By Ross Fisher
Big data and predictive modeling are transforming how claim examiners identify and manage high-risk workers' compensation claims, and yielding results that are helping to reduce return-to-work time frames and claim costs. Vice President and Claim Data Scientist Paul Drennan is a driving force behind The Hartford's innovative approach to predictive modeling, which informs the company's comprehensive solutions for workers' compensation claim intervention and management. I recently sat down with Paul to discuss big data and why it is such a critical value proposition for large, loss-sensitive companies.
RF: What is big data?
PD: Big data is a product of the digital age. It refers to the large streams of information that businesses collect every day through digital sources ranging from websites and social networks to sensors and electronic communications. This data can be analyzed, often in real time, to quickly reveal insights and trends that aid decision-making, improve business performance and build competitive advantage.
RF: What makes big data different from traditional data?
PD: Big data is coming at us faster and in a volume that is exponentially greater than traditional data. For instance, we may collect a payload of data through our in-house claim system, but that's still fairly manageable compared to the data captured on every click on all of a company's web portals. Or, consider an activity tracker like Fitbit® which generates a pulse of data every 15 seconds for everyone wearing the device. Data of that velocity and volume is big data.
Big data is also characterized by variety, specifically in the form of freeform data that can be sifted through for insight. A claim handler can enter the standard data about a car accident to a claim system, but it's the details in his/her text notes about the police report and the interview with the claimant and the claimant’s doctor that yield insight about potential fraud. That data doesn't have a structure; there is no template for capturing it. Big data pulls the meaning out.
RF: How is big data processed so it can be put to practical use?
PD: We use advanced analytical methods to see patterns in the data and build models based on those patterns. Predictive models allow us to forecast what might happen in the future based on what happened in the past, providing the user with objective information on which to base decisions. A credit score is an example of predictive analytics that enables banks and other lending institutions to evaluate a potential customer's ability to make loan payments on time.
While predictive modeling is an alerting mechanism, prescriptive analytics go a step further by offering advice or the “next best action.” An everyday example is a traffic navigation application that identifies possible driving routes and recommends the speediest one based on an evaluation of multiple factors.
RF: How are insurance companies using predictive modeling?
Big data has application in many areas of insurance operations. According to a 2016 survey by Willis Towers Watson
, 67 percent of property and casualty insurers currently use predictive modeling for underwriting and risk selection. Other top but less used applications among commercial insurers include claims triage (15 percent of insurers), fraudulent claim potential (14 percent) and litigation (10 percent)1
. A primary application of predictive modeling at The Hartford is in the area of workers’ compensation claims. As far back as 2008, we deployed a model to identify potentially high-risk workers' compensation claims before they become volatile. The model that we launched has been rebuilt and improved upon twice, and is patented.
RF: Tell us more about The Hartford's use of predictive modeling for claims.
PD: Our claim predictive modeling portfolio spans property and casualty lines of business as well as group benefits. We have models for everything ranging from fraud, subrogation and reinsurance to large loss, claim volatility and triage.
Within claims, we've concentrated our energy on text mining of unstructured data. That's where the secret sauce is, where the nuances of the claim are revealed. We have patents on the processes we use to take in that data every day, translate it from whatever language it's in (such as nomenclature, shorthand, medical terms and prescription drug names), apply labels to it and recognize patterns that allow us to make conclusions to improve claim handling. Our text mining engine is very robust, which enables us to quickly identify higher-risk claims to ensure that these claims are assigned to our more experienced adjusters.
RF: What staffing resources make your predictive modeling at The Hartford possible?
PD: The success of our models depends upon input from a cross-functional team with a broad range of skills and experience, including claim handlers and supervisors, experts in strategy and workflow processing, and data scientists and risk engineering consultants. We also bring in our actuarial and finance partners to evaluate how well our models our working.
Our text mining engine is the work of an in-house team of five “knowledge engineers” who build the dictionaries, develop the pattern-matching rules and evolve these capabilities to new use cases and sources of data.
RF: What do you see ahead for the future of big data?
PD: According to Willis Towers Watson, P&C insurers have big aspirations to expand their use of big data across business functions. At The Hartford, big data will play an increasingly important role, helping us achieve greater efficiencies and better serve our customers while also empowering a new generation of employees.
About Paul Drennan
Paul Drennan is vice president and predictive analytics lead in the data science unit at The Hartford. With more than 20 years of experience establishing and leading data science teams across multiple industries, Drennan focuses on marrying world class analytics with deep process knowledge in large scale operations to create sustained profitable changes in business practices.
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