How AI is Used for Detecting Insurance Fraud

In 2024, insurers are in the race against fraud, with fraudsters using new sophisticated fraud techniques enhanced by the development of visual AI tools.

Not long ago, fraudsters using AI-generated photos for car insurance scams were not very convincing. However, today, the technology of tools like DALL-E and Midjourney has advanced so much that these AI-generated images can surpass traditional fraud detection algorithms. This makes it much harder for standard methods to tell the difference between real claims and fake ones.

Insurers are now turning to AI and machine learning for AI fraud detection in insurance. This article examines the role of AI in fraud detection, key technologies, and case studies. Read on to learn how AI is used to detect insurance fraud.

Understanding insurance fraud

Insurance fraud is the attempt to defraud the insurance company by faking accidents, exaggerating claims, or selling fake insurance policies. Insurance fraud can be committed by a seller, buyer, or third party with every type of insurance. Here are the common types of insurance fraud:

Categories by insurance type

1. Health insurance fraud:
• Provider fraud: Includes billing for services not provided, upcoding, unbundling services, and receiving referral kickbacks.
• Policyholder fraud: This includes faking or exaggerating illnesses or injuries, using another person's insurance, and going to the doctor to obtain multiple prescriptions.
2. Auto insurance fraud:
• Claims fraud: Staging accidents, padding claims, or filing multiple claims for the same incident.
• Application fraud: Providing false information when applying for insurance, such as falsifying driving history or using a false address.
3. Life insurance fraud:
• Application fraud: Providing false information about age, health, or habits to secure lower premiums.
• Claims fraud: Faking death, altering the cause of death, or falsifying beneficiary information.
4. Property insurance fraud:
• Homeowners insurance fraud: Filing claims for pre-existing damage, arson, or staged burglaries.
• Commercial property fraud: Inflating the value of damaged goods or claiming losses for items never owned.
5. Workers' compensation fraud:
• Employee fraud: Faking injuries, exaggerating the severity of injuries, or working while collecting benefits.
• Employer fraud: Misclassifying employees or underreporting payroll to reduce premiums.
6. Disability insurance fraud:
• Claimant fraud: Exaggerating disabilities or working while receiving benefits.
• Provider fraud: Certifying a disability that doesn't exist or is less severe.

Categories by fraud nature

1. Hard fraud: Intentional acts like staging accidents, arson, or theft to file a claim. This type of fraud is often orchestrated to deceive insurers into paying for damages or injuries planned and intentionally created. Another name for it is staged losses.
2. Soft fraud (opportunistic fraud): Exaggerating existing claims or minor falsifications to increase payouts. Examples include a homeowner claiming that an entire roof needs replacing due to minor storm damage when only a small section was affected. Another example is inflating the value of items lost in a burglary. In auto insurance fraud detection, a car mechanic can exaggerate the damage done to the car.

3. Internal fraud (Agent/broker fraud):
• Embezzlement: Agents stealing client premiums or using client funds for personal gain.
• Selling fake policies: Issuing non-existent coverage to collect premiums without the backing of an actual insurer.
4. Premium diversion: When an agent or broker takes premium payments without forwarding them to the insurer, often leaving clients without actual coverage.

Traditional methods or "trends" of fraud

Now that we have established common types of insurance fraud, let's dive deeper into the scenarios of how the scam is performed. This includes both traditional scenarios and the ones made possible by AI.

1. Staged accidents (auto insurance fraud):

• Swoop and squat: Fraudsters deliberately cause accidents, often with unsuspecting drivers, and then claim damages and injuries.
• Ghost passengers: In staged or legitimate accidents, fraudsters claim that additional passengers were present and injured, inflating the claim.

2. Inflated or exaggerated damages:

• Auto repair overbilling: After a legitimate accident, claimants or repair shops inflate repair costs or charge for repairs that aren't needed.
• Homeowners insurance: Claimants exaggerate the damage caused by storms or fires, claiming more repairs or replacements than necessary.

3. Exaggerated or faked injuries:

• Workers' compensation: Employees may fake or exaggerate injuries to receive compensation benefits or claim to be unable to work while secretly working elsewhere.
• Auto insurance: After a legitimate accident, claimants exaggerate their injuries to receive higher medical payouts or extended disability benefits.

