"Adjusting" Claim Processing

Claims handling is evolving rapidly and we’re facing the reality that many seasoned adjusters are retiring, taking decades of expertise with them. While we’re hiring more trainees to fill these gaps, it’s likely that this new group will take longer to reach the same experience levels as their predecessors. Without seasoned colleagues to consult, trainees lack the insights that once came from round-table conversations and quick chats. We need a strategy to capture that institutional knowledge and pass it along to our fresh recruits.

Fortunately, past claims decisions can be broken down into key data points. With the help of artificial intelligence, these data points can now be transformed into valuable tools for adjusters. By mining this information, we can create user-friendly reports and dashboards that help staff prioritize their work more effectively.

Our approach starts by sorting claims into three buckets: those that need “little touch,” “no touch,” or direct human involvement. After setting these strategic thresholds, we turn our attention to data gathering. This should begin with the underwriting process, capturing information from the first notice of loss and continue through whatever systems carriers are using. Carriers can also run one-time reviews of their past decisions to help populate these data sets.

The more historical data we have, the greater our advantage. Looking back in time allows us to mine information that can feed predictive tools for future claims. Connecting with external sources—like social media, municipal databases and prior carriers’ claims histories— can sharpen our decision-making even further.

Automation delivers the most costefficient claims handling, but the human touch remains vital for customer communication, negotiation and complex cases.

Programming these data sets to pinpoint statistically significant variables is essential. By analyzing historical outcomes, whether good, bad, simple or complex, we can spot common factors. When these factors show up in new claims, we can use them as probable predictors, helping carriers route claims appropriately—whether that means automated processing, further investigation or paying out with minimal customer interaction. This strategy can be tailored to each carrier’s risk appetite, service standards and internal protocols.

Not all significant factors are directly tied to the claim itself; some are customer-specific data points that serve as guides rather than hard-and-fast rules. While factual data eliminates bias and discrimination, we must preserve the human element through internal controls. Periodic reviews of decisions ensure accuracy and keep us aligned with current trends—like double-checking that we’re not paying for treatment from a doctor whose license has lapsed.

Though investing in predictive tools may require upfront costs, the payoff comes quickly. Automation delivers the most cost-efficient claims handling, but the human touch remains vital for customer communication, negotiation and complex cases. Automated systems are scalable, consistent and efficient—they don’t need benefits, raises, bonuses or time off.

Behind the scenes, straight-through processing identifies claims that can be resolved quickly, boosting customer satisfaction and retention. The goal is for our new staff to benefit from the cumulative wisdom of retiring senior adjusters, focusing only on claims that truly require their expertise and learning how to prioritize them effectively.