Articles - Predictive Analytics in Insurance

Building models alone is not an effective use of predictive analytics. He offers a four-step procedure that will position actuaries for leadership and endurance in the industry. Actuaries use predictive modelling and data analysis techniques on large data sets to identify patterns for business use. The profession is currently seeking to advance its use of predictive analytics methods across the board.

 By Rene Schoenauer, product marketing manager, EMEA, Guidewire Software

 While actuarial science has been leveraged for decades in insurance pricing, the industry stands to benefit greatly by both expanding the methods being used, and extending those methods to claims operations and underwriting. For example, predictive modelling can help with early identification of people whose injuries are likely to become more severe so that proactive action can be taken. However, using predictive analytics effectively across an entire business can be complicated.

 Typically, actuarial teams develop various models and test them against a data set to identify an analytical approach that performs best. Deploying models into the operation often consist of emailed spreadsheets and triage lists of potentially problematic claims or policies. This offline approach to rollout limits the probability of success.

 How this happens is not surprising. Actuaries are not experts at core system integration. Even for IT experts, there is a lot to consider beforehand. Who will be using it? How can the data be made usable? How can insurers oversee the operation and impact of the models on an ongoing basis? To make a success of predictive analytics requires a four-step process. It starts with establishing great models, but it certainly does not stop there.

 1 – Create the models
 Obviously, the very first step is to acquire data and build models. To do this, insurers must acquire and prepare data, set it up, and choose some modelling technique, whether generalised linear models or advanced analytics models. Whichever approach is chosen, models must be built on sound methodology and deep subject matter expertise that enables accuracy, credibility, and that can be repeated for multiple future models.

 Remember, part of building models is understanding how, when, and where the models must be used: What systems will they touch? What business rules must be changed? Where is the data to build the models coming from, and where is the data to use the models in operation coming from?

 Actuaries may believe mistakenly that building these models means they are using predictive analytics effectively. The problem with stopping here, however, is that it does not yield any kind of business result. For that, actuaries must deploy the models.

 2 – Take a strategic approach to deployment
 Implementation of predictive models is arguably one of the biggest hurdles within the whole process. During this phase, the models are integrated with existing systems. The best thing to do is take a strategic approach by starting deployment with a system that will make the output easy for the organisation to get to grips with.

 Often from an IT integration perspective, the easiest place to begin deploying models is in pricing. The ratings structure is likely to be familiar to the company as a whole, and a new model that creates a new variable is pretty easy to plug back into a ratings structure.

 That said, it is important to keep in mind that it can be more complicated to determine which models to deploy to underwriters or claims adjusters, to inform the underwriting or claims-handling processes, for example. Asking the IT department to programme complex models into the underwriting or claims workflows is a lot to ask. It is difficult to deploy the correct algorithm and hard to get the data from a production environment, transform it to be usable for the model, and send appropriate data back from the model.

 3 – Interact with the output
 If actuaries want predictive analytics to truly make an impact on their business, models must be positioned in a way that allow users to interact easily with the output. For example, if a claims adjuster or underwriter faces a sharp learning curve and must jump through an inordinate number of hoops to use the information, that model will not be used. The best way to avoid this is to embed predictive analytics directly into the business user’s workflow whenever possible.

 4 – Monitor the operational systems
 Many actuaries fail to put monitoring at the top of their priority list until it is too late. The models have been built and deployed, and then insurers realise that they must have some way to monitor these now-operational systems. Are they working? Is the data right?

 Even if the models are working well, are things changing? Should they be refreshed?

 It is Important to note that a data-and-analytics-driven organisation delivers better business results all around. Executives get a comprehensive system that uncovers measurable results, and business users get easy-to-consume information that helps them do their jobs more effectively. Essentially, predictive analytics transforms the business and becomes fundamental to every aspect of it.

 We all agree that predictive modelling is a powerful tool that can help us make better business decisions in every segment of the actuary industry. The ‘how we do it’ is just as important as the ‘what we do’. The efficiency of how we run a predictive analytics project matters more than saying “yes, we do predictive analytics because it is a recommended industry best practice.”

 Simply building models alone will not enable effective use of predictive analytics. This four-step process—along with the technology that helps achieve all four steps seamlessly and in an integrated fashion with as small an IT footprint as possible—will help actuaries provide meaningful insight and clarity of vision to the businesses they serve.

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