements will continue to adhere, or will they be chipped away just like the others?

  • The instant you change the rules, people try to find new loopholes. You cannot stop. One or two years of results is not a trend line to claim a victory. We will probably see erosion, and we will have to come up with solutions as an industry.
  • I’m not sure if we are seeing SB 863 play out as intended, because of issues like IMR and liens. There will probably be tweaking.
  • Many stakeholders are trying to prevent erosion, so there is cautious optimism.

What are trends to look for?

  • Formulary – we could adopt the Texas system, and, while it wouldn’t play out here exactly the same way, I think we need it.
  • Ways to reduce frictional costs for employers, like IMR.
  • The impact of a new president and immigration reform on the workers’ compensation system.
  • Attracting talent for claims adjuster positions.
  • The next generation of workers entering the workforce and becoming injured workers. Engaging with them as injured workers will be vastly different from how we have engaged with workers in the past. They will have different expectations.
  • Changes in the market cycle and how it affects the health of the workers’ comp system.

The Rise of the Robo-Advisers?

The robots are here. Not the humanoid versions that you see in Hollywood movies, but the invisible ones that are the brains behind what look like normal online front-ends. They can educate you, advise you, execute trades for you, manage your portfolio and even earn some extra dollars for you by doing tax-loss harvesting every day. These robo-advisers also are not just for do-it-yourself or self-directed consumers; they’re also for financial advisers, who can offload some of their more mundane tasks on the robo-advisers. This can enable advisers to focus more on interacting with clients, understanding their needs and acting as a trusted partner in their investment decisions.

It’s no wonder that venture capital money is flowing into robo-advising (also called digital wealth management, a less emotionally weighted term). Venture capitalists have invested nearly $500 million in robo-advice start-ups, including almost $290 million in 2014 alone. Many of these companies are currently valued at 25 times revenue, with leading companies commanding valuations of $500 million or more. This has motivated traditional asset managers to create their own digital wealth management solutions or establish strategic partnerships with start-ups. Digital wealth management client assets, from both start-ups and traditional players, are projected to grow from $16 billion in 2014 to roughly $60 billion by end of 2015, and $255 billion within the next five years. However, this is still a small sum considering U.S. retail asset management assets total $15 trillion and U.S. retirement assets total $24 trillion.

What has caused this recent “gold rush” in robo-advice? Is it just another fad that will pass quickly, or will it seriously change the financial advice and wealth management landscape? To arrive at an answer, let’s look at some of the key demographic, economic and technological drivers that have been at play over the past decade.

Demographic Trends

The need for digital wealth management and the urgent need to combine low-cost digital advice with face-to-face human advice have arisen in three primary market segments, which many robo-advisers are targeting:

 

  • Millennials and Gen Xers: More than 78 million Americans are Millennials (those born between 1982 and 2000), and 61 million are Gen Xers (those born between 1965 and 1981); accordingly, this segment’s influence is significant. These groups demand transparency, simplicity and speed in their interactions with financial advisers and financial services providers. As a result, they are likely to use online, mobile and social channels for interactive education and advice. That said, a significant number of them are new to financial planning and financial products, which means they need at least some human interaction.

 

 

  • Baby Boomers: Baby boomers, numbering 80 million, are still the largest consumer segment and have retail investments and retirement assets of $39 trillion. Considering that this segment is either at or near retirement age, the urgency to plan for their retirement as well as draw down a guaranteed income during it is critical. The complexity of planning and executing this plan typically goes beyond what today’s automated technologies can provide.

 

 

  • Mass-Affluent & Mass-Market: Financial planning and advice has largely been aimed at high-net-worth (top 5%) individuals. Targeting mass-affluent (the next 15%) and mass-market (the next 50%) customers at an affordable price point has proven difficult. Combining automated online advice with the pooled human advice that some of the digital wealth management players offer can provide some middle ground.

 

Technological Advances

Technical advances have accompanied demographic developments. The availability of new sources and large volumes of data (i.e., big data) has meant that new techniques are now available (see “What comes after predictive analytics?”) to understand consumer behaviors, look for behavioral patterns and better match investment portfolios to customer needs.

