Fighting Insurance Fraud in India with Technology

Insurance fraud in India is changing at a fast pace, with fraudsters taking advantage of digital loopholes to manipulate policy and claims information. From ghost policies to faked accidents and misrepresented vehicle information, the sector is exposed to serious threats. In this interview, Prajakt Deolasee, Turtlemint's Chief Technology Officer, illuminates the most common fraud methods and how advanced technologies—AI-based behavioral analysis, Vahan integration, QR code authentication, and Robotic Process Automation (RPA)—are fortifying fraud prevention. Here's how Turtlemint is leading the fight against insurance fraud.

What's your perspective on the current landscape of insurance fraud in India? What are the most common types you've observed?

Insurance fraud in India is evolving rapidly with digitalization, creating new challenges for both intermediaries and consumers. We're seeing a rise in identity and document fraud, including fake policies issued with forged details or misrepresented information, sometimes resulting in "ghost policies." On the consumer side, claims manipulation is increasingly common, ranging from inflated health claims to staged motor accidents and unnecessary medical procedures aimed at maximizing payouts.  

How do you leverage advanced fraud detection tools and consumer-focused solutions?

We use a range of advanced tools to combat fraud. For motor insurance, Vahan integration is crucial, ensuring vehicle details match policy information and preventing misrepresentation. This authenticates the information provided by agents and consumers against the Regional Transport Office (RTO) database. We also use QR code scanning on policy documents for easy authenticity verification, preventing payments on fake policies. Robotic Process Automation (RPA) further streamlines processes and reduces opportunities for fraud.  

How do your fraud detection systems adapt to the evolving tactics of fraudsters?

Fraudsters are constantly finding new loopholes, so our systems are designed for continuous improvement. We employ behavioral analysis, real-time monitoring, and geographical risk mapping. We analyze emerging fraud patterns using Management Information System (MIS) reports and escalate suspicious cases for further investigation. By continuously learning from flagged cases, we refine our risk markers and improve our detection algorithms to stay ahead of evolving fraud tactics.

You mentioned validation tools and Vahan integration for detecting mismatched motor insurance policies. Can you elaborate on how these work?

Vahan integration is key to preventing misrepresentation in motor insurance. When a policy is issued, our system automatically retrieves vehicle details like make and model from RTO records. This prevents fraudsters from insuring high-value vehicles under lower-premium categories (e.g., classifying a car as a two-wheeler). Our discrepancy detection systems flag any mismatches between the policy details and official RTO data, preventing fraudulent policy issuance.

How do real-time geographical and behavioral analysis contribute to motor insurance fraud detection?

Fraud patterns often have geographical hotspots. Our technology analyzes historical fraud trends by location, identifying high-risk regions for policy misrepresentation or claim fraud. For example, we've identified Gujarat, Delhi NCR, and Odisha as high-risk zones based on past activity. Behavioral analytics helps us detect unusual agent or applicant activity, such as frequent policy cancellations or suspiciously high-value claims from a specific region.

How do measures like risk markers, income checks, and video screening help prevent false life insurance claims?

In life insurance, we use internal risk markers tailored to identify potential mortality risks. These markers are powered by advanced analytics, enabling early intervention in the claims process. Income verification checks ensure financial information aligns with other data sources. Video screenings provide visual confirmation of claimants' circumstances, further enhancing the accuracy of fraud detection.

With increased EV adoption, how does automated data integration help flag policy misrepresentation in this segment?

Automated data integration, similar to the process used with traditional vehicles, helps flag discrepancies between the declared vehicle specifications and the actual specifications registered with the relevant authorities. This helps prevent misrepresentation related to battery capacity, charging capabilities, and other EV-specific features.

Have you observed unique fraud patterns specific to EV insurance compared to traditional motor insurance?

Yes. One common tactic is underreporting battery value, as EV batteries can represent a significant portion (up to 40%) of the vehicle's cost. We see instances where policyholders attempt to misrepresent their vehicle's charging capabilities or battery status to secure lower premiums. 

How does QR code scanning for policy authenticity improve transparency and reduce fraud?

QR code scanning, now mandated for all motor insurance policy documents, allows both consumers and agents to quickly verify policy legitimacy, drastically reducing the risk of fake documents. The combination of QR code scans and manual checks has significantly reduced fraud.  

What role do data privacy and compliance play in deploying these tech-driven solutions?

Data privacy and compliance are paramount at Turtlemint. Our fraud detection systems are built on secure cloud infrastructure, ensuring policyholder verification processes don't compromise sensitive data. We handle all consumer data securely and ethically, adhering strictly to regulations like GDPR and India's IT Act.