Solution Overview

How a scientific approach to accounts receivable...

...can give you more of the working capital you need

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Many factors can have a major effect on the way your accounts receivable (AR) department manages business-to-business (B2B) credit and risk. Customer behavior is volatile, for one thing. Changes in local and global business environments can also have an impact. These conditions call for proactive measures. If you don’t move quickly, your company could face more days sales outstanding (DSO) – and that ties up your working capital.

Yet B2B collections processes have traditionally been reactive – an approach designed for more stable economic times. It might be wise, then, to rethink how you handle AR. One innovation to consider: applying algorithms that predict past-due invoices and risk. These algorithms can also help you segment your market more effectively.

Update old-fashioned methods with scientific rigor

Many global firms base their B2B collections strategies on aging AR data. These companies often don’t segment very effectively. Worse, they’re likely to have sporadic access to external data, and to manage their portfolios poorly. Their ERP tools and collection workbenches struggle to store and analyze large datasets with multiple variables and complex structures. What’s more, legacy processes and relationship maps can force them to take a static approach to collections strategies.

These challenges present problems in the following areas.

  • Productivity: We’ve found that firms waste between 10% and 40% of their collections spend because of poor portfolio definition and segmentation. Additionally, account coverage per AR collector is about 65% to 70%, leaving up to 30% untouched each month
  • Days sales outstanding: DSO levels can range from 35 to 50 days in different regions
  • Working capital: For large, multinational organizations with billions of dollars in revenues, each day of outstanding AR can result in millions of dollars in lost opportunity cost
  • Profitability: Past-due collectible invoices lock up as much as 5% of earnings before interest and taxes

Without scientific and statistical approaches for predicting customers’ payment behavior, collectors make decisions intuitively or in an ad hoc manner. The outcome?

  • Firms aren’t collecting or managing past due accounts and invoices effectively
  • Companies depend on collectors’ experience of customer payment behavior. When these collectors move on to new roles, they leave a knowledge gap behind
  • Performance on past due aging is flat because collectors can’t learn much from weak historical data

What’s needed? A dynamic feedback loop between AR collections risk and credit risk management. Without one, you’ll run into problems, such as irregular credit reviews and assessments. And you’ll depend on aging threshold-based triggers and limited external credit appraisal data.

That’s more bad news, because:

  • Enterprises write off 1% to 3% annually in bad debt
  • Firms leak 0.5% to 1% in revenue because no one intervened in a timely manner when assessing credit improvement and deterioration

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Analytics and the latest technology for predictive modeling

Genpact’s cloud-based dynamic credit and collections engine can help you face these challenges. It uses advanced digital technologies, such as machine learning, natural language processing, and big data analytics, to drive predictive modeling and accurately forecast past-due invoices and AR risk. With its continuous cognitive assessment of collections portfolios and strategy, machine learning identifies patterns across vast data sets. It predicts customer payment behavior, and helps manage customer credit risk in real time.

Here’s what the engine can do:

  • Data integration consolidates data of different kinds and from different sources to deliver a statistical view of customers’ credit health, including:

    • Historical structured payment data, such as payment history and frequency of late payment or disputes. It extracts this information from ERPs, collections workflow, reporting solutions, and more
    • External structured customer data, such as liquidity ratios, financial stress scores, and credit scores to determine customers’ profitability and business health
    • External unstructured data, such as legal and political news, customer reviews and feedback, political disturbances or other events affecting your customers’ business, and deals won
  • Statistical predictive analytics anticipate customer payment behavior, predict potential late payment, and estimate the aging of past-due invoices
  • A dynamic risk-based segmentation model builds on a credit and AR-risk scoring framework to define multidimensional customer segments and deliver an efficient strategy. The model refreshes as new information becomes available (see figure 1)
  • A dashboard of customer and invoice past-due risk and scoring status guides credit and collections teams with segmented actions to drive customer credit and collections response workflows
  • A decision-support collections workflow based on customer size, AR at risk, channel, and other parameters provides more effective customer segmentation, strategy definition, and portfolio management

Figure 1: Dynamic risk segmentation model


A highly responsive collections system and more working capital

Genpact’s Lean DigitalSM approach, which harnesses design-thinking methods, Lean practices, and advanced digital technologies, powers our dynamic credit and collections engine.

We used our knowledge of running collections operations for more than 90 clients to help shape our solution’s sophisticated analytical models and process workflows. Your company can benefit from the solution along two tracks. First, the algorithms can help you predict:

  • Which open invoices will be overdue
  • The most probable aging of overdue invoices

Knowing the answers means collections teams can adopt proactive strategies that reduce aging and late payment. Second, the solution’s scientific approach to portfolio segmentation and strategy integrates your collectors’ experience with domain expertise to produce advanced analytics. That gives you real return on investment on collections spend. And it helps you make informed decisions about:

  • Where to focus collections efforts
  • Which team member should perform the collection
  • How to maximize collections while minimizing the cost to collect

It’s easy to configure the dynamic credit and collections engine to work alongside existing systems. Key benefits include:

  • Improved DSO by 5% to 15%
  • Reduced past-due invoices to less than 1% and sustainedperformance
  • More working capital
  • Collections efficiency improved by up to 40%
  • Collections costs reduced collections by 20% to 30%

That’s a sure way to meet your company’s core credit and collections objectives.

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