 

  • Data Availability: The availability of data, including personally identifiable customer transactional level data and aggregated and personally non-identifiable data, has been increasing over the past five years. In addition, a number of federal, state and local government bodies have been making more socio-demographic, financial, health and other data more easily available through open government initiatives. A host of other established credit and market data companies, as well as new entrants offering proprietary personally non-identifiable data on a subscription basis, complement these data sources. If all this structured data is not sufficient, one can mine a wealth of social data on what customers are sharing on social media and learn about their needs, concerns and life events.

 

 

  • Machine Learning & Predictive Modeling: Techniques for extracting insights from large volumes of data also have been improving significantly. Machine learning techniques can be used to build predictive models to determine financial needs, product preferences and customer interaction modes by analyzing large volumes of socio-demographic, behavioral and transactional data. Big data and cloud technologies facilitate effective use of this combination of large volumes of structured and unstructured data. In particular, big data technologies enable distributed analysis of large volumes of data that generates insights in batch-mode or in real-time. Availability of memory and computing power in the cloud allows start-up companies to scale on demand instead of spending precious venture capital dollars setting up an IT infrastructure.

 

 

  • Agent-Based Modeling: Financial advice; investing for the short-, medium- and long-term; portfolio optimization; and risk management under different economic and market conditions are complex and interdependent activities that require years of experience and extensive knowledge of numerous products. Moreover, agents have to cope with the fact that individuals often make investment decisions for emotional and social reasons, not just rational ones.

 

Behavioral finance takes into account the many factors that influence how individuals really make decisions, and human advisers are naturally skeptical that robo-advisers will be able to match their skills interpreting and reacting to human behavior. While this will continue to be true for the foreseeable future, the gap is narrowing between an average adviser and a robo-adviser that models human behavior and can run scenarios based on a variety of economic, market or individual shocks. Agent-based models are being built and piloted today that can model individual consumer behavior, analyze the cradle-to-grave income/expenses and assets/liabilities of individuals and households, model economic and return conditions over the past century and simulate individual health shocks (e.g., need for assisted living care). These models are assisting both self-directed investors who interact with robo-advisers and also human advisers.

Evolution of Robo-advisers

We see the evolution of robo-advisers taking place in three overlapping phases. In each phase, the sophistication of advice and its adoption increases.

 

  • First Generation or Standalone Robo-Advisers: The first generation of robo-advisers targets self-directed end consumers. They are standalone tools that allow investors to a) aggregate their financial data from multiple financial service providers (e.g., banks, savings, retirement, brokerage), b) provide a unified view of their portfolio, c) obtain financial advice, d) determine portfolio optimization based on life stages and e) execute trades when appropriate. These robo-advisers are relatively simple from an analytical perspective and make use of classic segmentation and portfolio optimization techniques.

 

 

  • Second Generation or Integrated Robo-Advisers: The second generation of robo-advisers is targeting both end consumers and advisers. The robo-advisers are also able to integrate with institutional systems as “white labeled” (i.e., unbranded) adviser tools that offer three-way interaction among investors, advisers and asset managers. These online platforms are variations of the “wrap” platforms that are quite common in Australia and the UK, and offer a cost-effective way for advisers and asset managers to target mass-market and even mass-affluent consumers. In 2014, some of the leading robo-advisers started “white labeling” their solutions for independent advisers and linking with large institutional managers. Some larger traditional asset managers also have started offering automated advice by either creating their own solutions or by partnering with start-ups.

 

 

  • Third Generation or Cognitive Robo-Advisers: Advances in artificial intelligence (AI) based techniques (e.g., agent-based modeling and cognitive computing) will see second generation robo-advisers adding more sophisticated capability. They will move from offering personal financial management and investment management advice to offering holistic, cradle-to-grave financial planning advice. Combining external data and social data to create “someone like you” personas; inferring investment behaviors and risk preferences using machine learning; modeling individual decisions using agent-based modeling; and running future scenarios based on economic, market or individual shocks has the promise of adding significant value to existing adviser-client conversations.

 

One could argue that, with the increasing sophistication of robo-advisers, human advisers will eventually disappear. However, we don’t believe this is likely to happen anytime in the next couple of decades. There will continue to be consumers (notably high-worth individuals with complex financial needs) who seek human advice and rely on others to affect their decisions, even if doing so is more expensive than using an automated system. Because of greater overall reliance on automated advice, human advisers will be able to focus much more of their attention on human interaction and building trust with these types of clients. 

