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How can companies tasked with recovering consumer debt for clients make best use of resources? The key is to focus collection strategies on the people who are most likely to pay – but how can you tell the good debtors from the bad ones?

This was the challenge facing Afni, a leading provider of contact center outsourcing for enterprises in the United States and the Philippines. Working together with IBM, the company implemented IBM SPSS Modeler – using predictive analytics to deliver up to 94 percent reductions in manual processing, six-figure financial benefits for its receivables business, and an estimated potential return of several million dollars across the entire company.

Corporate background

Headquartered in Bloomington, IL, Afni employs approximately 4,000 people. With expertise in a wide range of industries including communications, insurance and healthcare, the company delivers a comprehensive set of offerings that includes acquisition and enrollment; care and loyalty; up-sell and cross-sell; and receivables and subrogation services.

Detect good payers to boost profitability

To enable maximum cost-efficiency in its receivables and subrogation business, Afni wanted to focus its collection efforts on the debtors who have the highest probability of repaying.

As Barry Gamage, Director of Advanced Analytics, explains, “Afni provides late-stage, post charge-off debt collection services for many clients. Only a small proportion of the debtors in these portfolios repay their creditors, and each time we accept a package of receivables from a client, we take on a degree of business risk.

Afni deploys predictive

analytics to drive

million-dollar financial benefits

Using a smarter approach to debt recovery to identify

the best payers and focus collection efforts

Overview

The need

Afni wanted to offer more profitable debt-recovery services to its clients by accurately and cost-efficiently predicting which debtors were most likely to pay, and prioritizing collection efforts accordingly.

The solution

The company deployed IBM® SPSS® Modeler and IBM SPSS Modeler Server software in-house – replacing third-party analytics services and enabling the company to develop more advanced predictive models.

The benefit

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“To maximize returns for our clients and minimize our risk, we use predictive analytics to estimate each debtor’s individual circumstances and give them a score based on their probability of repaying.” Afni knew that building smarter analytics models would help improve the accuracy of its predictions – enabling it to spend more on high-probability payers, and gain a greater return for each dollar spent on telephone calls, letters and “skip-tracing” (the process of locating a debtor’s current whereabouts if their address details are missing or inaccurate).

“In the past, we used a third-party provider for predictive analytics, which was expensive,” says Gamage. “We calculated that making changes to our models and running multiple scorecards per day would result in prohibitive costs if we continued with this approach. We were ready to develop an in-house predictive analytics capability, and we wanted to do it quickly.”

Choosing a smarter approach to predictive

analytics

After evaluating a number of analytics solutions from different vendors based on criteria including ease of use, functionality and total cost of ownership, Afni chose IBM SPSS Modeler.

“Of all the solutions we considered, we felt that IBM SPSS Modeler was the best fit for our business requirements,” says Gamage. “We sought a short implementation timeline, and were confident the IBM solution offered the user-friendly, cost-efficient package of advanced analytics functionalities we needed to achieve our business goals.” He adds: “Once we had built the business case for an in-house predictive analytics solution, there were no difficulties securing senior buy-in; our executives immediately understood the need for investment in this area.”

With the full backing of its executive team, Afni deployed IBM SPSS Modeler and IBM SPSS Modeler Server software.

Solution components

Software

• IBM® SPSS® Modeler • IBM SPSS Modeler Server

“Of all the solutions we

considered, we felt that

IBM SPSS Modeler

was the best fit for our

business requirements.”

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“The SPSS Modeler

solution enables us to focus

our efforts on cases with

the highest probability of

returns.”

— Barry Gamage, Director of Advanced Analytics, Afni

Delivering game-changing business insights

Today, Afni’s IBM SPSS Modeler solution forms the heart of its advanced analytics department. By augmenting its predictive analytics capabilities, the company has achieved its goal of boosting profitability for its clients and itself, and minimizing risk in its receivables business. “Before the IBM solution, we had to make do with predictions based on a limited set of data,” says Gamage. “Because our in-house analytics program is more cost-effective, we can now enrich our data with credit reports, ZIP+4 information from the census bureau, average incomes, house values, and levels of education in debtors’ neighborhoods.” Using automatic segmentation tools in IBM SPSS Modeler, Afni can now identify the strongest predictors of a good or bad payer – avoiding time consuming trial-and-error processes. Based on these indicators, the solution automatically scores each debtor with ultra-low, ultra-low, medium, high or ultra-high probability of repayment. These classifications determine the threshold for the maximum number of telephone calls, letters and skip-traces that Afni will invest in for each debtor’s individual treatment plan. Depending on each individual score, the solution automatically initiates automated mailing or dialing campaigns – streamlining the contact process.

