The auto insurance industry has endured a turbulent decade, with underwriting losses in recent years. Inflation, rising claims severity, and supply chain disruptions have compounded the pressure.

Outsourcing in insurance isn’t new. For decades, carriers and agencies have relied on BPOs to handle data-intensive, time-consuming back-office tasks, such as submissions, renewals, and document processing.

Risk analysis has long relied on human expertise—underwriters parsing reports, interpreting inspections, and weighing risks based on experience. While invaluable, this approach struggles under today’s demands.

Insurance carriers are rethinking traditional risk models in response to today’s dynamic and data-intensive environment. Catastrophic weather patterns, cyber volatility, and evolving behavioral risk signals demand real-time, high-fidelity analysis. Yet most insurers are constrained by fragmented data architectures, legacy scoring models, and the sheer cost of building and maintaining in-house AI infrastructure.