An auto finance firm takes the lead in automated collections
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An auto finance firm takes the lead in automated collections

Applying digital to deliver a best-in-class collections process

Who we worked with

A leading provider of auto finance products in Australia and in the UK.

How we helped

We analyzed the collections landscape, redesigned dialer operations, automated operations to eliminate manual processes, and enhanced customer experience with self-service tools.

What the company needed

Delinquencies brought under control, operational inefficiencies eliminated in collections, and improved response times.

What the company got

20%-25% reduction in outbound call inventory, 50%-55% reduction in inbound call volume through self-service, 1% increase in annual revenue by identifying unprofitable accounts, and improved customer experience.

Everyone loves to have a shiny new car in the driveway. And taking out an auto finance loan can be a great way to get one there. But when an Australian banking giant acquired a large portfolio in 2015 from a major bank, it found its sizable collections department was experiencing an unexpected spike in delinquencies.


Develop a new automated model for collection

The bank’s management thought that the most logical and expeditious way to handle this sudden escalation in collections was to add more personnel. Doing so, however, revealed a few previously hidden issues. Adding headcount naturally added to the cost of collections. But soon other challenges were evident in a number of operational inefficiencies, including high customer query volume, dialer KPIs not tied to business outcomes, managers with little visibility into real-time data, and a response process that was primarily manual and extremely time-consuming.

The bank had already been working with Genpact for more than four years on several other transformation initiatives, so they called on our deep expertise in auto finance operations to address its collections concerns by enhancing its analytics capabilities and operational efficiencies.

There were four primary considerations for transforming the bank’s collections process:

  1. Identifying activities that did not add value
  2. Reviewing current processes to standardize and/or automate them wherever possible
  3. Conducting workshops with employees to explore challenges and potential solutions
  4. Aligning the business with industry best practices

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Analyze the terrain to develop a roadmap

In a six-week period, we provided the bank with a transformation roadmap, moving collections to the front with lower costs and improved productivity levels. After an initial diagnostic of the collections process covering operations, dialer, customer query, and collections analysis, we implemented the following solutions:

  1. Collections analytics: We identified numerous improvements that could be made across the many interconnected components of collections, enabling the bank to establish a data-driven, best-in-class operations model with high efficiencies and lower collections costs. These included:
    • Risk-based treatment and omni-channel contact strategy – provided greater insight into customers and accounts
    • Collections score – allowed better targeting of delinquent customers
    • Recovery model – focused attention on risk and exposure vs. ongoing cost
  2. Dialer operations redesign: We concentrated efforts on workforce management (forecasting, staffing, and scheduling) and utilized prime-time window and real-time management tools to improve the bank’s planning and execution capabilities.
  3. Operational optimization: We automated or redesigned manual processes as necessary in the areas of hardships, forms, and letters to streamline the bank’s operations. This included custom-designing a collector incentive framework, focused on business outcomes and collector behavior.
  4. Enhanced customer experience: We implemented digital self-service tools, including smart/visual interactive voice response (IVR) for self-service, e-mail management through an natural language processing engine, and hardship management through a self-service portal supported by analytics.


A top-of-the-line collections model

The full range of improvements identified helped the bank establish a best-in-class collections operations model with higher efficiencies and lower costs. Data-driven decision making with predictive models and visualization delivered significant value to the business, including:

  • 20%-25% reduction in outbound call inventory by developing a collections scoring model and prioritizing high-risk customers
  • 50%-55% reduction in inbound call volume by promoting self-service and IVR
  • 1% increase in revenue from debt by identifying unprofitable recovery accounts earlier
  • Improved customer experience

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