How analytics can help in fraud identification?
How Predictive Analytics is Helping Prevent Insurance Fraud
Insurance fraud has been a problem for as long as insurance has been available. Unfortunately, there will always be people trying to abuse the system, which increases costs for everyone else. Insurance fraud is a felony, but that doesn’t stop some people from trying to benefit unfairly.
Even if a person is caught in the act of insurance fraud, it’s still expensive for companies to investigate all the suspected cases. Fraud that goes undetected can cost even more than the investigative work required to identify it.
The good news is that today’s technology has been helping to prevent and detect insurance fraud more efficiently, reducing the success of these crimes and the cost of investigating them. By adding data analytics into their operations, insurance companies have a chance to detect suspicious data patterns that could indicate fraudulent activity.
Why is Fraud So Common in the Insurance Industry?
People need insurance to protect against major expenses in their lives, such as car crashes, fire damage to a home, severe illness or injury, or the accidental death of a supporting family member. When a person thinks that a large expense won’t be covered by insurance, they are often tempted to pretend that it should be a covered expense. Other people intentionally try to manipulate insurance in their favor.
For these reasons, fraud is common in the insurance industry. In fact. In 2022, insurance adjusters believed that around 20% of claims might contain fraudulent information. While the actual incidence of intentional fraud is probably much lower, it’s still a large concern in the industry.
Because most claims can pay out thousands or even hundreds of thousands of dollars, insurance companies need to make sure that people are not misrepresenting the situation, such as shifting the blame for an accident off of themselves or intentionally damaging their own property. This can be a challenge since an insurance adjuster might not get a full picture of the situation from the information they have to work with.
How Predictive Analytics Can Help
Predictive analytics is the process of using big data to detect patterns and predict future behavior from historical data. This process has applications across countless industries and can assist with everything from anticipating demand for products to creating effective marketing strategies.
Insurance predictive analytics can be used to assess risk and set premiums, but it can also help spot suspicious activity that points to fraud. Insurance companies deal with a wide range of different fraud patterns, so it’s easy for less obvious behavior trends to go unnoticed. Big data platforms have the ability to analyze huge amounts of data and find insights that would be impossible to identify manually.
Once a possible case of fraud is identified and flagged, insurance adjusters can take a closer look at the claim and determine if fraud occurred. While predictive analytics can’t prevent every single case of fraud, it can reduce the losses insurance companies face, ensure that people are held accountable, and bring down the costs of fraud prevention.
Digital Forensics in Modern Insurance Fraud Investigations
Spotting fraud in the insurance sector often involves a new type of sleuthing: digital forensics. Data can provide many of the answers insurers are looking for when investigating fraud. After predictive analytics processes flag a potential fraud case for further investigation, the insurance company will take a closer look.
In some cases, law enforcement agents trained in digital forensics might come in to investigate the case for potential prosecution. By collecting relevant data from hard drives and cloud storage and examining it, digital forensics investigators can gather proof that can be used in criminal case trials to convict suspects.
The Power of Data in the Insurance Industry
The more accurately an insurance company can calculate risks when quoting customers, the better. Insurance companies are out to make a profit, so understanding how likely someone is to file an expensive claim is important for remaining profitable.
However, being able to accurately perform risk assessments and reduce fraud also helps customers. Insurance companies need to remain competitive, making it important for them to price their premiums attractively. This helps to keep costs down as much as possible for consumers.
The use of predictive analytics and artificial intelligence in fraud detection is becoming standard among insurance companies, due to its effectiveness. The insurance industry is always looking for new solutions to the age-old problem of fraud. In predictive analytics, they might have found their answer.
How analytics can help in fraud identification?