collection analytics case study

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Case Studies

Debt Collection Analytics

Challenge: Debt Management Service (DMS), within the Fiscal Service at the Department of the Treasury, seeks to identify, collect, and resolve debts owed to government agencies, including those associated with state child support and delinquent student loans. DMS utilizes a number of passive and active collection activities to collect on delinquent debt. DMS aimed to apply optimal collection techniques to maximize collections given their limited resources.

Summit’s Approach: Summit integrated relational databases containing information on debts and collection activities. We then classified different clusters of debts based on debt and debtor characteristics. Implementing machine learning and regression techniques, we determined which debts would likely result in the highest collections using DMS' active collection tools and campaigns. The models also identified the optimal collection timing and holding period for conducting collection activities to maximize overall collections. The operational and financial impact of these changes in collection strategies were then estimated through the use of randomized control trials.

Result: The revised collection strategies led to an 7% increase in collections, increasing DMS’s revenue as well as reducing the costs of the DMS program to taxpayers.

Case Study: Improving Cash Collections with Analytics

QUALCO | June 9, 2021

During the recent Consumer Credit Risk Conference, Terry Franklin , QUALCO’s Global Business Development Director, presented a case study showcasing the benefits of adopting predictive analytics models in collections workflows to significantly increase cash collections.

You can watch now the full presentation below:

By following our tested approach and employing the powerful data intelligence of QUALCO’s Data-Driven Decisions Engine (D3E) , we managed to deliver:

7% improvement in cash collections during the first month of deployment, a percentage that is continuously increasing
Better roll rates powered by insight driven customer treatment
Measurable improvement in treatment outcomes, especially in terms of “unofficial” payment promises

Identify, monitor and verify your performance optimisation initiatives more effectively and efficiently.

Learn more on how to bring predictive insights to your operations, here

A Leading Utility Increases its Debt Collection by 50 Percent with Predictive Analytics

23 june 2015.

A WNS Perspective

Challenge The client wanted to optimize collection processes to improve recovery and create strategies to manage customer write-offs more effectively

Solution WNS transformed the client's collections process by leveraging predictive analytics and enhancing the customer interaction strategy

Benefits The client achieved a 50 per cent improvement in debt collection in three months

One of the leading energy and utilities companies

The Challenge

The client had a dire business need to re-balance its energy final debt portfolio. On one hand, its debt recovery rate was 4 percent, compared to 14 percent achieved by its competitors. On the other hand, the commissions charged by the client's debt collection agencies were as high as 50 percent of the collected amount, which resulted in high operational costs. Consequently, profit margins were dented. The client intended to optimize its final debt collection processes to improve recovery of receivables. The client also wanted to formulate focused debt management strategies for different customer segments to manage customer writeoffs more effectively and decrease operational costs.

The WNS Solution

WNS concentrated on transforming the client's collections process by embedding predictive analytics and making changes to the customer interaction strategy. Key aspects of the WNS Solution:

A Propensity-to-Pay Predictive Data Model exclusively for residential customers. This model predicted the likelihood of customers being able to pay their dues after their accounts were finalized. The model assigned a propensity-to-pay score to every customer.

Customer classification into high, medium and low propensity-to-pay segments based on their scores.

Focused delinquency management strategies for every segment.

Customer segment prioritization. Customer segments were prioritized on the basis of the propensity-to-pay scores and the amount of outstanding debt.

Rigorous cost-benefit analysis to streamline operational, financial and human resource activities. This exercise was instrumental in optimizing the debt management process.

Engaging with customers. The customer service executives used customized call scripts and pre-determined verbiage to conduct settlement negotiations and provide debt management advice to customers.

Performance monitoring of pilot strategies against critical tactical and quality indicators and also against the parameters set by the standard process.

Benefits Delivered

By deploying predictive analytics, WNS was able to fulfill the client's business objectives and helped achieve the following outcomes:

Debt collection increased by 50 percent within 3 months

The modified process recorded an 8 percent rise in conversion rates compared to the standard process

Operational expenses decreased by 20 percent

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