Using Predictive Analytics to Find Claims Leakage

Overpaying claims, known as claims leakage, is one of the major concerns for not only insurers but also for third-party administrators, self-administered organizations, and the policyholders. Every year, millions of dollars of cost are incurred by the industry due to claim leakage.

There are two general factors which pave the way for claims leakage– process issues, which are inefficient processing of claims and improper payments resulting in fines, penalties, or worker’s compensation issues, and human issues, such as poor decision making, low-quality customer service, and most importantly, fraud.

Determining Claims Leakage

It is essential for companies to identify and determine the magnitude of past and current claims leakage to determine if they are overpaying claims and develop a strategy to counter it. Determining claims leakage results in several benefits, including increased cost recovery, elimination of unnecessary or even fraudulent payments, and improved productivity of the workforce.

One way of identifying and determining claims leakage is through an audit of closed claim files, but when used alone, this may not be an optimal approach. An alternative and efficient approach is to incorporate predictive analytics in the process.

Use of Predictive Analytics for Determination of Claims Leakage

Predictive analytics, a branch of advanced analytics, makes use of data mining, modeling, statistics, machine learning, and artificial intelligence to make predictions about future events. Predictive analytics has experienced dramatic growth in use by the insurance industry. Using this technique, claims leakage can be analyzed and modeled at any stage of claim development. Claim assignment, coverage confirmation, investigation, negotiations, and recoveries are areas where predictive analytics helps.

Step 1: Data Gathering

The first and foremost step in determining claims leakage using predictive analytics is the gathering of data. Much of the data used is internal company data, i.e., claims data, or preferably, claims transactions data. Data from third party providers such as medical bill review data is also desirable.  External data, such as geodemographic data, are also often incorporated.  Questionnaires are also  a great way to augment a company’s internal structured data. The data is then cleaned and preprocessed. Feature engineering is used to transform the cleaned data so that it is more predictive.

Step 2: Data Analytics

The data related to claim leakage can be explored. Examples include:

  • Litigation rates
  • Closure rates
  • Reserve adequacy statistics
  • Salvage and Subrogation
  • SIU (special investigation unit) referrals
  • Large changes in paid amounts over time as the claim develops
  • Large changes in case reserves over time as the claim develops

These statistics are evaluated at various maturities of the claim cohort. The questionnaire date is used to identify additional claim leakage opportunities and identify claims where claim leakage occurred in the past. This data can be used to construct a dependent variable for a model that predicts the patterns associated with claim leakage.

The results of the data analytics step is typically summarized and presented in charts and tables.

Step 3: Reviewing of Analytics Results

Reviewing the results of the data analytics helps in concluding whether there is an excessive source of leakage, and based on this conclusion, whether an organization must take action.

It is essential for every company to control claims leakage to reduce their overall costs. Tools and techniques such as basic reporting analysis and predictive analytics are valuable allies in reducing this cost.


Authored by:



Consulting Actuary

Huggins Actuarial Services, Inc.


Louise Francis, FCAS, CSPA, MAAA

Francis Analytics


Kim Piersol, FCAS, MAAA

Consulting Actuary

Huggins Actuarial Services, Inc.