Major Life Insurance Group (Asia-Pacific)

A major life insurance group operating across the Asia-Pacific region was struggling with the operational weight of fully manual underwriting and claims processing workflows. Each claim required human reviewers to gather, validate, and assess documentation across multiple systems — a process that routinely took up to two business days per case. Beyond the time cost, the manual approach created inconsistent outcomes, elevated error rates, and poor visibility into processing backlogs. For a high-volume insurer, these delays translated directly into customer dissatisfaction, competitive disadvantage, and significant operational expenditure. A large reservoir of historical claims and underwriting data remained untapped, offering no predictive value under the existing paper-based model.

FPT Software deployed Confidon, an AI-powered insurance automation platform built around predictive machine learning models trained on the insurer's historical underwriting and claims data. The platform was integrated with existing cloud infrastructure and complemented by data analytics tooling to create an end-to-end automated workflow. Confidon's predictive ML models analyze incoming claim documentation in real time — extracting, classifying, and validating information that previously required manual review. The underwriting module applies similar logic to assess risk profiles against policy criteria without human intervention. Cloud deployment enabled scalable processing capacity and ensured the system could handle peak submission volumes without degradation. The implementation connected directly with the insurer's core policy and claims management systems, eliminating manual handoffs between departments.

The transformation delivered outcomes that redefined operational benchmarks for the insurer:

• Claim processing time: reduced from 2 days to 2 seconds — a reduction of over 99.99%
• Claims processing accuracy: 99%, meeting or exceeding the quality of manual review
• Underwriting processing time: accelerated to 2 seconds per case

Beyond speed, the consistency introduced by automated decision logic reduced adjudication variability across the portfolio. Freed from high-volume routine processing, operations staff could redirect effort toward complex cases and customer-facing activities. The insurer also reported increased revenue, attributed to faster policy issuance and improved straight-through processing rates.

• AI automation in insurance claims and underwriting is viable at enterprise scale — the 99% accuracy rate demonstrates that predictive ML can meet the quality bar previously held by human reviewers.
• Historical claims data is a strategic asset; insurers with mature data environments are better positioned to realize immediate value from ML-based automation.
• End-to-end workflow automation delivers compounding returns: speed improvements reduce cost, consistency reduces rework, and faster issuance creates revenue lift.
• Cloud-native deployment is essential for handling variable submission volumes without manual capacity planning.
• Integrating AI with existing core systems — rather than replacing them — reduces implementation risk and accelerates time to production value.