Insurance Fraud Detection Using Machine Learning: What You Should Know

Fraudulent insurance claims cost insurance companies and consumers in Europe €13bn annually. Insurance fraud is rife, especially in the property, automotive, and healthcare sectors. Insurance companies are recognizing the need to adopt digital innovations urgently to reduce instances of fraudulent claims and better prepare for future threats. According to a report by Forrester, global investments in Insurtech exceeded $15B in 2021. 

How can AI and machine learning help your organization detect insurance fraud more effectively?

 

How to Detect Insurance Fraud

 

Investigating fraudulent claims is costly and time-consuming for insurers. It is physically impossible for insurance companies to do a thorough check of the thousands of claims that enter their systems daily.   

Early computerized systems could do so much – only allowing rudimentary analysis and search for fraudulent indicators known as red flags. A big limiting factor with this system is that fraudulent claims had to fit into a particular template or else they would not be recognized. Therefore, new technology is a blessing to insurance companies, providing game-changing solutions to enhance and automate processes along the insurance value chain.  

Nordic insurance companies have already modernized their fraud detection processes with RPA, which assists in verifying information located in different sources to detect the right data. Using RPA, an insurance company recorded a decreased claims cycle time from 6 – 10 minutes to 90 seconds. 

That being said, how do insurers ensure the utmost accuracy in filtering out fraudulent claims? This is where machine learning comes in. 

 

Machine Learning to the Rescue  

 

AI is known for simplifying menial tasks and freeing human agents to do more complex analyses. In terms of insurance fraud detection, machine learning applies aspects of AI to give systems the ability to improve from experience with no extra programming by analyzing large, labeled data sets.  

Machine learning can improve fraud detection techniques in the following ways: 

• Processes data in a short period of time.  
• Highlights where connections can exist between various factors that human eyes cannot detect. 
• Applies various data analysis techniques to allow the discovery of new fraud schemes. 

Although it borrows underlying principles found in statistical models, the main focus of machine learning is producing predictions. These predictions are based on the analysis of known outcomes, known as “ground truth.” Machine learning also can search for fraud in unstructured and semi-structured data such as claims notes and documents.  

Furthermore, machine learning can prevent fraud by detecting suspicious patterns in claims processing and customer background checks, which can potentially save insurers a lot of money. Since investing in a fraud prevention system, this Turkish insurer saved $5.7 million and recorded a 210% increase in ROI.  

 

The Insurance Fraud Detection Dataset 

 

The ground truth provides a label that identifies the outcome of each claim based on a historical dataset of insurance claim information and patterns. While there are varying outcomes between insurance claims, the labels are generally divided into “valid” claims or “fraudulent” claims.  

Health Insurance Fraud Detection Dataset 

In this case study, there are close to a million claims records with more than 20 variables. Claims have been assessed and labelled as normal and flagged for possible fraud. Claims that were flagged showed signs of suspicious policy profiles or malicious agencies, claims, or hospital-related fraudulent behavior. A machine learning model was created, a so-called binary classifier, to detect the two labels as accurately as possible. A supervised learning approach was applied since the data was already labelled.  

Auto Insurance Detection Dataset 

This project highlights the challenge of building a model that can detect fraud, where legitimate insurance claims far outweigh the fraudulent ones. This problem is known as imbalanced class classification. The data set consists of 1,000 auto incidents and insurance claims which had a total of 39 variables before any cleaning or feature engineering. Specific types of machine learning models, such as neural networks, natural language processing, and network graph analytics were also utilized in this dataset. 

 

Anomaly Detection in Insurance Fraud

 

Deep anomaly detection is a popular form of machine learning that can be utilized by the insurance industry to detect fraud. In claims processes, anomaly detection will analyze genuine claims by consumers. It then forms a model of what a typical claim looks like which is then applied to larger data sets. Insurers can also use anomaly detection to identify the suspicious behavior of users on an insurer’s network. In addition, deep anomaly detection can be combined with other AI applications such as predictive analysis to further automate the fraud detection process. 

 

Insurance Fraud Detection Using Big Data Analytics  

 

The Digital Insurer recommends a 10-step approach to implement analytics in fraud detection: 

1. Perform SWOT – A SWOT analysis of existing fraud detection frameworks and processes to identify gaps must be conducted.  

2. Build a dedicated fraud management team – It is important to have a team, not an individual, handling fraud claims.  

3. Whether to build or buy – Companies must evaluate whether they have the capacity and resources to build their own analytics framework or whether they need to engage an external vendor. 

4. Clean data – Remove inefficiencies and redundancies and integrate siloed databases. 

5. Come up with relevant business rules – Companies should leverage existing domain expertise and experienced resources. 

6. Come up with pre-determined anomaly prediction thresholds –Companies should provide inputs for threshold values for different anomalies.  

7. Use predictive modelling – An effective fraud detection method is one that uses data mining tools to build models that produce fraud propensity scores linked to unidentified metrics.  

8. Use of SNA – Effective identification of fraud activities by modelling relationships between various entities involved in the claim.  

9. Build an integrated case management system leveraging social media – This allows investigators to capture all key findings that are relevant to an organization including claims data and social media data.  

10. Forward thinking analytics solutions – Insurers should always be on the hunt for additional sources of data to improve existing fraud detection systems.  

An insurance company’s efficacy in distinguishing between valid and fraudulent claims plays a big part in determining its financial strength, allowing optimal compensation and support for its customers.