With your experience in the insurance industry, you understand more than most about how the actions of a smattering of people can cause disproportionate damage. The $80 billion in fraudulent claims paid out across all lines of insurance each year, whether soft or hard fraud, is perpetrated by lone individuals, sketchy auto mechanic shops, or the occasional organized crime group. The challenge for most insurers is that detecting, investigating, and mitigating these deceitful claims is a time-consuming and expensive process.
Rather than accepting loss to fraud as part of the cost of doing business, some organizations are enhancing their detection capabilities with insurance analytics solutions. Here is how your organization can use insurance fraud analytics to enhance fraud detection, uncover emerging criminal strategies, and still remain compliant with data privacy regulations.
Recognizing Patterns Faster
When you look at exceptional claims adjusters or special investigation units, one of the major traits they all share is an uncanny ability to recognize fraudulent patterns. Their experience allows them to notice the telltale signs of fraud, whether it’s a body shop regularly sending suspicious estimates or complex billing codes intended to hide frivolous medical tests. Though you trust adjusters, many rely on heuristic judgments (trial and error, intuition, etc.) rather than hard rational analysis. When they do have statistical findings to back them up, they struggle to keep up with the sheer volume of claims.
This is where machine learning techniques can help to accelerate pattern recognition and optimize the productivity of adjusters and special investigation units. An organization starts by feeding a machine learning model a large data set that includes verified legitimate and fraudulent claims. Under supervision, the machine learning algorithm reviews and evaluates the patterns across all of the claims in the data set until it has mastered the ability to spot fraud indicators.
Let’s say this model was given a training set of legitimate and fraudulent auto insurance claims. While reviewing the data for fraud, the algorithm might spot links in deceptive claims between extensive damage in a claim and a lack of towing charges from the scene of the accident. Or it might notice instances where claims involve rental cars rented the day of the accident that are all brought to the same body repair shop. Once the algorithm begins to piece together these common threads, your organization can test the model’s unsupervised ability to create a criteria for detecting deception and spot all instances of fraud.
What’s important in this process is finding a balance between fraud identification and instances of false positives. If your program is overzealous, it might create more work for your agents, forcing them to prove that legitimate claims received an incorrect label. Yet when the machine learning model is optimized, it can review a multitude of dimensions to identify the likelihood of fraudulent claims. That way, if an insurance claim is called into question, adjusters can comb through the data to determine if the claim should truly be rejected or if the red flags have a valid explanation.
Detecting New Strategies
The ability of analytics tools to detect known instances of fraud is only the beginning of their full potential. As with any type of crime, insurance fraud evolves with technology, regulations, and innovation. With that transformation comes new strategies to outwit or deceive insurance companies.
One recent example has emerged through automation. When insurance organizations began to implement straight through processing (STP) in their claim approvals, the goal was to issue remittances faster, easier, and cheaper than manual processes. For a time, this approach provided a net positive, but once organized fraudsters caught wind of this practice, they pounced on a new opportunity to deceive insurers.
Criminals learned to game the system, identifying amounts that were below the threshold for investigation and flying their fraudulent claims under the radar. In many cases, instances of fraud could potentially double without the proper tools to detect these new deception strategies. Though most organizations plan to enhance their anti-fraud technology, there’s still the potential for them to lose millions in errant claims – if their analytics are not programmed to detect new patterns.
In addition to spotting red flags for common fraud occurrences, analytics programs need to be attuned to any abnormal similarities or unlikely statistical trends. Using cluster analysis, an organization can detect statistical outliers and meaningful patterns that reveal potential instances of fraud (such as suspiciously identical fraud claims).
Even beyond the above automation example, your organization can use data discovery to find hidden indicators of fraud and predict future incidents. Splitting claims data into various groups through a few parameters (such as region, physician, billing code, etc. in healthcare) can help to find unexpected correlations or warning signs for your automation process or even human adjusters to flag as fraud.
Safeguarding Member PII
As you work to improve your fraud detection, there’s one challenge that all insurers face: protecting the personally identifiable information (PII) of members while you analyze your data. The fines related to HIPAA violations can amount to $50,000 per violation and other data privacy regulations can result in similarly steep fines. The good news is that organizations can balance their fraud prediction and data discovery with security protocols, if their data ecosystem is appropriately designed.
Maintaining data privacy compliance and effective analytics requires some maneuvering. Organizations that derive meaningful and accurate insight from their data must first bring all of their disparate data into a single source of truth. Yet unless they also implement access control through a compliance-focused data governance strategy, there’s a risk of regulatory violations while conducting fraud analysis.
One way to limit your exposure is to create a data access layer that tokenizes the data, replacing any sensitive PII with unique identification symbols that keep data separate without revealing members. Paired with clear data visualization capabilities, your adjusters and special investigation units can see the clear cut trends and evolving strategies without revealing individual members or claimants. From there, they can take their newfound insights into any red flag situation, saving your organization millions while reducing the threat of noncompliance.
Want to learn more about how the right analytics solutions can help you reduce your liability, issue more policies, and provide better customer service? Check out our Insurance Analytics Solutions page for use cases that are transforming your industry.
Greg Marsh is a Data Engineer Manager at Aptitive. In his role, Greg facilitates the discovery of business insights from data. From “Big” data like IoT streams or classic relational ERP information, Greg helps companies to unlock the power of their data.