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Improving claims decisions with embedded analytics and reduced biases

Operationalize analytics to overcome insurance adjuster biases and improve claims decisions.

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Many insurers have begun adopting advanced data analytics to improve various aspects of their claims functions. Think predictive analytics to improve fraud programs, fitness bands and wearable technology to help improve customers’ health and reduce claims incidents, and predictive models to identify subrogation potential or predict and maximize recovery amounts and collectability.  Despite how truly transformative data and analytics can be for insurers’ claims processes, every adjuster and business unit manager brings his or her own business experiences and personal biases to every claim. It’s what humans do. And even if the biases aren’t necessarily negative, they detract from the company’s ability to rely on standard claims decisions across the board.

For example, the availability heuristic – that is, a person’s tendency to judge the frequency or likelihood of an event based on how easily relevant instances come to mind – influences how an adjuster responds to a claim. Likewise, probabilities and risk bias — that is, our inclination to overstate the probability of certain kinds of negative outcomes — affects adjusters’ estimation of case value. And in fraud determination and subrogation, confirmation bias — that is, the unconscious reference to perspectives that confirm pre-existing views — often comes into play. Unfortunately, these and other types of biases lead to poor, and costly, business decisions. 

So, what can insurers do?

Operationalize analytics to overcome bias and improve results

Although personal bias and recent experiences can greatly affect claims decisions, adjusters can learn to make decisions with more standardized, reliable methods. That journey begins with a reimagined process that leverages technology and enables decision-makers to better embrace analytics. Insurers must think of this process as occurring over a constant loop that is designed to continuously improve their operations by more effectively steering data into insights and then actions.

Provide decision support training to help adjusters make decisions more analytically

As insurance companies struggle to take advantage of the  growing possibilities of data resources and analytics tools, they must make sure that their front-line professionals have the capabilities to make reliable decisions and use tools more efficiently.

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Insurers should build frameworks containing specialized training modules that will help their claims adjusters and business unit managers overcome their inherent biases, and realize the value that data can bring to their decision-making process. These training modules will help insurance professionals better understand points in the claims process where they should employ expert decision-making skills, and how using those skills can result in a more reliable, standardized flow of claims. Insurers should also offer skills training that not only enables supervisors to better equip their teams for making decisions, but also prepares them to implement the latest analytics tools to improve their work.

Once adjusters and other decision-makers understand how to make decisions that are soundly rooted in analytical thinking and mitigate the influence of personal bias, their companies can introduce decision-support models, visualizations, enriched data, and historical statistics into the mix to streamline the claims process and drive better business outcomes. And because claims are insurance companies’ greatest source of cash outflow, any improvement in the indemnity and expense of paying claims can deliver a large financial payoff.

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