Red-flagging

This is the final part in a series by Vince Venturella on the future of claims litigation technology. Part one was about predictive analytics in insurance claims defense. Vince’s second post focused on how AI technologies can help claims professionals in the area of legal billing. In this post, Vince turns to the topic of “red-flagging.”

Red flagging is the practice of using technology to call attention to claims that are likely to become troublesome without intervention. There is a myriad of issues that can be identified via red flagging, such as the potential for fraud, high risk of an undesirable outcome in litigation, or rising complexity. While the severity of issues that can be identified varies widely, all are more effectively addressed proactively. Red flagging can help draw attention to emerging issues at any point during the lifecycle of a claim and even after claims are closed. Let’s examine the possibilities at three points:

At the Start of a Claim/Litigation

Red flagging can be used early in the claims process to identify elements about the claim and/or litigated claim. Technologies such as artificial intelligence (AI) have the ability to examine a very large data set and use it to provide analytical insights in a more detailed way, and earlier, than a human ever could. For example, if enough historical information is available, it is possible for technology to spot potential fraud shortly after the claim is opened, even before litigation.

The claims industry has long employed well-trained professionals who are good at identifying possible fraud, but it’s an intensive and time-consuming process that, while critical for the company, doesn’t add value for policyholders. AI is very good at triaging open claims and recommending cases that people should look at closely, highlighting these so that experts can then perform additional investigation.

Similarly, some litigated claims may have dimensions or fact patterns that imply they will end up being far more costly (either in legal fees or indemnity) than similar past litigation within the same line of business. Here as well, those associates with years or decades of experience may be good at flagging these issues early, but having the system recognize these and highlight them helps ensure that nothing is missed. When these sorts of high risk or high severity litigated claims are recognized early, case plans can be developed from the start to properly account for the situation and lower the overall risk.

During the Litigation Process

Increased risk or severity issues may not be identified at the very beginning of a litigated claim. Sometimes, they are found during discovery or arise in deposition or early research. This situation can be far more dangerous because, without proper communication, there can be a lag time before they are dealt with.

Systems should be in place supporting quick and efficient collaboration between outside counsel and carrier claims staff, in combination with AI technology, that combs through claims data using tools like natural language processing. As soon as an issue is found, it should be immediately marked as a red flag so that case plans can be adjusted. The sooner these things are found, the more likely that a positive outcome can be reached by managing the complexities over the life of the case. AI’s ability to always “watch” the data, down to the smallest detail, means that problems are likely to be spotted earlier than they would be by humans, whose time is limited.

Retrospective Analysis

It may seem at first that after a case has closed, it’s too late for AI systems to make a difference. While that may sometimes be true for individual litigation (if appeals are not possible), a line of business is larger than one individual case. The ability to have AI examine past work means that it can provide previously unknown correlations and red flags that can be used in future work.

Technology can also be used in retrospect to identify good work – for example, putting a spotlight on law firm counsel that effectively communicates and handles these issues well when they arise. This provides a data-backed way to find and select outside counsel partners that understand not only your business but your individual cases. It’s easy for outside counsel to say “We get you. We understand your business,” but the data doesn’t lie. Outside counsel that is attentive, communicative and effective will show clearly in the data set. This technology makes it possible to more confidently show that you are selecting the best counsel to represent your policyholders and fulfill your fiduciary responsibilities.

As we’ve seen throughout this series, technology – and AI in particular – is making it easier for claims professionals to achieve the business goals of the organizations they work for. When combined with the experience and expertise that humans have to offer, next-generation technologies provide value that helps ensure the success of insurance carriers and good outcomes for their policyholders.

Learn more about the ways artificial intelligence can help insurance professionals in our whitepaper, Artificial Intelligence in Claims Litigation: Meeting Challenges and Predicting the Future.


About The Author

Vince Venturella

Vince is the product manager responsible for the development of Wolters Kluwer's ELM Solutions insurance market offerings for claims and staff counsel. Vince is a strategic, results-oriented legal technology leader with a consistent record of improving processes, developing innovative solutions, and leading diverse product teams. He has worked in legal management consulting and technology solutions within the insurance market for almost a decade. Vince is a graduate of The Ohio State University in Columbus, Ohio.