Motor Insurance Sector’s Susceptibility to Fraud

• How rampant is Vehicle Insurance Fraud?
• How is it impacting the Insurer and the Insured?
• Can we plug this leakage with technology?

Background

Fraud is becoming rampant in General Insurance; Motor Insurance contributes to a large part of it along with Health insurance, given the various loop holes. Wikipedia defines Insurance Fraud as “Any act committed to defraud an insurance process. It occurs when a claimant attempts to obtain some benefit or advantage they are not entitled to, or when an insurer knowingly denies some benefit that is due.”

As per the latest Deloitte Insurance Fraud Survey conducted in the second quarter of FY2023, “About 60 percent of survey respondents believe that there has been a significant rise in fraud, while further 10 percent experienced a marginal increase.” Also, as per a report dated Feb 16th 2023 by ETBFSI, “insurance frauds cost insurance companies approximately USD 6 billion annually and insurers lose close to 10 percent of their overall premium collection to frauds.”

All of this evidently echo one single priority at the moment and that is mitigation of frauds. Technology has been one of the reasons for this burgeoning insurance frauds, hence detection and control of frauds cannot happen in silos, but with the adoption of advanced technologies like artificial intelligence and machine learning.

Motor vehicles are bound by The Motor Vehicles Act, 1988, that mandates that all motor vehicles have to be insured against third party risk, underscoring the huge responsibility of insurance companies, thereby leaving no motor vehicles from their ambit.

What are the different forms of fraudulent activities related to motor insurance policies and claims?

?Staged Accidents: Fraudsters intentionally cause accidents, often involving innocent parties, to make fraudulent insurance claims for vehicle damage, injuries, or both.

Fraudsters may damage their own vehicles and then file claims for the repairs or total loss, sometimes inflating the cost of damages or misrepresenting the cause of damage.

A policyholder might also falsely report a hit-and-run accident to avoid increased premiums or other consequences of an at-fault accident.

Individuals sometimes claim to have been involved in an accident with a “phantom” vehicle that doesn't exist or was not actually involved in the accident and create a fabricated scenario.

Exaggerated or False Injury Claims: ?Some individuals or medical providers may exaggerate the extent of their injuries or even fabricate injuries altogether to claim compensation for medical expenses.

Ghost Brokers: Scammers pose as insurance agents or brokers, offering fake insurance policies to unsuspecting consumers. These policies are often invalid, leaving policyholders without coverage.

Fronting and False Information: Parents may purchase insurance policies for their children but list themselves as the primary drivers to secure lower premiums. This misrepresentation is illegal and constitutes fraud.

Providing false information on insurance applications, such as misrepresenting the vehicle's usage or location, can lead to lower premiums.

Policy Lapsing Fraud: Some individuals stop paying premiums but continue to use their vehicles, misrepresenting their insured status. They may then file claims for accidents or damages that occurred during the lapse in coverage.

Upcoding: Repair shops may inflate the cost of repairs or parts, charging insurers more than what is necessary for the actual repair of the vehicle.

How can technology ease this fraud burden?

Artificial Intelligence (AI) plays a significant and evolving role in detecting and preventing frauds in the motor insurance industry. These advanced tools are used by insurers for Risk assessment, Underwriting, Claims processing, Fraud detection and Customer service to expedite and streamline processes.

Risk Assessment, Mitigation and Pricing: AI algorithms analyse vast amounts of data to identify patterns and anomalies, such as driver behaviour, vehicle specifications, location data, and historical accident records, to assess the risk associated with insuring a particular motorist.

This allows insurers to take preventive measures and reduce potential losses and offer more accurate and personalized premium rates based on individual risk profiles. For example, they can detect multiple claims for the same incident or identify irregularities in claim submissions.

Underwriting: AI automates the underwriting process by quickly evaluating policy applications and determining eligibility based on predefined criteria. This hastens up the issuance of policies and reduces the chances of human error to a great extent.

Customer profiling is done in an efficient way as AI analyzes customer data to create detailed profiles, enabling insurers to offer personalized recommendations, discounts, and policy options that better match the specific needs and preferences of individual policyholders.

Claims Processing: AI-powered chatbots and virtual assistants handle first-level claims inquiries and guide policyholders through the claims reporting process with instant responses.

AI can assess the validity of claims by analyzing police reports, medical records, or repair shop invoices, accident photos, and other relevant data, expediting the claims settlement process.

AI-based anomaly detection models can automatically flag claims or policyholder behaviors that deviate from the norm and assign a fraud likelihood score. This can include unusual claims amounts, atypical claim submission patterns, or discrepancies in policyholder information.

Fraud Detection: AI algorithms can detect patterns indicative of insurance fraud by analyzing claims data, policyholder behavior, and historical fraud cases, that may not be apparent to human underwriters. This helps insurers identify and investigate potentially fraudulent claims more efficiently.

AI models, along with machine learning can continuously learn from new data and adapt to evolving fraud tactics and forecast trends and identify emerging risks, helping insurers adapt their strategies and pricing models accordingly.

Telematics: AI-powered telematics devices installed in vehicles collect real-time data on driving habits, such as speed, braking, and acceleration.

This is slowly catching up and insurers use this data to reward safe drivers with lower premiums which encourages better driving behavior.

This is used in various ways like Usage-Based Insurance (UBI) and Pay As You Drive (PAYD), wherein the insurance premium will be based on the distance travelled and driving behaviour.

As evident from the above observations, Motor Insurance Sector’s Susceptibility to Fraud can be plugged to a great extent by leveraging AI and ML technologies in the right ways. Insurers can reduce losses due to fraud, protect the integrity of their insurance products, and ultimately provide more cost-effective coverage to policyholders.