Automatically identifying the best payers

“We had not anticipated just how accurate ZIP code predictors could be,” says Gamage. “The SPSS Modeler solution has revealed that neighborhoods with higher than average incomes and house values are strong indicators of good payers – enabling us to focus our efforts on cases with the highest probability of returns. In fact, we estimate that the solution will deliver six-figure savings for our receivables business and a return of up to 15 percent for our clients.”

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“With IBM SPSS

Modeler, we can now

use ZIP+4 data to

enhance our insight into

uninsured drivers, which

helps predict good payers

more accurately. As a

result, we estimate that

we can increase successful

subrogation collections by

as much as 15 percent –

helping us recover more

dollars for our clients.”

— Barry Gamage, Director of Advanced Analytics, Afni

Increasing success rates for subrogation

The predictive analytics capability is also enabling the company to improve debt-recovery outcomes in its subrogation business – which acts on behalf of insurers to recover the cost of claims from the party who is at fault or their insurance company.

“Subrogation is an important part of our service portfolio,” says Gamage. “In cases of car accidents, we are often contracted to subrogate an insurance claim from either the fault party or the at-fault party’s insurer.

“In the past, pursuing a subrogation claim against an uninsured driver had a low probability of success. Since we have no legal right to see credit data in such circumstances, it is difficult to identify the drivers who are most and least likely to pay.

“With IBM SPSS Modeler, we can now use ZIP+4 data to enhance our insight into uninsured drivers, which helps predict good payers more accurately. As a result, we estimate that we can increase successful subrogation collections by as much as 15 percent – helping us recover more dollars for our clients.”

Cutting manual processing by 94 percent

Using IBM SPSS Modeler also enables the company to accelerate the process of identifying opportunities to pursue subrogation claims that might previously have been overlooked.

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“Without a doubt, the

predictive analytics

capabilities provided by

IBM SPSS Modeler are

a key differentiator for

our business.”

— Barry Gamage, Director of Advanced Analytics, Afni

“In one recent project, we used ACER to cut our manual closed-file review workload down from 100,000 to just 6,000 records – a 94 percent reduction in manual effort. We found the same number of subrogation claims to pursue as a full manual review would have yielded – but at a much lower cost to our client.”

Potential for multi-million dollar savings

The successful predictive analytics projects described in this case study are just the first of many that Afni hopes to move into production. The advanced analytics team currently has a list of 108 projects on its agenda, which are estimated to deliver total cost benefits of several million dollars if they all reach implementation. These will improve results for clients and give Afni more ability to invest in research and development of its service set.

Gamage concludes: “Without a doubt, the predictive analytics

capabilities provided by IBM SPSS Modeler are a key differentiator for our business. Thanks to the IBM solution, we can focus our investment and resources on people who are most likely to repay their debts – helping us to deliver even greater returns for our clients.”

About IBM Business Analytics

IBM Business Analytics software delivers data-driven insights that help organizations work smarter and outperform their peers. This comprehensive portfolio includes solutions for business intelligence, predictive analytics and decision management, performance

management, and risk management.

Business Analytics solutions enable companies to identify and visualize trends and patterns in areas, such as customer analytics, that can have a profound effect on business performance. They can compare scenarios, anticipate potential threats and opportunities, better plan, budget and forecast resources, balance risks against expected returns and work to meet regulatory requirements. By making analytics widely available, organizations can align tactical and strategic decision-making to achieve business goals.

For more information

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Produced in the United States October 2013

IBM, the IBM logo, ibm.com, and SPSS are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at: www.ibm.com/legal/copytrade.shtml. This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates.

The client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions.

THE INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided.

The client is responsible for ensuring compliance with laws and regulations applicable to it. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the client is in compliance with any law or regulation.

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