Magazine article Risk Management

Calculating the Future: (How to Develop a Successful Predictive Analytics Program)

Magazine article Risk Management

Calculating the Future: (How to Develop a Successful Predictive Analytics Program)

Article excerpt

FOR MANY RISK MANAGERS AND CLAIMS PROFESSIONALS, THE USE of predictive analytics is a complex subject, especially since there is a lack of standard methodologies. The lines between true predictive analytics and data analysis and data access are often blurred. Terms are interchanged and performance is unsubstantiated. As a result, there is often confusion, which can hinder a risk professional's evaluation of an algorithm's efficacy. Becoming versed in key aspects of data and statistics, as well as knowing what to look for when evaluating a predictive analytics algorithm, can arm the risk manager with the information needed to properly evaluate, apply and use predictive analytics.

START WITH THE BUSINESS PROBLEM

The goal of predictive analytics is to generate information that will help make better decisions. Therefore, it is important to build the predictive analytics algorithm with a specific business problem in mind. What is the nature of the business problem you are trying to solve? What is the outcome you hope to achieve? For example, if you are seeking an algorithm that, when put into practice, will help avoid claim losses greater than $I million, it is not necessary to build a model to discern the dollar value of losses less than that amount.

Keeping the business problem in mind right from the start can also help determine if more than one algorithm is necessary. For example, it is often important to measure the full lifetime value of a customer, not just the initial conversion to the business. Customers who are most easily converted may not be the most easily retained. In this case, it might be best to have two predictive models: one to predict conversion and another to predict retention.

CONSIDER YOUR DATA SET

Data is the foundation for predictive analytics. To qualify as "predictive analytics," an algorithm must rely upon a sufficient quantity of data to determine the best predictors for the most accurate predictions. Consider Google: By analyzing the data entered in their search engine, they accurately predict trends. In fact, Google can estimate flu levels around the world by adding all flu-related search queries together. Year after year, the company compares its model's estimate to traditional flu surveillance systems and refines the model to improve performance. One report even suggested that Google can detect regional flu outbreaks 10 days faster than the Centers for Disease Control.

In workers compensation, pharmacy data can be used to determine which injured workers are most likely to experience high pharmacy costs or have the longest duration of opioid use. By using historical pharmacy data, you can correlate each independent factor known about the injured workers to long-term severity. With statistical modeling, these balance against each other to generate an accurate prediction. Therefore, when evaluating a predictive analytics algorithm, always consider the data set. The more data the algorithm uses, the more accurate the predictions generated.

GEOGRAPHY MATTERS

It is just as important to consider the geography as it is to consider the data set. In workers compensation, for example, every state has different regulations. One state's solution or regulation may not work in another state for many reasons, including some specific to their geography. This is a very old problem with the use of applied statistics that is still the case today. Franz Kafka, who was a workers compensation attorney before finding fame as an author, faced the problem of applying data from Germany as he tried to manage the Austrian system. In his case, he called the data from another geography "defective and inadequate."

Using data appropriate to your geography avoids what Kafka scholars call "a practice of calculating with dubious figures"--the kind of illogical endeavor that might now be called "Kafkaesque."

KEEP TIME AND INTERACTIONS IN MIND

One of the greatest limitations in predictive analytics is the assumption that the future will always resemble the past. …

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