How AI, data analytics could help identify insurance fraud

People are becoming more sophisticated in perpetrating fraud—filing claims online and operating from around the world, people are approaching fraud digitally and at higher frequencies.

Insurance companies have been put to the test since spring of 2020, as pandemic relief funds came into play and insurance organizations felt the changing profile of claims within personal and commercial lines. With the increasing difficulty to predict and segment claims, many organizations have found themselves falling behind, giving people a wide berth to carry out fraud without detection.

Fortunately, there are new methods, using artificial intelligence, that eases the burden and helps insurance companies stay one step ahead. Historically, insurers tend to react tactically to verification and customer checks, leaving gaps in detection accuracy because of the manual nature of this process. The latest AI techniques can help fill in these oversights with interpretable results that provide context for automation and efficient operational decisions. The combination of data and tech gives the insurer full control to see how any AI model decides to ensure a fair customer process, following regulatory scrutiny.

Preventing insurance fraud
As seemingly endless lines of data pile up each day, it’s no wonder insurance companies are overwhelmed by disorganized, fragmented data that never seems good enough to effectively be put to use. The problem isn’t the data itself, though—it’s how the data is analyzed, how quickly it becomes out of date and how resources are wasted on trying to decipher patterns without automation.

The right AI can help insurance companies detect fraud as it occurs, and can help connect data sets that would usually be siloed. AI and analytics meanwhile, give organizations new levels of control over fraud prevention by providing a level of context regarding behaviors, relationships and modus operandi that has previously been out of reach. This lets companies focus on customer profiles and can give investigators the ability to quickly identify fraud as it happens, while similarly identifying legitimate claims, leaving a consumer’s journey seamless and uninterrupted.

Gaining this accurate, contextualized and holistic view gives insurance companies the ability to use their existing data more effectively. It also frees up the time and talent of employees so they can carry out complex investigations as needed instead of spending hours sifting through out-of-date data only to come up short.

Data visualization
Once companies begin to harness the power of data, organizational leaders will find that analytics and AI bring about several benefits. The technology not only helps detect fraud and improve customer experience, but it also speeds up decision-making processes across an organization including areas like customer insight, underwriting and risk.

AI does this by providing a fully transparent view of the customer, their claim and their network. This view then lets decision-makers automate, build and understand the timeline of events leading to a claim. Data and visualization speed up this process, gaining back valuable time and energy that employees can instead use to improve the experience of legitimate customers and investigate and pursue fraudsters before they cause harm.

An enterprise view of data
With the right technology in place, it becomes far easier for companies to decide whether to pursue a fraud investigation in the first place. That extra time can help prevent further fraudulent activities and give insights into criminal networks that may have infiltrated a company’s claims process.

To fight insurance fraud, companies need to be able to consolidate all the disparate data they currently have and combine it with powerful, external insights. Instead of using fragmented data, they need a data hub that’s accessible across an organization, in a single view that can help insurers understand their customers, who they are, what they do, and how to detect fraud as it occurs.