4. Faked theft or loss:

• Fake burglary: A claimant stages a burglary or falsely claims items were stolen to file a fraudulent claim. For example, they may damage their property to make it look like a break-in.
• Faked loss of items: Claimants claim that high-value items like electronics, jewelry, or art were lost or stolen when sold or never existed.

5. Account takeover fraud:

• In this scheme, fraudsters gain unauthorized access to a policyholder's account (usually by using stolen credentials) to make changes, file claims, or divert payouts. This can happen via phishing, credential stuffing, or using breached data from other sources.
• Once the account is taken over, fraudsters may update contact details, change beneficiaries, or file fraudulent claims under the rightful account holder's name.
• For example, a fraudster takes over an individual's health insurance account, changes the mailing address and personal information, and then files fake medical claims in their name.

6. Synthetic identity fraud:

• Fraudsters create new, fake identities by combining real information (like a legitimate Social Security number) with fake details (like a fabricated name and address). These synthetic identities are used to open accounts, apply for insurance policies, and eventually file claims.
• The fraudster gradually builds a credit profile and applies for multiple forms of insurance under the synthetic identity, eventually submitting false claims or obtaining fraudulent benefits.
• For example, one may use a synthetic identity to apply for auto insurance, and after some time, the fraudster stages an accident and files a claim under this fake identity.

7. Deepfakes (digital identity fraud):

• Deepfakes are highly realistic but manipulated video or audio files that are used to impersonate someone else, such as an account holder or policyholder, to commit fraud.
• Fraudsters use deepfakes to create fake video or audio evidence supporting their claims. For instance, a deepfake video might show a staged accident or someone confirming their identity during an account takeover attempt.
• For example, a fraudster might create a deepfake video of a policyholder confirming a fake identity during an account recovery process or falsely "appearing" to authorize changes to their policy.

8. Arson (property insurance fraud):

• Claimants set fire to their property (home, business, or vehicle) to collect the insurance payout. This often happens when the claimant is experiencing financial hardship.
• In some cases, claimants hire someone else to commit arson and share the proceeds of the insurance payout.

9. Faked death (life insurance fraud):

• Phony death: A policyholder fakes their death or the death of another insured individual to collect life insurance proceeds. They may use fake documents, remote locations, or even hire individuals to assist with the fraud.
• Suicide disguised as an accident: When suicide is excluded from life insurance policies, some claimants may stage an accidental death to secure the payout for beneficiaries.

10. Ghost brokers (fake insurance policies):

• Selling fake insurance: Ghost brokers sell fake or counterfeit insurance policies to unsuspecting individuals who believe they have valid coverage. The fraudsters collect premiums without ever forwarding them to an insurer, leaving the policyholders without coverage.
• Duplicate claims: Fraudsters file the same claim with multiple insurers or submit a claim for a previously compensated loss, trying to get double payouts.

11. Misrepresentation of policy applications (application fraud):

• Falsifying information: Clients provide false information about their health, age, driving record, or property details to obtain lower premiums or qualify for policies they otherwise wouldn't be eligible for.
• Understating risk: This is common in auto insurance, where policyholders lie about the nature of the vehicle's use (e.g., personal vs. commercial) or the location where it's stored to lower their premiums.

12. Premium fraud:

• Underreporting payroll (workers' compensation): Employers intentionally underreport the number of employees or their total payroll to reduce their workers’ compensation premiums.
• Misclassifying employees: Businesses misclassify high-risk employees as working in low-risk positions (e.g., classifying construction workers as office workers) to pay lower premiums for their workers' compensation insurance.
• False business operations: Employers may misrepresent the nature of their business (e.g., claiming they operate an office environment when they run a high-risk business like a construction company) to obtain lower premiums for various types of insurance, including general liability or property insurance.

13. Duplicate claims (multiple insurance fraud):

• Double dipping: Fraudsters submit multiple claims for the same loss with different insurers, attempting to collect payouts from each one.
• Filing multiple claims for the same incident: After receiving compensation for a loss, the fraudster may submit another claim for the same loss either with a different insurer or by manipulating the event's details.

14. False medical claims (health insurance fraud):

• Doctor shopping: Patients visit multiple doctors to obtain unnecessary prescriptions (often for controlled substances) or medical treatments, which are then fraudulently claimed on health insurance.
• Phantom billing: Health providers bill for services never rendered, often inflating costs or charging for procedures the patient never underwent.

