AI Agents

Context

Built for insurance within the Claims Processing stack and owned by Claims / SIU, this agent runs from FNOL through to settlement, generating a live “fraud risk signal” for each claim. Its role is not to make the final fraud decision, but to consistently highlight suspicious or exaggerated cases early, so they can be handled differently from straight-through, low-risk claims.

What it does

The agent ingests claim information at FNOL and as the file evolves—loss details, policy data, claimant history, supporting documents, payments, and simple behavioural signals such as timing and channel. Using anomaly detection and graph-based relationships, it scores each claim for fraud or exaggeration risk and updates that score as new information arrives. Claims with typical patterns remain in the normal flow; those with unusual combinations of attributes, connections, or document patterns are flagged with a clear “high-/medium-risk” signal and basic reasons (for example, unusual linkages to other claims or entities, inconsistent narratives, or patterns often seen in exaggerated losses). SIU and senior handlers then decide how to act on those signals; the agent’s job is to surface them reliably.

Core AI functions

The core capability is anomaly detection plus graph analysis: the agent learns what normal claims look like for a given product and channel, detects outliers in that space, and uses graph techniques to spot unusual links between claimants, policies, addresses, devices, and other entities. These two views—“this looks unlike standard claims” and “this sits in a suspicious network”—are combined into a simple risk indicator that can be consumed by standard claims workflows, not just by data specialists.

Problem solved

Without this agent, fraud and exaggeration detection relies heavily on manual red flags and individual experience. Some suspicious claims are spotted late or not at all, while other ordinary claims are escalated unnecessarily, tying up SIU time. Rules-only approaches quickly become brittle as fraud patterns evolve. By providing a continuously updated, data-driven risk signal from FNOL onwards, the agent gives Claims and SIU a more consistent way to see where extra scrutiny is warranted.

Business impact

The primary impact is lower loss ratios through more targeted SIU review. High-risk claims are identified earlier and with better context, so investigative effort can focus on the small subset of files most likely to be fraudulent or exaggerated, rather than broad manual screening. At the same time, low-risk claims can move faster, improving customer experience and reducing handling cost. Over time, this leads to more effective fraud containment and a clearer audit trail for why certain claims were escalated.

Integration and adjacent use cases

Integration complexity is medium: the agent needs access to claim and policy data across the lifecycle, basic customer and entity references to build its graphs, and a way to store and surface risk scores inside the claims system so handlers and SIU can see and act on them.

Common combinations in this stack:

• FNOL Intake & Triaging Agent to capture First Notice of Loss cleanly and attach an initial fraud-risk view at the very start;

• Claims Document Completeness Checker to ensure the required documents and photos are present so the fraud signal is built on a complete file;

• Policy Terms & Coverage Validator (Claims) to align fraud review with how coverage applies to the reported loss;

• Damage Evidence & Estimate Extractor to compare extracted damage and cost patterns with typical claims and support the anomaly view; and

• Eligibility & Threshold Checker (Claims) to ensure that any payments on high-risk claims are checked carefully against deductibles, sub-limits, and internal approval thresholds before funds are released.