Case Study

How bots are relieving a global bank’s reporting woes . . .

. . .making it agile and freeing up workers’ time to add value

  • Facebook
  • Twitter
  • Linkedin
  • Email

Who we worked with

A major bank with operations in financial centers around the world. 

What the company needed

A more efficient legal entity reporting (LER) function that harnesses opportunities for robotic process automation (RPA) in internal, external, and regulatory reporting processes.

How we helped

We worked with our client to reimagine its LER function by assessing, standardizing, and simplifying its processes and clustering activities. We used our Intelligent Automation Index to identify where RPA could have the greatest impact, and helped the company re-engineer its processes to standardize reporting.

What the company got

When all cylinders are firing, 65 bots will manage rules-based processes across the LER function, resulting in up to 60% automation, up to 75% faster cycle times, and the liberation of at least 12 highly skilled staff to pursue value-added work.

Compliance with a rapidly expanding list of statutory and regulatory reporting requirements is a critical function for all financial institutions. Global banks also face another challenge: reporting across myriad combinations of activities and jurisdictions.

The growing scale and complexity of reporting is diverting highly-skilled staff to manual operations, creating efficiency bottlenecks and undercutting performance. But digital transformation can help, as technologies like RPA are able to take over repetitive, rules-based tasks.


Liberate skilled staff from manual tasks to focus on value creation

Our client, a global diversified bank with operations in every major financial center in the world, wanted to make its legal entity reporting process more efficient. Yet as it prepared to meet the demands of a growing list of reports, the institution knew it wasn’t getting the most out of the people in the team.

Staff struggled with too many manual, repetitive tasks. Skilled professionals were handling transactional activities and high-volumes of exceptions, leaving little time for analysis. While the company wanted to automate many of these processes using its RPA platform, it needed custom-programmed bots to handle each task. But first it had to prioritize, optimize, and prepare the underlying processes for automation.

Take a copy for yourself

Download PDF

The firm’s key challenges included:

Disparate data sources
Before we came on board, staff had to manually extract data from multiple sources using different formats. Then they had to transform and manipulate the data using ineffective tools, such as Excel, and upload content into reporting systems.

Inadequate data quality
The team had the arduous task of individually resolving data quality issues and designing workarounds. It was the only way to meet the complex requirements of multiple legal entities and different reporting systems.

Obstacles to standardization
The bank wanted greater consistency, but requirements and processes varied across products and entities, making it difficult to apply consistent approaches and implement robust controls.


A three-step approach

Digital transformation is not just about technology. Taking advantage of today’s innovations and preparing for continuous improvement requires standardized and optimized operations. Companies must understand the root causes of inefficiencies before designing automation solutions. So we took a three-step approach: assess, design, and build.

We applied Genpact’s Intelligent Automation Index to pinpoint where automation has the most potential in LER processes. The assessment took into account many aspects of existing processes, including the levels of standardization, criticality, and dependence on IT systems. While highlighting the potential for RPA, it also examined the other  building blocks of intelligent automation, including artificial intelligence (AI). 

Our subject matter experts collaborated with key client stakeholders across different entities and functions to standardize and simplify an array of existing reporting systems. We identified the processes with the most potential for simplification and automation, including opportunities for straight-through processing, while also prioritizing those with the best ROI. Another goal was to identify clusters of activities where robotic code could be replicated and re-used.

We focused on six key activity clusters:

  • Extraction of data from feeder systems
  • Reconciliation of data across reporting systems
  • Data enrichment using standard assumptions
  • Exception handling
  • Uploading of enriched data to the reporting tool and realtime dashboards for financial control 
  • Delivery of analytics and commentary based on the unique needs of different user groups

By pinpointing the tasks within each cluster in the RPA platform, we were able to quickly replicate them across different reports and accelerate the RPA roll out. 

We implemented the process re-engineering strategy that we identified in the design phase by creating RPA objects that could be replicated across various reports, products, and organizational entities. As we deployed new bots, each one was rigorously tested, first by our technical experts and then though a systematic program of user acceptance testing. When the automated processes went live, we activated our hypercare maintenance framework to handle ongoing change requests and stay compliant with service-level agreements.

As robotics automates financial report production, bots work in a hierarchy, with supervisor and worker roles. Supervisor bots allocate and manage work based on bot availability, how critical the activity is, and what other tasks depend on it. Each worker bot has a profile in the ERP that the supervisor uses when allocating work.

This holistic, end-to-end approach establishes the most effective scope for RPA implementation. It also allocates systems to clusters where bots can be replicated while meeting the individual needs of each process and report. This approach guided the creation of replicable custom-coded objects for the RPA platform, which reduced the development effort considerably. With 20 usable codes we reduced the effort spent on development and testing by around 15%.


Accelerating the digital journey across the organization

The organization has 65 bots in various stages of development, testing, and implementation. And once fully established, our client can look forward to:

  • Automation of up to 60% of manual tasks
  • Process cycle times up to 75% faster
  • Over 90% fewer exceptions caused by data quality issues
  • Potential cost savings of up to 30% for reporting when at peak capacity
  • Enhanced process transparency and audits
  • Clear dashboards to highlight changes made and flag the exceptions the team must deal with

The impact of RPA deployment goes beyond the reporting function, as appetite for digital transformation is growing across various processes. Genpact continues to work with the finance leadership team to deliver further innovation.

Next steps will see the financial services firm introduce technologies such as AI and machine learning to enable intelligent automation.