Benefits of Fraud Analytics in Insurance

Investigating insurance claims for fraud requires a lot of resources. Most companies only have a limited number of investigators, so they can’t thoroughly check every claim. That could make an insurance company lose a ton of money due to fraudulent claims. Using predictive analytics solutions can be very effective in this situation.

Turning Business Challenges into Analytics Solutions

Organizations aim to increase revenue, acquire new customers, boost sales, or reduce fraud. In a data analytics project, it’s crucial to initially grasp the problem the organization seeks to address. Next, think about how a predictive data analytics model, made with machine learning, can give helpful insights to tackle this problem. This part focuses on making the right analytics solution and is really important in the Business Understanding stage of the project.

Predicting Fraudulent Claims

A predictive analytics model forecasts the probability of fraud in insurance claims. It examines patterns in historical insurance claims data, encompassing both fraudulent and legitimate claims, to pinpoint fraud indicators. Training the model necessitates a substantial dataset of classified insurance claims, delineating fraudulent and non-fraudulent instances.

The model learns from the data to identify patterns and connections commonly found in fraudulent claims. For instance, it might discover that claims filed right after a policy change or for specific types of incidents are more likely to be fraudulent.

Once the model has been trained, it can be utilized for new claims. Each claim is assigned a score indicating the probability of it being fraudulent. This scoring is usually conducted on a scale, where a higher score signifies a greater likelihood of fraud.

Claims that receive a high likelihood of fraud score are flagged by the system. This doesn’t confirm they are fraudulent, but indicates they possess characteristics deserving closer examination. 

Prioritizing which claims to investigate using the model enables the company to concentrate on the most suspicious cases. This focused method works better than randomly checking or trying to investigate many claims.

It will help find more fraudulent claims, saving the company money and resources. And it might make fraudsters hesitate because they know they’re more likely to get caught.

The practicality

The main need for effectively implementing a claim prediction analytics solution in an insurance company is the business’s ability to supply a database of historical claims labeled as fraudulent and non-fraudulent, including details of each claim, the associated policy, and the claimant.

The prioritization mechanism needs to recognize and flag specific claims as high priority while adhering to the current timeframe for processing claims. 

If the insurance company has an existing claims investigation team, the feasibility study would evaluate their current operations and how they would integrate with a new system.

Prediction of High-Risk Policyholders

The main objective is to forecast the probability of a member (policyholder) engaging in fraud in the near future. This proactive method aims to catch potential fraud before it occurs, instead of dealing with it after the fact. Running the model regularly, such as quarterly, enables consistent updates on the risk profiles of members.

The model would most likely use past data like previous claims, behavior patterns, policy changes, payment history, and other important information. The system would use advanced analytics and machine learning to study this data and uncover patterns or behaviors associated with fraud from previous instances.

Afterward, each member receives a risk score indicating their likelihood of committing fraud.

Members with higher scores are flagged as high risk. Using this risk assessment, the company may contact the policyholder to provide a warning or consider canceling their policies.

By spotting and dealing with possible fraud ahead of time, the insurance company could save a lot by stopping fraudulent claims. Plus, if potential fraudsters know the company is taking action early, they might think twice before trying anything.

The practicality

The success of the recommended analytics solution for detecting potential fraud risks among members hinges on meeting several important criteria. Here are scenarios in which the solution would be considered feasible:

• The organization can connect each claim and policy to an individual member and retain historical records of policy alterations.
• The operational capability to carry out thorough analyses of customer behavior every quarter.
• An adept team able to uphold positive customer relationships, even when addressing sensitive topics like fraud.

The organization must possess knowledge of relevant legal and regulatory standards, including privacy laws, and establish systems to ensure compliance.

Prediction of Applicant’s Fraudulent Intent

This plan is all about catching possible fraud early, especially when someone applies for insurance. 

The main goal is to figure out if a new insurance application might lead to fake claims later on, focusing on stopping fraud before it even happens instead of just finding it afterwards. 

To do this well, the system looks at lots of different things like what’s in the application, past insurance info, trends in past fake claims, and sometimes even stuff like credit scores or public records. 

Each application goes through a check by the system, which gives it a score showing how likely it is to lead to fake claims in the future. 

If an application gets a score higher than a certain point, it might need a closer look or could even get turned down.

The practicality

Here are situations where this solution would work:

• The organization has a database of claims data that’s been categorized as either fraudulent or legitimate over several years. This is important because there could be a long time between when people apply for insurance and when they make a claim.
• The organization can connect each claim to the original application information. This means they can track which claims come from which applications.
• The organization needs to be able to smoothly combine the automated application evaluation process with their current application approval procedures. This ensures everything works together without any problems.

Prediction of Exaggerated Insurance Claims

In insurance, exaggerated claims, where people ask for more money than they should, are a big problem. 

When insurance companies think a claim might be exaggerated, they usually spend a lot of time and money investigating it. To deal with this, they’re thinking of using a computer program that learns from past cases to guess how much money a claim should get. 

The system checks details such as the reason for the claim, the initial requested amount, events during the investigation, and the final payout amount. 

Instead of doing long investigations, the insurance company could use this program’s guess to quickly and cheaply decide how much money to give to the person making the claim.

The practicality

The solution will work in situations where the organization meets these conditions:

• They have access to data about the initial and final amounts stated in a claim.
• They have the operational ability to act on the model’s insights. This involves making offers to claimants, which requires having a customer contact center or a similar way to directly communicate with claimants.

Creating the Foundation for Analytics Tables

The main idea of the model is to create a special table called the Analytics Base Table. 

This table puts together all the past information about insurance claims. It focuses on certain details that could signal possible fraud (these are called “descriptive features”) and whether a claim turned out to be fraudulent or not (this is the “target feature”). 

The structure of this table is based on important concepts in the insurance world, like Policy Details or Claimant History. These concepts are turned into specific details in the table. 

People who understand data analysis and those who know a lot about insurance work together to figure out which details are important. This teamwork makes sure the table has all the right info to catch fraud effectively.