Automated underwriting has come a long way in the last 25 years. It may be surprising that there was automated underwriting 25 years ago. At that time, it was called ‘expert’ underwriting. The idea was right, but the timing was wrong. The underwriting engines were black box algorithms; there was no user interface; data was fed from a file to the system; programming was required to write rules; and specialized hardware was necessary to run the systems. Not surprisingly, this attempt at automating underwriting was dead on arrival.
The next major iteration occurred about ten years later. Automated underwriting systems included a user interface; rules were exposed (some programming was still required to change the rules); data interfaces were introduced to collect evidence from labs and the medical inquiry board; underwriting decisions could be overridden by the human underwriter; and workflow was provided. Some insurers chose to take a chance on this new technology, but it was not widely adopted. There were two strikes against it: cost and trust. The systems were expensive to purchase, and the time and costs involved in integrating and tailoring the systems to a specific company’s underwriting practice could not be outweighed by the benefits. The lack of benefits was partially because the underwriters did not trust the results. Many times this caused double work for the underwriters. The underwriters reviewed the automated underwriting results and then evaluated the case using manual procedures to ensure the automated risk class matched the manual results.
Moving ahead fifteen years to today, changes in the underwriting environment place greater demands on staff and management. Staff members are working from home, and contractors are floating in and out of the landscape, all while reinsurers are knocking on the insurer’s door. There are now state-of-the-art new business and underwriting (NBUW) systems that address the challenges associated with the new demands. The solutions do not just assess the risk but provide workflow, audit, and analytics capabilities that aid in the management process. Rules can be added and modified by the business users; evidence is provided as data so that the rules engine can evaluate the results and provide the exceptions for human review. Subjective manual random audits of hundreds of cases evolve into objective, data-driven perspectives from thousands of cases. Analytics provide insights on specific conditions and impairments over the spectrum of underwritten cases to provide a portfolio view of risk management. Underwriting inconsistencies become easy to find and specific training can be provided to improve quality.
In our report, Underwriting Investments that Pay Off, Karen Monks and I found that the differences between insurers who are minimally automated and those that are moderately to highly automated are substantial. For minimally automated insurers, the not in good order (NIGO) rates are four times higher, the cycle times are 30% longer, and the case manager to underwriter ratio is almost double compared to the metrics for the moderately to highly automated insurers. This outcome may not reflect your specific circumstances, but it is worth preparing a business case to understand the benefits. With the advances in the systems and the advantages provided for new business acquistion, there are few justifications for any company not to seek greater automation in their underwriting.
