Is It Possible To Prevent Fraud In The Insurance Sector?

Fraudulent insurance claims cost the insurance sector billions of dollars every year. Detection of fraud is becoming challenging for the insurance sector. Most health insurance and vehicle insurance companies are relying on cutting-edge edge technologies to prevent fraud effectively. Traditional methods of fraud detection are not as effective. Successful detection of fraud will require analyzing data from many sources. Data science, artificial intelligence, predictive analytics and machine learning are being used by them. In this blog, we will discuss the ways the insurance sector is dealing with fraud.

Use of Artificial Intelligence

Since errors can be possible in manual checking, an AI model can be developed for insurance claims fraud detection. The AI model is trained to give a fraud score to a claim that has arrived for processing. AI will be able to detect exaggerated claims, false claims and duplicate claims. Fraud-flagged claims are then sent to fraud analysts.

Real-time notification is possible with AI systems as they can work 24*7, unlike humans. Unusual behavior of policyholders is detected in time so that remedial action can be taken by the agents.

Big Data can be used for the detection of fraud.

While AI systems can be used for fraud detection, an interesting question is how data analytics can help curb insurance fraud. Analytics can be used gainfully in the insurance sector for various purposes. Predicting risk while underwriting is possible with the help of analytics. An insurance agent will be able to ensure that the premium covers the risk. In addition to this, analytics can also help in lead generation. Customer relations can also be maintained with the help of analytics that helps in CRM.

But, how data analytics can prevent fraud? The history of fraudulent cases can be stored by an insurance agency. While processing fresh claims, the insurers can carefully check if there is a similarity. Fraud prevention can also be done with the help of analytics.
 
Predictive Analytics can also be used to prevent insurance frauds

It is easy to see how data analytics can help curb insurance fraud, but predictive analytics is in a different league. Predictive analytics uses statistical modelling to calculate the probabilities of any fraud. Statistical techniques are applied to data that is collected from customer portals, self-service apps, CRM systems, etc. to arrive at the probabilities.

It is possible to detect fraud using machine learning

Machine Learning is also an AI-based system that can help in the automation of claim processing. The models based on ML use natural language processing to detect any unusual behavior and alerts the agents before the claims go through. It can help firms in the extraction of meaningful insights from text data.

Techniques of data science to prevent insurance frauds

In the near future, the role of data science in the prevention of insurance fraud will become relevant. While making an assessment of how data analytics can help curb insurance fraud, it is necessary to understand that the underlying principles come from data science.

One cannot deal with a question like “how can data analytics prevent fraud?” simplistically. Strategies used in data analytics cover data mining also. They reveal the structure and content of information by representing it as a set of interconnected or linked objects or entities. Through these strategies, insurance companies can look through large volumes of data available on social platforms and establish relationships via links and nodes. These relationships, in turn, will be helpful while validating claims. A hybrid approach, including a host of analytical methods, is adopted in the tools used for the detection and prevention of fraud. The methods include mining data along the lines of organizational rules, statistical techniques, network linkage analysis, and pattern analysis to uncover relationships.    

IT auditing for the prevention of fraud.

IT auditing is a proactive approach to the detection and prevention of fraud. As a solution for the increasing number of health insurance claim fraud cases, IT auditing is being used. Since the amount of data that needs to be perused for detecting fraudulent claims are vast, IT-auditing techniques like Data Mining are used. Among the thousands of claims that have to be processed, Data Mining can help in identifying claims or claimants that can be classified as deceptive.