Role of AI and ML in Insurance Fraud Detection
Insurance frauds have been in existence ever since the inception of the concept of insurance. False claims and incorrect statements in subscriptions cause the insurance companies to lose several millions of dollars every year. An FBI report states that the total value of insurance fraud is approximated to be more than $40 billion per year. The loss on fraudulent insurance claims last year reached $34 billion in the U.S alone. According to the Friss Insurance Fraud Report, 75% of insurance industry professionals believe that more than 10% of all insurance claims have some kind of fraudulence involved.
Due to the Covid-19 pandemic, some industry professionals trust that the number of claims with some element of fraud has almost increased twice. Covid-19 enforced 65% of insurance companies to focus on digitization, nearly half of them concentrated on reducing costs, and 30% increased their fraud controls.
Even though an insurance agent would have the capacity to investigate each case and conclude whether the claim submitted is genuine or not, it’s a time-consuming process. Using skilled labor to review each one of the thousands of claims that are filed a day is not feasible. Insurers cannot control these claims using traditional computerized systems. Hence they have started adding artificial intelligence to their fraud-fighting toolkit.
Artificial intelligence (AI) plays a key role in insurance scam detection by detecting false claims. As a result, insurers can achieve an efficient and effective claims management system. AI algorithms can analyze huge amounts of data rapidly to find patterns and spot anomalies that don’t fit the patterns.
Insurance fraud can occur during various points of the insurance lifecycle which are explained below.
Application fraud is the most common form of fraud and it happens when intentionally providing false information in an insurance application.
False Claims Fraud:
This kind of fraud happens while filing insurance claims under false pretenses. Examples can include:
- Create an accident or injury so as to deliberately file a claim
- Filing a claim for an injury or accident that never happened
- Filing a claim with incorrect information
- Death fraud happens when someone fakes their own death in order to collect a life insurance benefit
It is the simple act of adding a small amount to the total bill when you file your insurance claim. Auto Insurance fraud is a major area of concern where fraudulently inflated claims are frequently filed for vehicles damaged due to accidents and those that have been stolen.
Forgery and Identity Theft Fraud:
Sometimes people try to file claims under someone else’s insurance. This is particularly common with health insurance. Individuals get another person’s identifying information then attempt to make claims against their insurance.
Fraud Detection Using ML and AI-Based Approaches:
Deep Anomaly Detection
Deep Anomaly Detection is a popular form of machine learning that can be used by the insurance industry to detect scams. During claims processing, it will analyze genuine claims and also identify the ones that smell suspicious behavior.
ML algorithms that facilitate Fraud Detection:
Based on predictive data analysis, this is the most commonly used ML algorithm for fraud detection. In this model, all the input information has to be labeled as good or bad. However, it cannot discover frauds that occur outside of the historical data sets.
This algorithm stores data related to the critical category parameters. It works for cases where labeling information is either impossible or highly expensive. Semi-supervised learning stores information about the important group parameters even when the group membership of unlabeled data is unknown.
An unsupervised learning model can detect the unusual actions in the transactions. It continuously processes and analyzes new data and updates its models based on the findings. It looks for specific patterns in the data to determine fraud in transactions.
This kind of algorithm makes software automatically verify behavior in a particular context. This algorithm instructs the environment to find out risks in the transactions. It constantly absorbs information from the environment to find ways to reduce risks.
Predictive analytics observes the historical data as well as existing external data to find patterns and behaviors. The Predictive Data Analytics solution is used to identify false bills, raise suitable red flags, perform data analytics and generate visualization reports. These advanced analytics, data mining, and statistical techniques pave the way for proactive fraud detection.
AI Algorithms that facilitate Fraud Detection:
History of Referrals to Special Investigations Unit (SIU)
Technology specialists construct algorithmic models for calculating the probability of a claim being above a threshold level that can be referred to as SIU. This algorithm leverages the historical data of claims that have been referred to SIU to determine a probability value. Using investigation scoring automation methods claims that score more than the threshold value can be detected. Based on the investigation score, it can be categorized as good or bad risk claims.
Advanced Digital Algorithms for detecting Historically Rejected Claim Records
The claims that have historically been rejected will have a great probability of being rejected in the future too citing potential fraudulence. Specialists put in place digital algorithms that automatically scan through the claims parameter patterns. This includes conciliation patterns and claims risk indicators such as an individual’s SSN, phone numbers, address, etc.
This can be achieved by using advanced clustering-based Data Mining techniques. These algorithms categorize clusters with high claim frequency based on the above risk indicators, filter the clusters, and classify them into bins of various degrees. These bins indicate the level of risk and each bin might need a different degree of attention.
Fraud Pattern Recognition Method for Specific Individuals/Groups
A fraud pattern recognition algorithm is used to detect and flag network groups or individuals that repeatedly file fraudulent claims. Based on these flags, ‘fraudulent patterns’ can be identified using automated algorithms to categorize individual or group records that have similar historical risk patterns.
Fraud Detection via Social Media Profiles using Advanced Analytics Models
These are the latest algorithmic models built to identify potential insurance frauds based on the social media profile and interaction patterns, recent behavior, and attitude on social media of individuals filing the potential fraudulent claims. These algorithms look for and detect possible mismatches in the actual profile of the person on social media as compared to his/her claims. For example, if a person has been flaunting his/her lifestyle recently on social media, then an accident claim filed around the same time is likely to be fraudulent.
Benefits of AI and ML in detecting Insurance Frauds:
- All claims suspected of fraud will be exactly detected.
- Data is processed in a very short span of time.
- The continuous revision of these algorithms and training with a variety of data for analysis will allow for the discovery of new fraud schemes in the future.
- With the evolution of AI, insurance claim settlements, and the personalization of user interfaces, fraud detection can be extensively improved in terms of speed and correctness.
- With the aid of AI, the insurance industry has reconstructed the claims management process by making it not just quicker but also easier to use.
Finally, the goal of AI in the field of insurance fraud is to make it easier for human agents to find and scrutinize false claims and transactions, rather than sifting through tons of claims in a fatigue-inducing and ineffective manner. Many insurance providers and companies are limited due to the exploitation of insurance fraud and the cost of human agents.
The ROI provided by using AI and ML to detect frauds automatically will undoubtedly allow any insurance organization to grow by leaps and bounds.
Author: Vijayakumar A