Below each use case, we follow a uniform structure: Problem → Agentic/automation solution → Expected impact & KPIs → Implementation considerations & references.
Claims triage & near-instant settlement
Problem: High volumes of low-severity claims occupy adjudicators; manual triage creates time delays and variable customer experience.
Solution: An agentic claims pipeline that ingests claim intake (image, video, structured form), performs automated document extraction + damage estimation, runs fraud checks (ML risk score), applies business rules, and either issues immediate payment or escalates to a human adjuster. The agent records an auditable trail and updates policy/admin ledgers automatically. Lemonade is a pragmatic example of an insurer that uses such automation to process a large share of simple claims automatically.
Expected impact / KPIs
- % of claims auto-settled; average time-to-settle (seconds → minutes), claimant NPS.
- Operational cost per claim; human adjuster throughput.
- Fraud detection precision/recall and false positive rate.
Implementation considerations
- Integrate image/video analysis (structured sensors), robust OCR/NLP for documents, and a rules engine for business guardrails.
- Maintain human-in-the-loop thresholds for high-value or ambiguous claims.
- Log every agent decision for auditability.
Underwriting, risk selection & dynamic pricing
Problem: Traditional pricing cycles are monthly/quarterly because data refreshes, model refits, and approval loops are slow. This limits responsiveness to emerging risk patterns (e.g., sudden rise in local accident frequency).
Solution: A real-time actuarial cockpit combines streaming exposure data (policies written, telematics, third-party feeds), GLM/ML scoring pipelines, and agentic agents that alert or propose dynamic relativity updates to pricing teams (or automatically adjust non-regulated components within governance rules). The architecture blends credibility weighting (company vs. industry data) with real-time experience monitoring and scenario testing.
Expected impact / KPIs
- Time to reprice segments; drift in modeled vs. observed loss ratio; pricing accuracy (loss ratio convergence).
- Reduction in manual rate filing prep time; velocity of assumption updates.
Implementation considerations
- Keep a conservative change control and regulator / actuarial sign-off for pricing rules in tariffed areas.
- Use AB testing on new relativities where allowed.
- Ensure feature engineering pipelines are explainable for regulatory review.
Reserving, IFRS 17 & finance-actuarial convergence
Problem: IFRS 17 created a step-change in the granularity and frequency of actuarial outputs required by finance and regulators. Many firms still perform heavy manual reconciliations between actuarial models and the ledgers.
Solution: An automated valuation pipeline: standardized data model (policy, cashflows, claims triangles), scheduled model runs (net of reinsurance), automatic reconciliations, and agentic orchestration that prepares disclosures and runs “what-if” stress scenarios on demand for management. This reduces BAU cycle time, improves auditability, and frees actuaries for forward-looking interpretation. Large consultancies and implementers emphasize automation as a core outcome of IFRS 17 programs.
Expected impact / KPIs
- Run time for valuations (hours → minutes), reconciliation error rate, control exceptions, and audit findings.
- Frequency of management insights (e.g., monthly IFRS 17 insight packs rather than quarterly).
Implementation considerations
- Strong data lineage and GL control.
- Formal model governance, versioning, and independent validation processes.
- Scalable compute (cloud) for frequent runs.
Fraud detection & investigations
Problem: Fraud evolves; static rules produce many false positives; manual review is expensive.
Solution: Agentic agents combine ML anomaly detection, network analysis (link detection across claims, social media, third-party registries), and dynamic scoring to prioritize high-risk cases for human investigators. Agents can also assemble evidence packages (documents, metadata, prior claims) to accelerate investigations.
Expected impact / KPIs
- Reduction in undetected fraud; investigator time saved; speed to referral for special investigations.
Implementation considerations
- Privacy and lawful data use; explainability for model outputs; chain-of-custody for evidence assembled by agents.
Policy administration & customer servicing automation
Problem: Renewals, mid-term adjustments, and customer queries generate repetitive tasks across admin and finance.
Solution: Agents handle intake, cross-check policy data, propose mid-term price adjustments (where allowed), auto-issue endorsements, and respond to natural language queries while escalating complex cases. This preserves the human workforce for exception handling and strategy. Lemonade and other digital insurers have illustrated how integrated automation improves conversion and NPS.
Expected impact / KPIs
- Reduction in manual endorsements, first-contact resolution, average handling time, and customer satisfaction.
Implementation considerations
- Integration with CRM, policy admin systems, and compliance rules; clear audit trail of agent actions.
