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The private insurance sector is recognized as one of the fastest-growing industries. This rapid growth has fueled incredible transformations over the past decade. Nowadays, there exist insurance products for most high-value assets such as vehicles, jewellery, health/life, and homes. However, as much as insurance provides assistance and support for the well-being of the citizens, it is one of the most challenging industriesthe problems of insurance fraud demand high security and fraud detection plans.

According to the Insurance Fraud Detection Market Research, 203, the global insurance fraud detection market size was valued at $3.3 billion in 2021 and is projected to reach $28.1 billion by 2031, growing at a CAGR of 24.2% from 2022 to 2031. The goal of fraud detection is to save insurers from incurring fraud-related losses. Fraud detection greatly increases the speed at which insurers identify fraudulent or potentially fraudulent claims. Todays economy is critical in cases of workers compensation where fraud is increasing.

From Nickolas Di Puma to Ali Elmezaye, the terror stories of insurance fraud are not new to the world. It has caused loss of money and even loss of lives. For example, Nicholas Di Puma staged a kitchen accident by setting his home and car on fire.

Gerald Hardin chopped off their friends hand to cash in on a $671,000 dismemberment claim. Jaques Roy committed the biggest health insurance fraud by performing unnecessary home visits, ordering medical services for healthy patients, and submitting fraudulent claims. Ali Elmezyen staged a car accident that killed his two autistic children and nearly drowned his wife. The stories of insurance fraud are never-ending. Indias Sukumara Kurp, who committed murder to claim insurance fraud, has never been caught.

There are six different types of insurance fraud, commonly. Making fake claims includes providing false, exaggerated claims, fabricating false healthcare records, and filing multiple claims for one incident. In provider fraud, providers file bills to the insurer for services not included in the treatment. Under application fraud, the insurer offers false information on the application form while purchasing the policy to receive plans at a lower premium or gain enhanced coverage. At times the policyholder intentionally misrepresents facts and information- these are fraud by the policyholder.

When someone uses another persons identity to obtain insurance coverage, it becomes identity theft. Finally, when the applicant or policyholder submits a claim for something that never happened, it is called claimant fraud.

Medical insurance frauds are causing billions of dollars in losses for public healthcare funds worldwide. AI automates the HIC fraud detection system. As per recent studies, AI has been mainly used to solve HIC fraud detection using several ML, deep learning, and data mining models. In addition, behavioral profiling methods based on ML techniques detect anomalies and fraud detection. For this purpose, each individuals behavior pattern is to monitor it for derivation from norms.

ML techniques used in HIC fraud detection is categorized into:

The high volume of healthcare data in electronic form is generated due to technological advancements. The major security issues in the HIC included the interlinked structure of electronic health records, the weakness of the health insurance portability and accountability act, and the threats of cybersecurity attacks, including software attacks and communication network attacks.

Blockchain has recently attracted much research interest, as it is a breakthrough database technology that may aid in the solution of complicated problems across many sectors. Artificial Intelligence (AI) and machine learning systems can be integrated into the claims processing, customer service, and fraud detection sub-sectors of the insurance sector.

A case study of fraud and premium prediction in automobile insurance was presented in Predicting fraudulent claims in automobile insurance at IEEE International Conference on Inventive Communication and Computational Technologies.

A data mining-based method was applied to calculate the premium percentage and predict suspicious claims. Three different classification algorithms were applied to predict the likelihood of a fraudulent claim and the percentage of premium amount.

The study presented in Robust fuzzy rule-based technique to detect frauds in vehicle insurance at IEEE International Conference on Energy, Communication, Data Analytics and Soft Computing employed a fuzzy logic approach by framing fuzzy rules for the machine learning algorithm to improve fraud detection. The latter technique was used for big and high-dimensional datasets to predict fraud using fuzzy logic membership functions.

AI can help in the above-shown ways for better customer satisfaction, profits & reducing frauds, and effective time and operational complexities. Proof of Concept has use cases of AI backed by corporate examples, thus showing the huge perspective of development in the insurance industry.

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Tackling the terrors of insurance fraud with AI - INDIAai