Did you know that insurance claim frauds cost each customer an extra $900 in premiums unknowingly? With the advent of new technologies like digital signatures, image editing software, and deepfake technologies, fraudsters are shifting their tactics and creating more risks. Yes, it has been a real pain for both businesses and individuals in the past and the present.
So, how do you fight back? Well, some smarter technologies in the market can help curb this situation. These include leveraging artificial intelligence (AI) and machine learning (ML) techniques to detect and prevent insurance claim fraud.
5 Solutions for Beating Insurance Frauds
Implementing advanced solutions like AI/ML is crucial for insurance companies looking to enhance their fraud detection capabilities and protect their bottom line. Let us highlight the five most efficient solutions with the potential to transform the way insurance companies tackle fraud.
1 Natural Language Processing (NLP): Extensive Text Analysis
NLP is all about processing vast amounts of textual data. It’s a difficult task for your business to analyze several pieces of information such as claims management, policy documents, medical reports, and customer communications manually to verify that claim request is permissible.
NLP can do this work on your behalf by utilizing techniques like sentiment analysis, semantic analysis, and named entity recognition and takes the burden out of human staff.
For example, the legitimacy of a customer’s request for a car accident claim can be verified in the following ways:
- Claim Narrative Analysis: NLP will analyze the claim narrative for suspicious keywords or phrases. It can recognize patterns like inconsistencies, exaggerations, or false stories in the text.
- Document Verification: When the claimant submits documents such as police reports, medical records, and repair estimates, NLP compares these documents with known legitimate ones to identify discrepancies.
- Customer Communication Analysis: NLP lets insurers gather information about policyholders’ lifestyles and activities by analyzing social media activities and customer communications on various websites. This data can be used to identify inconsistencies between claimed losses and actual circumstances.
Lemonade, an insurtech company, uses its AI bot “Jim” to interact with the claimant, asking questions and gathering necessary information. This is how NLP helps in understanding the context and detecting inconsistencies or fraudulent activities through their responses.
2 Behavior Analysis: Finds Fraudulent Patterns
Any insurance company operating in present times can predict a customer filing a fraudulent claim instantly. Yes, behavioral analytics makes it possible by understanding and anticipating customer behavior in every aspect. You can even track and update your customer profile using real-time monitoring, predictive analytics, and data analytics.
For example, a sudden increase in small claim pay-outs for minor injuries suggests potential staged accidents, which can be verified through the following ways:
- Claim Frequency Analysis: Real-time monitoring spots unusual claim frequencies and rapid policyholder info changes, flagging them for investigation.
- Network Analysis: The claimant’s connection to the policyholders can be identified using data analytics. If it seems to be an organized fraud ring, you can take proactive actions.
- Customer Segmentation: Behavioural analytics can analyse customers’ behaviour’s and help you group high-risk profiles. Then, the policy terms can be tailored to fraud prevention.
Drive wise, an AI-powered telematics program, keeps the insurer updated about the customer’s driving behaviour in real-time. Thus, faking accidents is merely impossible for the customers in this context.
3 Blockchain: Enhances Data Protection
Though blockchain and AI are different, integrating them can power businesses to prevent insurance fraud through enhanced data integrity and traceability. Both technologies prevent data tampering and manipulation of critical records like policy information. They can even prevent your staff from making errors during the claiming process or payment release. It is possible with the creation of certain predetermined conditions and triggering actions that help you handle these requests.
Consider that there is a claim request for property damage. Here’s what you can do in this context:
- Claim and Document Verification: Blockchain ensures that claim records and customer documents are safe and even hackers couldn’t tamper with them. It makes it easy for artificial intelligence in insurance to analyze the data for inconsistencies before processing such claims.
- Supply Chain Transparency: Blockchain ensures accurate asset tracking and the claimant cannot bluff on lost or damaged items. This improves transparency and helps property insurance avoid approving false claims.
- Fraud Detection Networks: Blockchain lets the insurers stand together and create a collaborative fraud detection network. The combined data can be analysed using AI insurance algorithms to identify cross-industry fraud patterns.
Etherisc, a German insurance provider, uses blockchain to create smart contracts for various insurance products. Their flight delay insurance automatically processes claims based on flight status data recorded on the blockchain, eliminating manual claim verification and reducing fraud.
4 Robotic Process Automation (RPA): Increases Operational Efficiency
RPA and AI together can intensify the automation process and prevent insurance claim fraud by eliminating human errors. In fact, AI for insurance supports RPA solutions through data analysis and identifying patterns to automate repetitive tasks.
This proves to be helpful when the insurance company is overwhelmed with a high volume of small claims after a natural disaster. If you process these requests manually, you are increasing the chances of approving false claims. Consider resorting to the following solutions in this case:
- Claim Routing: Not every claim needs to be reviewed when RPA solutions and artificial intelligence in insurance can do the partial work. It can automate the initial triage of claims based on predefined rules, routing simple claims for automated processing and complex claims for human review.
- Fraud Pattern Identification: RPA can automate the identification and tracking of fraudulent patterns, while AI insurance algorithms refine these patterns using historical and real-time data for enhanced fraud detection accuracy.
- Investigative Support: The routine tasks involving fraud investigations such as document retrieval, data analysis, and report generation can be automated. RPA solutions can speed up the threat identification process and free human resources to focus on taking proactive measures.
Zurich, Switzerland’s largest insurer has already saved 51% of its operational cost by integrating RPA in their 48 app flows. It uses a standard format template through which the robots handle repetitive tasks like data entry and document verification, speeding up the process and reducing the risk of human error, which can be exploited by fraudsters.
5 Computer Vision: Fraud Detection through Visual Analysis
With computer vision identifying discrepancies, anomalies, and staged accidents by analyzing evidence, fraudsters can no longer gain profit with fake videos and images.
This proves to be helpful in verifying certain hit-and-run accident claims. Here’s how:
- Image Verification: It can identify forged submissions with images like photoshopped damage or altered vehicle identification numbers.
- Damage Assessment: Inconsistency in the level of damages and pre-existing conditions can be identified using computer vision.
- Video Analysis: Computer vision can analyse surveillance footage or dashcam videos to identify suspicious behaviour.
Geico, the second largest auto-insurer in the U.S., is currently using this computer vision technology to accelerate its auto claim and repair process which potentially finds fraud claims while improving the speed of settlement for genuine requests.
Bottom Line
Artificial intelligence, in its various forms, has emerged as a go-to solution against insurance claim fraud. From NLP to computer vision, these AI/ML technologies help combat various insurance fraudulent activities. However, there is no denying that even the best AI tools need a human touch to work efficiently.
Furthermore, you can augment tech-savvy solutions by subjecting them to continuous learning and improvement. This is how AI/ML can go beyond just detection and help build a more resilient and trustworthy insurance ecosystem that benefits both insurers and policyholders alike.
If you need a leg up in your fight against insurance fraud, get the Trigent AXLR8 Labs advantage. It gives you access to our AI accelerators that enable accurate risk assessment, help streamline the underwriting process, and deliver intuitive customer experiences.
Beat Insurance Fraud with AI. Contact Us Today!