15. Premium diversion (agent/broker fraud):

• Embezzling premiums: Insurance agents or brokers collect premiums from policyholders but pocket the money instead of forwarding it to the insurance company, leaving the policyholder without valid coverage.
• Fake policies: Agents sell non-existent or fake policies to unsuspecting clients, who believe they are covered until they file a claim and discover no coverage.

Consequences and impact of such activity

• The Coalition Against Insurance Fraud estimates that insurance fraud costs around $308 billion annually across all lines of insurance in the US (Source ).
• Globally, insurance fraud costs hundreds of billions of dollars annually.
• Due to the high fraud costs, insurers often pass these expenses onto policyholders through higher premiums. Fraud-related expenses can increase premiums by as much as 10-15%.
• In markets where insurance fraud is widespread, insurers often face increasing costs due to fraudulent claims, making them reluctant to offer certain types of coverage. In some cases, insurers may even withdraw from high-risk regions entirely. For example, many insurance companies have left Florida in recent years due to fraud related to exaggerated claims of water or roof damage after hurricanes (Source ).

Ways to address insurance fraud

Traditional programming algorithms for insurance fraud detection are rule-based. This means that the system flags suspicious evidence and details of the case according to predefined claims that are perceived as red flags. While this method is quite consistent, it is difficult to add new rules to it.

Fraud detection in insurance can sometimes be checked with manual audits and investigations in case the claim is perceived as complicated. Other traditional methods that can be applied are statistical analysis through identifying outliers and data matching through cross-referencing.

However, the thing that holds the most promise is AI fraud detection in insurance, as it eliminates many difficulties associated with traditional algorithms. With machine learning algorithms, developers can train systems using thousands of examples of fraudulent activities. Once the algorithm is set in motion, it is more susceptible to change than traditional methods.

The rise of AI in fraud detection

Artificial intelligence is revolutionizing fraud detection. AI-powered systems can analyze massive datasets in real time, detect patterns that humans may miss, and continuously learn to improve fraud detection over time.

AI's ability to quickly process vast amounts of data is crucial in insurance fraud detection. Insurers handle millions of claims annually, making it impossible for human agents to manually review each one. AI can:

1. AI algorithms can recognize unusual patterns or behaviors, such as inflated claims or exaggerated injuries, often signs of fraud.
2. Unlike traditional systems, AI can instantly perform real-time checks on incoming claims and flag suspicious ones, allowing insurers to act quickly. AI for insurance fraud detection is capable of real-time anomaly detection at first notice of loss.
3. Machine learning models can be trained on historical data to identify new types of fraud as they evolve. AI gets smarter with time, adapting to emerging fraud tactics.
4. AI can reduce the number of false fraud alerts, ensuring that legitimate claims are processed swiftly while only truly suspicious cases are flagged for further investigation.
5. AI-powered insurance fraud solutions can help paint a more comprehensive picture of each case, with external data sources and even social media data profiles built into every case.
6. AI solutions for monitoring insurance fraud can predict future fraud trends before they become widespread. For example, the future development of deepfakes can be speculated upon even today.

In general, AI-driven insurance fraud detection systems are faster and more accurate because they result in fewer false positives and are more scalable and adaptable.

Key technologies in AI for insurance fraud detection

In this segment, let's review actual algorithms used in AI fraud detection in insurance:

Machine learning

• Trains models on labeled data (fraud vs non-fraud) to predict potential fraudulent activities.
• Financial fraud detection using machine learning detects anomalies in claims data without prior knowledge of fraud by identifying suspicious patterns and outliers.
• Analyzes complex data sets such as images or voice recordings to identify fraudulent claims (e.g., detecting fake injuries through medical imaging).

Natural Language Processing

• Analyses claim descriptions, emails, and unstructured text data to spot suspicious language or inconsistencies.
• Identifies fraudulent intent by detecting certain phrases or tones that suggest exaggeration or deceit in claims.
• For example, let's say a customer's initial accident report states that their car was parked at the time of the accident. Still, follow-up emails mention "swerving to avoid a collision. NLP detects the contradiction between the parked status and swerving, indicating possible fraud.

