The insurance claims process is on the verge of changing in significant ways thanks to new technologies that are being adopted by insurance carriers. One example, artificial intelligence (AI), is in use across the claims space to aid with straight-through processing (STP) claims, fraud, QA audits, and other aspects of claims processing. AI is an effective tool for sifting through large volumes of data and detecting, for example, claims with high risk for fraud. This facilitates identification of claims that require auditing, as well as those that represent a low risk and can be processed quickly to expedite the customer experience.
Some people worry that big data, analytics, and AI will damage their relationships with outside counsel firms by holding them to a standard that is too high. History, however, shows that such interactions, when combined with effective communication, actually help to improve the relationship with the firm by giving them more feedback on how best to partner with their client. So the question is: how do you get from collected data to improved operations and law firm relationships? Most claims organizations are already on their way, even if they don’t realize it.
On the Road to Innovation
Insurance carriers have spent the better part of two decades implementing claims and spend management systems that have improved efficiency and predictability. These systems have also generated a large amount of data, but many organizations are unsure of how to operationalize this information for tangible results and even greater benefits. This is because information on its own isn’t necessarily useful. The real value lies not in data itself but in insight, which is constructive and supports sound decision-making.
Insight is the result of asking the right questions and analyzing data to find the answers. For example, a report that showed a list of cases, assigned law firms, the length of time they were open, and spend on each would certainly represent a great deal of data, but it isn’t particularly useful on its own to a claim manager deciding on a case assignment. However, if that report broke down average cycle time, costs, and outcomes by law firm for cases similar to the one at hand, that insight would help to determine a beneficial case assignment.
Making It Easy
It is not enough for data insights to be available on an isolated software screen that the user needs to go out of their way to find. It is important for insight to be delivered to users where they need it within their existing workflows, integrated seamlessly with normal actions. This can be done at any stage in the claims process, as illustrated by the following suggested best practices for leveraging AI to help claims professionals in their day-to-day tasks.
Case Opening: Early case assessment and predictive analytics provide insights into cycle time, average total costs, and jurisdictional success to aid in case and budget planning. Using a combination of the client’s data and benchmarking information, a firm, attorney, rate and case plan is recommended.
Budgets and Invoicing: When an outside counsel firm submits a budget, the user should be presented with average budgets and budget exceptions by firm for similar case types. Further into the case, invoices submitted to the carrier should be adjusted based on AI technology that goes beyond a simple rule-based engine. An intelligent machine learning process can look for proper actions, descriptions and codes based on case type, jurisdiction, previous history, and industry data.
Case Closure: The case manager should always record final critical information, including subjective firm/attorney ratings, for integration into decision-making on future cases. Reports for the closed case can be automatically generated and recorded, providing deltas from early case expectations to measure real against expected results and provide tools for future refinement.
Getting to these process goals is not a sprint. Rather, we are running an ultra-marathon and will need to take a long series of small but important steps to build capabilities and continue to deliver real, actionable insights through analytics. To do this, insurance claims specialists need to combine their expertise with the analysis abilities of AI, as well as other innovative technologies that increase value. The end result will be happier clients and better business results for carriers.
To learn more about the role of AI in claims litigation, download our whitepaper.