Insurance fraud can be as simple as providing incorrect contact information or even faking a serious accident.

According to a recent survey in insurance, Insurance Business America has announced that auto insurance is one of the most common types of insurance fraud. It says that approximately 14% contribution of all personal auto premiums goes to covering the cost of premium leakage.

Auto insurers lost at least $29 billion annually to what it called several “information failures and fraudulent practices.”
– Insurance Business America, 2022 –

Furthermore, according to the Federal Bureau of Investigation (FBI), “The total cost of insurance fraud (non-health insurance) is estimated to be more than $40 billion per year. That means Insurance Fraud costs the average U.S. family between $400 and $700 per year in the form of increased premiums.”

We are at a point where we cannot simply ignore the insurance fraud as it costs a considerable amount of money that could have been profit.

Now let’s see an example of car insurance fraud detection.

Automating Car Insurance Fraud Detection

The following example shows how you can automatically detect a car insurance fraud and send an email notification to a given authority. We have used the FlexRule Decision Automation tool with its built-in capabilities of data analytics and decision robotics.

Automated Fraud Detection

 

The process contains these main steps:

  1. Detect Frauds using a dataset.
    Using data analytics capability (AutoML), first, detects whether there is any fraud possibility in the claim request.
  2. If fraud is detected, check car registration information from the public directory.
    Using Decision Robotics, validate the car registration number and get details.
  3. If there is an error in the registration number, send a notification.
    If the registration cannot be found in the public directory, creates an error notification.
  4. Create the email alert message to inform the authority.
    Using the registration number details creates the email content that needs to be sent. If there is no registration, it will create a notification
  5. Send the email alert to the required authorities.
    Based on the created email content, send an alert email to a predefined email.

mainflow

This is a great example to show how to use advanced orchestration to combine different parts such as data analytics, and decision robotics to build one advanced automated solution.

If we dig deeper into the example, we have used a large dataset with previous claims.

dataset

The AutoML capability can find the best algorithm suitable for the given dataset and detect the feature important as well. Then it creates the model for the dataset.

frml

Finally, it trains and builds the model to detect fraud possibility of a submitted claim.

DRD

If fraud is detected, using Decision Robotics capabilities, it searches for the car registration details.

rpa-flow

It searches for the car registration details in the public vehicle registry.

rpa

As the final step, it sends an alert email to a given authority to immediately notify the fraud detection.

fraud detected

To see the complete description of this sample project, follow this article.

Global Need for Automated Insurance Fraud Detection Solutions

Due to the unnecessary cost of insurance fraud, there is a global urge to minimize insurance fraud, the insurance companies are investing more in solutions.

According to research conducted by MarketsandMarkets Research Private Ltd, there is an estimated 25.8% increase in insurance fraud detection. North America or the USA has the largest market.

fraud detected graph

They use various data sources to detect anomalies and unusual behaviors to detect vulnerabilities. There are a number of benefits,

  1. Highly Accurate
    Being able to use many different data sources and analyze them improves the accuracy of fraud detection.
  2. Extremely Efficient
    Unlike the manual process, you can use technologies such as Decision Robotics to improve the performance of the process and takes care of the respective tasks.
  3. More Reliable
    When you use an automated tool, it is available to detect frauds in real-time, unbiasedly.
  4. Require fewer resources.
    You will not need a team with a lot of resources to take care of the fraud detection. Since fraud detection is an additional cost, using a tool helps you to focus on product improvement rather than finding defaults.

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Therefore, automating fraud detection can not only have a major impact on a company’s profit but also can provide a better customer experience by reducing false positives and generating more legitimate customers.

Last updated January 9th, 2024 at 01:06 pm, Published June 2nd, 2022 at 01:06 pm