Client: Equipment finance major
Industry: Financial services
Business need addressed: Increase the percentage of payments auto applied
Genpact solution: Data analysis, cleansing, creation of two-layered auto-application algorithm, and process improvement
- Higher auto cash application rate
- Reduction in manual cash application, which leads to a more streamlined process
A leading equipment financier received more than half of its payments as checks, which were then auto-applied using an algorithm. Although the auto-application logic was well-built, it still resulted in a 15% rejection rate, amounting to 40,000 open items per month. Each month, these open items had to be applied manually, which was inefficient, created an additional cost, and caused controllership issues.
Although it was imperative to streamline the cash application process (including the technology), changing the batch algorithm on the client legacy system was not an option.
Genpact undertook a joint initiative and ran a Lean Six Sigma (LSS) project
- More than 40,000 records (100% of the sample) were analyzed at three different levels (statistical, trend, and business analysis)
- Genpact captured 36 possible causes across 9 different processes
- Based on the analysis, a three-step solution was implemented:
- Conduct a one-time data analysis and cleansing
- Build a second layer to the cash application algorithm that was added to the internal tool and do not modify the primary batch algorithm
- Process rejects from the batch algorithm through enhanced auto cash application logic, which will reduce the number of manual transactions required of the cash team
- Internal process flows and hand-offs elsewhere were streamlined, and policy changes were recommended, which had a great additional impact
Business impact delivered
The project resulted in an additional $96 million in cash annually applied through automated application processing. The expansion of this program, along with suggested changes to process flows and hand-offs, provides the potential to further increase that amount to $384 million annually, thereby creating additional cost savings by reducing manual processing.