Data mining and analytics

• Finds hidden relationships and recurring fraud patterns in large datasets.
• Uses historical data to predict the likelihood of fraud in new claims.
• For example, in healthcare fraud, link analysis could reveal that certain medical providers are connected to many dubious claims for procedures that are either unnecessary or never performed.

Generative AI

• Can simulate large datasets to train machine learning models against fraud tactics.
• Generative AI in the insurance sector can also be used to create different claim scenarios and test potential fraud tactics.
• Can analyze hundreds of pages of documents simultaneously.

Challenges of implementing AI in fraud detection

For all of its benefits, insurance fraud prevention with AI is still evolving, with best practices not yet set in stone.

Here are some of the common challenges that occur in automating insurance fraud detection with AI:

• AI models require large amounts of high-quality data to function effectively. If data is incomplete, inaccurate, or inconsistent, it can lead to incorrect fraud detection results.
• Many organizations store data in disconnected systems, making it difficult to compile a comprehensive dataset for AI training.
• In industries like insurance, regulations often require clear explanations of how decisions are made. AI's complexity can make it harder to meet these regulatory demands. The models should be as transparent as possible so that insurers can understand how claims are analyzed.
• AI models learn from historical data, and if this data contains inherent biases (e.g., related to race, gender, or socioeconomic factors), the models may unfairly flag certain groups as more likely to commit fraud.
• Many insurance companies rely on legacy IT systems that may not be easily compatible with modern AI tools. Integrating AI with these outdated systems can require significant investment in upgrading infrastructure.
• Implementing AI-powered fraud detection systems requires significant upfront investment in software, hardware, and talent (e.g., data scientists and AI specialists).
• There is a growing shortage of skilled professionals with the necessary expertise to develop and maintain AI models.

Case studies of AI in insurance fraud detection

Here are the examples of companies which have been at the grassroots of artificial intelligence in insurance fraud detection:

Zurich Insurance

Challenge: Zurich Insurance needed a scalable solution to combat insurance fraud across various product lines, from auto to life insurance, and reduce the growing cost of fraudulent claims.

Solution: Zurich adopted machine learning algorithms combined with behavioral analytics to monitor and analyze customer behavior during claims processing . The system analyzed millions of historical claims to identify patterns associated with fraud, including behavioral red flags like inconsistent statements or suspicious claim timing.

As of 2024, Zurich Insurance uses AI fraud detection in insurance in more than 160 cases. It combines AI with traditional detection methods. Different algorithms are used either internationally or in particular regions. A specific AI for auto insurance fraud detection for Zurich Insurance Germany can even differentiate between different types of damage to vehicles: scratches, scuffs, etc.

Outcome: Implementing AI improved Zurich's ability to detect fraud at both the claim and policyholder levels. The system helped the company reduce its overall exposure to fraud, and the speed and accuracy of fraud detection increased. Additionally, the AI tools allowed Zurich to prioritize claims for human review, making the entire process more efficient.

AXA Switzerland

AXA Switzerland employed AI in collaboration with Shift Technology to detect fraudulent auto and property claims in real time.

By integrating telematics data (vehicle behavior) and applying AI, AXA was able to identify inconsistencies in reported accidents. This approach allowed them to flag staged accidents and inflated repair costs more efficiently. The result was a significant reduction in fraudulent claims, leading to a 30% improvement in fraud detection (Source ).

CNA Financial

CNA partnered with Shift Technology to enhance its fraud detection efforts by using the FORCE AI-native platform, which analyzes claims data to detect potential fraud.

The platform provides dynamic fraud scores and suggests investigation paths, helping CNA optimize their special investigations unit (SIU) efforts. By leveraging AI, CNA has been able to focus on the most suspicious cases, improving both efficiency and accuracy (Source ).

Final thoughts

AI is reshaping how insurers handle fraud detection in insurance claims. With tools like machine learning and data analytics, insurers can spot patterns that indicate potential fraud much faster and more accurately than traditional methods. This helps reduce false claims and speeds up the overall claims process. As AI continues to evolve, it will become even more effective in detecting fraud, making it a key component in keeping insurance systems efficient and secure.

Using AI algorithms for fraud detection requires IT expertise. At Binariks, we can help you with:

• Machine learning algorithms
• Predictive analytics model
• Data mining
• Data processing
• NLP models
• Integrating telematics solutions
• Telematics integration
• IoT integration