Implications to Financial Service Providers

How should existing producers and intermediaries react to robo-advisers? Should they embrace these newer technologies or resist them?

 

  • Asset Managers & Product Manufacturers: Large asset managers and product manufacturers who are keen on expanding shelf-space for their products should view robo-advisers as an additional channel to acquire specific type of customers – typically the self-directed and online-savvy segments, as well as the emerging high-net-worth segment. They also should view robo-advisers as a platform to offer their products to mass-market customers in a cost-effective manner.

 

 

  • Broker Dealers and Investment Advisory Firms: Large firms with independent broker-dealers or financial advisers need to seriously consider enabling their distribution with some of the advanced tools that robo-advisers offer. If they do not, then these channels are likely to see a steady movement of assets – especially of certain segments (e.g., the emerging affluent and online-savvy) – from them to robo-advisers.

 

 

  • Registered Independent Advisers and Independent Planners: This is the group that faces the greatest existential threat from robo-advisers. While it may be easy for them to resist and denounce robo-advisers in the short term, it is in their long-term interest to embrace new technologies and use them to their advantage. By outsourcing the mechanics of financial and investment management to robo-advisers, they can start devoting more time to interacting with the clients who want human interaction and thereby build deeper relationships with existing clients.

 

 

  • Insurance Providers and Insurance Agents: Insurance products and the agents who sell them also will feel the effects of robo-advisers. The complexity of many products and related fees/commissions will become more transparent as the migration to robo-adviser platforms gathers pace. This will put greater pressure on insurers and agents to simplify and package their solutions and reduce their fees or commissions. If this group does not adopt more automated advice solutions, then it likely will lose its appeal to attractive customer segments (e.g., emerging affluent and online-savvy segments) for whom their products could be beneficial.

 

Product manufacturers, distributors, and independent advisers who ignore the advent of robo-advisers do so at their own risk. While there may be some present-day hype and irrational exuberance about robo-advisers, the long-term trend toward greater automation and integration of automation with face-to-face advice is undeniable. This situation is not too dissimilar to automated tax-advice and e-filing. When the first automated tax packages came out in the ’90s, some industry observers predicted the end of tax consultants. While a significant number of taxpayers did shift to self-prepared tax filing, there is still a substantial number of consumers who rely on tax professionals to file their taxes. Nearly 118 million of the 137 million tax returns in 2014 were e-filings (i.e., electronically filed tax returns), but tax consultants filed many of them. A similar scenario for e-advice is likely: a substantial portion of assets will be e-advised and e-administered in the next five to 10n years, as both advisers and self-directed investors shift to using robo-advisers.

Survey: Predictive Modeling Lifts Profits

The breadth and depth of predictive modeling applications have grown, but, of equal importance, the percentage of participants reporting a positive impact on profitability has dramatically increased, Towers Watson’s most recent predictive modeling survey finds.

Our 2014 Predictive Modeling Benchmarking Survey indicates the use of predictive modeling in risk selection and rating has increased significantly for all lines of business over the last year, continuing a long-term trend. For instance, in the personal auto business, 97% of participants said that in 2014 they used predictive modeling in underwriting/risk selection or rating/pricing, compared with 80% in 2013, a 17-percentage-point increase. For standard commercial property/commercial multiperil (CMP)/business-owner peril (BOP), the number jumped 19 percentage points, to 51%, during the same time period (Figure 1). In fact, the percentage of participants that currently use predictive modeling increased for every line of business covered in the survey.

Figure 1. The use of predictive modeling in risk selection/rating has increased significantly for all lines of business over the last year

Does your company group currently use or plan to use predictive modeling in underwriting/risk selection or rating/pricing for the following lines of business?

Sophisticated risk selection and rating techniques are particularly important in personal lines, where models have now penetrated most of the market. An overwhelming 92% of survey participants cited these techniques as essential drivers of performance or success. To a significant degree, this was also true for small to mid-sized commercial carriers, with 44% citing sophisticated risk selection and rating techniques as essential and another 42% identifying them as very important.

