AI Integration for Sapiens Fraud Detection

The integration connects at two primary layers within the Sapiens ecosystem: the claims intake/processing engine and the dedicated fraud management module. At FNOL, an AI service is triggered via API to score the initial loss report in real-time, analyzing narrative text, claimant history, and linked policy data for patterns indicative of soft fraud or buildup. This score is written back to the claim file as a custom field, allowing Sapiens' native rules engine to route high-risk claims automatically for enhanced validation or special investigation unit (SIU) assignment. For ongoing monitoring, a separate batch process analyzes newly linked entities (repair shops, medical providers, witnesses) across the entire book of business, flagging networks and outliers that may not trigger traditional rule-based alerts.

Implementation centers on Sapiens' extensible API framework and event hooks. A lightweight middleware orchestrator handles the call to the fraud detection model—hosted either as a cloud service or on-premises container—passing a structured payload of claim data. The model returns a probability score, key risk factors (e.g., 'inconsistent injury timeline', 'known provider association'), and a confidence level. This output is not only stored but also used to generate a prioritized alert within the investigator's workspace, pre-populated with the AI's reasoning and linked to the relevant evidence. This allows investigators to start with context, not a blank slate, focusing their effort on validation and action rather than data assembly.

Rollout is phased, beginning with a pilot on a specific line of business (e.g., auto physical damage) to tune model thresholds and integrate feedback loops. Governance is critical: all AI-scored claims and overrides are logged in an audit trail, and model performance is continuously monitored for drift against actual fraud outcomes. The system is designed for a human-in-the-loop; the AI prioritizes and enriches, but the investigator makes the final determination, ensuring compliance and maintaining accountability. This architecture doesn't replace Sapiens' fraud tools but augments them, creating a layered defense that adapts to new schemes faster than rules alone can be updated.

The integration connects to Sapiens' fraud detection modules via its RESTful APIs and event-driven architecture. At First Notice of Loss (FNOL), claim data is automatically enriched and sent to an AI scoring service. This service evaluates the submission against a multi-model ensemble—analyzing patterns in claimant history, policy details, loss description, and linked entities—to generate a real-time fraud probability score. The score and key risk indicators are immediately written back to the claim's fraud module record, triggering automated workflow rules for assignment and prioritization.

For continuous monitoring, a background process polls Sapiens for new activities, payments, or document uploads on open claims. This data is streamed to the AI service for re-scoring, detecting emerging patterns like linked claimant networks or inconsistencies in supplemental documentation. High-confidence alerts are automatically created in the investigator's queue with a pre-populated summary, highlighting the changed risk factors and suggested next steps (e.g., "Request additional proof of loss for Item X"). All AI inferences are logged with a full audit trail, including the model version, input features, and confidence scores, stored in a dedicated audit object linked to the claim.

Rollout follows a phased, human-in-the-loop approach. Initially, AI scores are presented as recommendations alongside existing rule-based flags, allowing investigators to compare and provide feedback. This feedback loop is used to calibrate model thresholds. Governance is maintained through a centralized prompt and model management layer, ensuring all AI outputs are grounded in the insurer's specific guidelines and regulatory requirements. The system is designed for explainability, allowing investigators to click into any alert to see the primary contributing factors (e.g., "Unusual frequency of claims from this postal code").

Integrate AI scoring as a secure, audited service call from within Sapiens' fraud detection modules. The AI service should be invoked via a dedicated API layer, passing only the necessary claim context (e.g., claim ID, FNOL data, linked party IDs) from Sapiens' Claim and FraudCase objects. All inferences must be logged back to a dedicated audit table within the Sapiens database, linking the model's score, version, key reasoning factors, and timestamp to the specific claim record for full traceability. This ensures every AI-influenced decision is explainable and can be re-evaluated.

Rollout should follow a phased, human-in-the-loop approach. Phase 1: Shadow Mode – Run the AI model in parallel with existing rules, logging scores without affecting workflows, allowing investigators to compare AI alerts against known outcomes. Phase 2: Assisted Prioritization – Surface AI scores and top risk factors within the investigator's Sapiens workspace, using a visual indicator to highlight high-probability cases without auto-routing. Phase 3: Conditional Automation – Configure Sapiens' workflow engine to automatically elevate claims exceeding a high-confidence AI fraud threshold into a dedicated review queue, while medium-scoring claims require manual investigator sign-off before escalation.

Governance is critical. Establish a Model Review Board with members from Claims, Special Investigations, IT, and Compliance. This board approves model updates, reviews false positive/negative rates, and adjusts scoring thresholds based on operational feedback. Access to the AI service and its configuration should be controlled via Sapiens' existing Role-Based Access Control (RBAC), ensuring only authorized fraud analysts and system administrators can modify integration parameters or view raw model outputs.