Claims triage is the process of sorting high volumes of claims by urgency. In case of catastrophic events or simply during peak season when insurers are experiencing large claim intakes, they need to quickly and confidently identify which claims need to be resolved first and by whom.

In typical situations, damage estimation is handled manually in a vehicle repair shop and/or by an adjuster arriving at the accident site. It takes days if not weeks to receive a report from an expert, which in turn has to be inspected by an insurance provider for mistakes or unfair payouts. Instead, the machine learning model can compare the smartphone image to its vast database of car damage pictures to determine the severity and estimate the cost of the repairs.

Claim adjudication refers to the process of paying or denying insurance claims by reviewing the claim’s correctness and validity. Typically done by the insurance company staff, adjudication includes the long list of checks: for errors or omissions, for the appropriate diagnosis or procedures, for the correct insurance policy, etc. Since many of these checks don’t require human intervention building Machine learning model helps you to achieve this with high accuracy

Insurers estimate that around 18 percent of all claims are fraudulent. Fake accidents and vehicle thefts along with phantom medical procedures are some of the most common types of claim fraud. Although you can create rules to make checks for well-known indicators of fraud, you will have nothing to combat newly appearing schemes or fraud patterns unnoticed by human analysts. A machine learning algorithm will analyze FNOLs(First Notice Of Loss), police reports, medical bills, forms filled by clients and insurance agents, will find correlations that may be missed, and will do this instantly, in real time. And when a claim is flagged as potentially fraudulent, depending on the probability, the payout can be automatically denied, or human agents can investigate the claim further.