Efficiency Dividend is the value unlocked when AI removes manual intake and data-gathering tasks, allowing expert capacity to be redeployed toward faster decisions, higher submission throughput, and more consistent risk evaluation.
A typical underwriting workflow in practice
A closer look at the underwriting workflow reveals where time is actually spent by experienced underwriters—and where the gaps of inefficiency are formed in the process.
For Sarah, a senior P&C underwriter, each submission triggers a structured but effort-intensive workflow. She consolidates data across multiple documents like ACORD forms, loss runs, inspection reports, and financials; validates completeness through follow-ups; and enriches inputs using external risk and market data.
Then, she triangulates additional inputs from catastrophe models, property records, credit data, and industry benchmarks to assemble a complete risk view. By the time a coherent risk profile is established, a significant portion of her effort has been expended—leaving the final crucial steps of judgment, appetite alignment, and pricing to a fraction of her day.
Due to fragmentation and sheer data/document volumes, underwriting workflows structured this way create friction across the insurance value chain.
- Employees spend a major part of their workday on non-value-adding tasks.
- Customers often wait days for quotes.
- Insurers are unable to scale their topline and consistently suffer productivity loss.
Rewrite the Underwriting Function with AI
At Birlasoft, we consistently see this pattern across P&C carriers—where significant underwriting time is absorbed by traditional workflows that are now well within the scope of AI-driven automation. As a result, the underwriting efficiency dividend is increasingly accessible to insurers through production-grade solutions.
Modern underwriting platforms combine machine learning, large language models, and agentic orchestration to re-architect how submissions are processed.
- Incoming documents are automatically ingested, classified, and parsed using document intelligence models.
- Key risk attributes are extracted, normalized, and mapped directly into core underwriting systems, eliminating manual rekeying.
- Validation is handled in-line: AI agents flag missing fields, detect inconsistencies across documents, and trigger targeted follow-ups with brokers.
- External data sources, like catastrophe models, geospatial property data, credit signals, and industry benchmarks, are programmatically integrated to enrich the submission.
Now, underwriters see continuously assembled, decision-ready risk profiles. Data no longer needs to be assembled, which enables underwriters to handle more submissions while focusing on client engagement and risk pricing.
Insurance at the Speed of Business: Key Benefits of AI in Underwriting
By rearchitecting the underwriting workflow with AI, insurers are able to realize the underwriting efficiency dividend: additional, usable underwriting capacity, along with highly coveted outcomes in the underwriting function:
- Higher submission throughput and lower cycle times without additional headcount.
- More consistent, data-driven portfolio outcomes through standardized and enriched risk views.
- Stronger client engagement increases retention and customer lifetime value (CLV).