Fraud Analytics in Insurance

Investigating insurance claims for fraud is resource-intensive. Most companies have a limited number of investigators. Not all claims can be thoroughly checked. An insurance company losing too much money due to fraudulent claims. Implementing predictive analytics solutions can be highly effective.

Converting Business Problems into an Analytics Solution

Organizations have goals like making more money, getting new customers, selling more, or cutting down on fraud. In a data analytics project, it's really important to first understand the problem the organization wants to solve. Then, figure out how a predictive analytics model, built using machine learning, can provide insights to help solve this problem. This step is all about creating the right analytics solution and is the key part of the Business Understanding phase in the project.

Fraudulent Claim Prediction

A predictive analytics model predicts the likelihood of fraud in insurance claims. It analyzes patterns in past insurance claims data, including both fraudulent and non-fraudulent claims, to identify indicators of fraud. To train the model, it would require a large dataset of insurance claims that have been classified as fraudulent or non-fraudulent. 

The model would use the data to learn patterns and correlations that are often seen in fraudulent claims. For example, it might find that claims filed immediately after a policy change or claims for certain types of incidents are more likely to be fraudulent.

Once the model is trained, it can be applied to new claims. Each claim would be given a score representing the likelihood of it being fraudulent. This is typically done on a scale, where a higher score indicates a higher likelihood of fraud.

Claims that receive a high fraud likelihood score would be flagged by the system. This doesn't mean they are certainly fraudulent, but they have characteristics that warrant closer inspection.

By using the model to prioritize which claims are investigated, the company can focus on the most suspicious cases. This targeted approach is more efficient than random checks or trying to investigate a large number of claims.

This approach will increase the detection of fraudulent claims, thereby saving the company money and protecting resources. This could also deter fraud over time, as potential fraudsters realize that the chance of being caught is higher.

The feasibility

The key requirement for successfully implementing a claim prediction analytics solution in an insurance company is the business's capacity to provide database of historical claims marked as fraudulent and non-fraudulent, with the details of each claim, the related policy, and the related claimant.

The prioritization mechanism should  identify and flag certain claims as high priority and operate within the existing timeframe for handling claims. 

If the insurance company already has a claims investigation team, the feasibility study would assess how the team currently operates and how they would adapt to using a new system.

High Risk Policyholders Prediction

The primary goal is to predict the likelihood of a member (policyholder) committing fraud in the near future. This preemptive strategy aims to identify potential fraud before it occurs, rather than reacting to it after the fact.

Running the model, for example, quarterly allows for regular updates on the risk profiles of members. 

The model would likely use historical data, including past claims, behavioral patterns, policy changes, payment history, and other relevant data points. Advanced analytics and machine learning algorithms would analyze this data to identify patterns or behaviors that have historically been indicative of fraud.

The model assigns a risk score to each member, indicating their propensity to commit fraud. Members with higher scores would be flagged as high risk.

Based on this risk assessment, the company might contact the policyholder with a warning to with some kind of canceling their policies.

By identifying and addressing potential fraud proactively, the insurance company could save significant amounts by preventing fraudulent claims. This approach could also deter potential fraudsters if they are aware of the company's proactive measures.

The feasibility

The feasibility of the proposed analytics solution for detecting potential fraud risks among members depends on several key conditions being met. Here are scenarios where the solution would be considered feasible. The organization has:

• the ability to link every claim and policy to a specific member and maintain historical records of policy changes.
• the operational capacity to conduct detailed analyses of customer behavior every quarter.
• a skilled team adept at maintaining positive customer relations, even when discussing sensitive issues like fraud.

The organization should be well-versed in relevant legal and regulatory standards, such as privacy laws, and has mechanisms in place to ensure compliance.

Fraudulent Intent of an Applicant Prediction

This is a strategy aimed at identifying potential fraudulent activity at the earliest stage – when a policy application is submitted. 

The primary goal of the model is to assess the likelihood of a new insurance application resulting in a fraudulent claim in the future. This preemptive measure is aimed at fraud prevention rather than detection after the fact.

To make accurate predictions, the model would analyze a variety of data points. This could include information provided in the application, historical data of similar policies, patterns identified in past fraudulent claims, and possibly external data sources (like credit scores or public records).

Each application would be screened by the model, assigning a risk score indicating the likelihood of a future fraudulent claim. Applications that score above a certain risk threshold could be flagged for further review or potentially rejected.

The feasibility

Here are scenarios where this solution would be considered feasible. The organization:

• has access to a collection of claims data, classified as either fraudulent or non-fraudulent, spanning many years, given the potential long interval between policy applications and claim submissions.
• have the capability to link each claim to the original application details.
• must have the capacity to integrate the automated application assessment process seamlessly with the existing application approval processes.

Exaggerated Insurance Claim Prediction

A common problem in insurance is claims where the requested payout is higher than what is justifiable. When an insurance company suspects a claim is over-exaggerated, they conduct an investigation. This process is resource-intensive and costly.

The idea is to develop a machine learning model that predicts the likely payout amount based on historical data of similar claims and their outcomes. The model would use historical claim data, including the nature of the claim, the amount initially claimed, the results of any investigations, and the final settled amount.

When a new claim is filed, this model can be run to estimate the likely legitimate payout amount. 

Instead of going through the full investigation process, the insurer could offer the claimant the amount predicted by the model. This would be a faster, less costly process than a full investigation.

The feasibility

The solution will be feasible in scenarios where the following conditions are met. The organization:

• have access to information on the original amount specified in a claim and the final amount paid out. 
• needs the operational capacity to act on the insights provided by the model. This includes making offers to claimants, which assumes the existence of a customer contact center or a similar mechanism for direct communication with claimants.

In this article, we are working under the assumption that following a review of its feasibility, the decision was made to move forward with the claim prediction solution. This involves developing a model capable of predicting the likelihood of fraud in insurance claims.

Designing the Analytics Base Table

The core of the model's design involves the creation of an Analytics Base Table. This table will compile historical claims data, focusing on specific features that are likely indicators of fraud (descriptive features) and the outcome of whether a claim was ultimately deemed fraudulent (target feature).

The design of the Analytics Base Table is driven by the domain concepts. Domain concepts are the fundamental ideas or categories that are essential to understand a particular domain or industry. 

Each domain concept translates into one or more features in the Analytics Base Table. For instance, the domain concept of "Policy Details" might be represented in the table through features like policy age, policy type, coverage amount, etc.

The identification of relevant domain concepts is a collaborative effort involving analytics practitioners and domain experts within the business.

The general domain concepts here are: 

• Policy Details. Information about the claimant’s policy, including the policy's age and type.
• Claim Details. Specifics of the claim, such as the incident type and the claimed amount. 
• Claimant History. Historical data on the claimant's previous claims, including the types and frequency of past claims.
• Claimant Links. Connections between the current claim and other claims, particularly focusing on repeated involvement of the same individuals in multiple claims, which can be a red flag for fraud.
• Claimant Demographics. Demographic information of the claimant, like age, gender, and occupation.
• Fraud Outcome. The target feature, which is derived from various raw data sources, indicating whether a claim was fraudulent.