Insurance Anti‑Fraud Risk Control System: Architecture, Core Capabilities, and Case Studies

Introduction

The insurance industry faces a growing fraud problem, with leakage rates reaching 20%. Fraud manifests in diverse, professionalized, and organized forms, creating three main challenges: difficulty in detection, confirmation, and recovery.

1. Industry Pain Points and Common Cases

Detection difficulty: Fraud becomes harder to spot as black‑market tactics diversify.

Confirmation difficulty: Traditional finance cannot block risky cases at the model level, requiring richer profiles to assist staff and improve model explainability.

Recovery difficulty: Many fraudulent claims are discovered only after settlement, and statutory limits complicate recovery.

High‑risk areas include car insurance (organized collusion, staged accidents, counterfeit repairs) and health insurance (agency‑hospital collusion, false claims, over‑treatment).

2. Core Anti‑Fraud Capability Construction

The solution consists of three pillars:

Fraud Identification Engine: Leveraging big data, machine learning, and AI algorithms to detect fraudulent patterns.

Risk Scoring, Profiling, and Alerts: Transforming engine outputs into scores, risk portraits, and actionable alerts for business staff.

In‑Process Identification & Disposal: Embedding the engine into the entire claim workflow to achieve early detection, confirmation, and recovery.

3. Anti‑Fraud Stories

Two case studies illustrate the approach:

Car Insurance: A graph‑based engine uncovered a fraud ring involving 199 cases across nine insurers by linking multiple claims to the same driver, vehicles, and locations.

Health Insurance: Graph analysis identified agency‑hospital collusion and fraudulent claim patterns such as repeated disease categories and inflated expenses.

4. Platform Blueprint

The platform is organized into four layers:

Data Foundation: Integration of life, property, pension, group, and external industry data.

Capability Layer: Digitalization – building risk‑profile tags and knowledge graphs, ingesting IoT and wearable data. Modeling – big‑data analytics and predictive models for pricing, underwriting, operations, and claims. Intelligence – image classification, duplicate‑claim detection, PS‑based forgery detection, voice emotion and voiceprint recognition.

Platform Layer: Four core modules – health‑risk engine, car‑risk engine, property‑risk engine, and an intelligent risk‑control platform covering pricing, underwriting, claim prediction, early warning, in‑process leakage prevention, and post‑claim risk graphs.

Product Layer: Services such as an underwriting assistant, IoT risk manager, and claim‑distribution tools that empower business staff rather than merely blocking risk.

5. Car‑Insurance Risk Identification Engine

Key steps include:

Scoring cards built on big‑data and machine‑learning to triage cases.

Guided field investigation with risk prompts and evidence‑collection instructions.

Graph‑based association of phone numbers, vehicles, drivers, repair shops, etc., to uncover organized fraud.

Use of metric‑learning for face and image matching, semantic segmentation for environment analysis, and vector‑based retrieval for similarity search.

Integration of external data (e.g., credit‑insurance data, telematics) and a radar‑chart risk score presentation.

6. Health‑Insurance Anti‑Fraud Engine

Construction focuses on multi‑dimensional profiling (customer, policy, historical, disease, agent) and a scoring‑card model trained on historical denial cases. The engine combines big‑data, OCR for document tampering detection, and image‑forgery detection to surface risky patterns such as over‑treatment, duplicate claims, and fraudulent billing.

7. Deep Integration with Business Processes

Risk scores and alerts are embedded throughout the lifecycle: renewal underwriting, claim intake (voice emotion and voiceprint checks), on‑site inspection (image‑based alerts), damage assessment (environmental photo analysis), and post‑claim quality review via knowledge graphs.

Conclusion

The presented framework demonstrates how AI, big data, and graph technologies can be woven into insurance operations to assist staff in identifying and managing fraud rather than relying on outright automated blocking.