Solution Overview

Reimagine cash applications with digital to boost accuracy and avoid revenue loss

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Each year, according to Genpact estimates, global companies spend $50–$60 million on processing cash applications manually. This often puts data integrity at risk, leads to loss of revenue, and results in poor customer experiences. Advanced digital technologies enable process automation despite challenges with data, inconsistent policies, and fragmented processes. Enabling zero-touch processing can deliver significant efficiency gains, and 100% accurate payment accounting.

Business challenges

 As customers place higher volumes of orders, a company’s cash applications process needs to be agile and respond quickly to realize revenues. For example, if cash is not applied swiftly to a customer that makes multiple orders, its credit limit will not be up to date and future orders will not go through until the finance team recognizes the payment. Not only does this lead to unnecessary resolution costs, but it also has a negative impact on the customer experience and can result in cancelled orders and lost revenues. 

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Achieving accurate cash applications is extremely difficult as processes can be highly manual and time consuming. Back-office support activities—for instance, exception handling, preparing refunds and direct debit proposals, generating customer statements, responding to collection queries, and reconciliations—tend to take 30%–50% of a team’s efforts, which delays payment posting, and significantly affects accounting efficiency. Despite the opportunities for improvement and cost reduction, organizations often fail to automate the cash applications process because of:

Unstructured payment-related data from disparate sources: Data can come into the accounts receivable (AR) team from various sources and in different formats, through invoices and payment methods, bank statements, remittance advice information, and credit notes. Payment-related information is not only unstructured, but often also delayed and incomplete. AR teams have limited control over input-data quality as they rely on information flowing from external and internal stakeholders.

Complex changing policies: Due to unwieldy and rigid ERP systems, policy changes, acquisitions, or other business and operational activities, it is difficult for AR to rapidly on-board changes to business rules.

Fragmented and inflexible legacy systems: Existing ERP and systems of record are complex, time-consuming, and expensive to modify or customize as they require structured datasets. As a result, AR teams resort to stop-gap, manual fixes, or use spreadsheets to support payment processing requirements.

Manual and time-consuming payment processes: Manual non-posting activities consume up to 50% of cash-applications effort. Invoice and payment-processing exceptions also require extensive research to resolve while teams coordinate varying approaches to payment processing. These manual methods are lengthy, prone to errors, and lead to delayed or cancelled orders, and poor customer experiences.

As process automation transforms cash applications, organizations that overlook this opportunity are putting transactional and master payment data integrity, revenues, and customer satisfaction at risk.

Genpact solution

Genpact’s Touchless Cash Application Engine automates the receipt of payment information, payment processing, and related AR activities, by leveraging advanced digital technologies, such as machine learning, natural language processing (NLP), robotic process automation and dynamic workflow as shown in figure 1. The cloud-based solution offers best-in-class usability, can be easily configured to work alongside existing technology solutions, and requires minimum time and cost to deploy.

Figure 1: Enabling zero-touch transactions through the power of digital


Key features include:

Payment information ingestion: Using NLP and intelligent optical character recognition, the system can receive data in any format and analyze patterns to extract actionable data and context. It also reduces the dependency on input standardization.

Payment auto-matching: Through robotic process automation, incremental logic, and probabilistic pattern-based response from an embedded machine-learning algorithm, the solution drives “auto cash”—i.e., payment received from a customer is automatically applied to the relevant open invoice. Plus, machine learning is leveraged to accurately identify exceptions.

Exception management and resolution: If auto matching fails, the embedded dynamic workflow helps resolve exceptions by automating the creation, classification, and routing of exceptions. Automated exception resolution is supported by built-in natural-language-processing algorithms to ingest unstructured data.

Other accounts receivable support and management activities: The system supports additional AR activities, including payment and refund proposals, request for balance statements, or other account information.


Genpact’s Touchless Cash Application Engine is powered by Genpact’s Lean DigitalSM approach that combines design-thinking approaches that focus on the end user with Lean practices and advanced digital technologies and analytics. With over 4,000 cash application professionals working across industries and countries, the solution is underpinned by domain experts with a deep understanding of accounts receivable processes and industry challenges.

The Touchless Cash Application Engine delivers impact against performance goals across multiple industries, including:

  • Automating cash applications by up to 95%, driving productivity gains with low incremental investments
  • Up to 100% accuracy of processing and outputs
  • Shorter transaction cycle times—from days to hours
  • Increased responsiveness to requests, improving the experience of customers and internal stakeholders
  • Improved agility, allowing cash applications teams to respond to changes in policy promptly

By adopting Genpact’s Touchless Cash Application Engine, organizations realize accurate and timely payment accounting while reducing operational costs and lost revenue.

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