Even as the use of predictive modeling extends to more lines of business, there is an increasing depth in its use. Predictive modeling applications are increasingly being deployed by insurance companies more broadly across their organizations as their confidence in modeling increases. For example, 57% of survey participants currently use predictive modeling techniques for underwriting and risk selection, and another 33% have plans to use them over the next two years. Although a more modest 28% currently use predictive modeling to evaluate fraud potential, a sizable additional 36% anticipate using it for this purpose over the next two years. Survey participants report plans to deploy predictive modeling applications in areas including claim triage, evaluation of litigation potential, target marketing and agency management. These applications will favorably affect loss costs, expenses and premium growth.

THE BOTTOM LINE

Eighty-seven percent of our survey participants report that predictive modeling improved profitability last year, an increase of eight percentage points over 2013 (Figure 2). The increase continues a pattern of growth over several years.

Figure 2. Companies implementing predictive models have increasingly seen favorable profitability impacts over time

What impact has predictive modeling had in the following areas?

Slide 9 of Executive Summary

A positive impact on rate accuracy helps explain the improvement. In fact, the percentage of carriers citing a positive impact on rate accuracy has increased every year since 2010, when 70% cited a positive impact. In three of the past four years, the percentage-point increase in carriers citing a positive impact has hovered around 10%. In this year’s survey, nearly all (98%) of the respondents reported that predictive modeling has improved their rate accuracy. Improved rate accuracy has both top- and bottom-line benefits: It boosts revenue because it enables insurers to price more effectively in very competitive markets, retaining existing customers and attracting potential customers with rates that accurately reflect their level of risk. At the same time, rate accuracy drives profit because it also helps carriers identify and write more profitable business,and not focus solely on market share and price.

More accurate rates also improve loss ratios, which have improved in parallel, according to our survey participants. In 2014, 91% of survey participants cited the favorable impact of predictive modeling on loss ratios, an increase of 14 percentage points over 2013. When premiums more accurately reflect risk, losses are more likely to be properly funded.

TOP-LINE GROWTH

The bottom-line fundamentals — profitability, rate accuracy and loss ratio improvement — identified in our survey are complemented by top-line benefits. Positive impacts were registered on renewal retention (55%), underwriting appetite (46%) and market share (41%).

THE NEXT STEP

Sophisticated risk selection and rating are cited as essential by many of our participants, but our survey indicates that, despite favorable trends, insurers are still far from leveraging sophisticated modeling techniques to their fullest, even in pricing. Two-thirds of participants aren’t currently using price integration (the overlay of customer behavior and loss cost models to create metrics that measure different rate scenarios) for any products. A few are past price integration and are currently implementing price optimization (harnessing a mathematical search algorithm to a price integration framework to maximize profit, volume and other business metrics) for some products.

The disparity between what is viewed as the optimal use of modeling techniques and the current level of implementation needs to be bridged if insurers want to leverage predictive modeling as a competitive advantage to identify and capture profitable business. Increasingly, insurers are making greater use of analytics including by peril rating (which replaces rating at the broad, line-of-business level with specific rating by coverage), proprietary symbol (customizing vehicle classifications for personal automobile policies) and territorial and credit analysis.

Those insurance companies that can’t employ sophisticated risk identification and management tools face the possibility of losing profitable business and adverse selection.

MORE PROGRESS IS STILL POSSIBLE

Profitability is hard-earned in the current competitive property/casualty market, and predictive modeling is recognized by a steadily growing number of companies as an invaluable tool to improve both top- and bottom-line performance that ultimately reflects in earnings growth. Our survey suggests that insurers are increasingly comfortable with predictive modeling and are using it in a growing number of capacities. However, participant responses also indicate that there are still many benefits offered by predictive modeling and other more sophisticated analytical tools that have not been achieved, such as treating data as an asset and more effectively using predictive modeling applications to improve claim and other functional results. Improving performance on these issues alone could make a significant difference in the profitability of insurance companies and offers all the more reason to explore new ways to benefit from data-driven analytics and predictive modeling.

ABOUT THE SURVEY

Towers Watson conducted a web-based survey of U.S. and Canadian property/casualty insurance executives from Sept. 3 through Oct. 22, 2014. The results discussed in this article represent the views of 52 U.S. insurance executives. Responding companies represent a significant share of the U.S. property/casualty insurance market for both personal lines carriers (17%) and commercial lines carriers (22%).