Altair > Case Studies > Advancing RPA Initiatives in Financial Reporting: A Case Study of a Global Bank

Advancing RPA Initiatives in Financial Reporting: A Case Study of a Global Bank

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 Advancing RPA Initiatives in Financial Reporting: A Case Study of a Global Bank - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Robotic Process Automation (RPA)
  • Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries
  • Buildings
  • Finance & Insurance
Use Cases
  • Leasing Finance Automation
  • Time Sensitive Networking
The Challenge
The global bank, based out of North America, was facing a significant challenge in managing its report repository connected to dozens of applications and database systems used across the enterprise. The bank, serving close to 20 million customers worldwide, had to manually download hundreds of thousands of reports from these applications to a centralized location for use on a weekly basis. The file formats were typically unstructured data, usually in text or PDF, with no consistency in report formats across the different applications, or even for reports created using the same application. End users would then manually copy data from the text / PDF formats to Excel-based reports used for reconciliation, attestation, financial reporting, journal entries and other uses. This process was time-consuming, prone to human error, and inefficient.
The Customer
About The Customer
The customer in this case study is a global bank based out of North America. The bank serves close to 20 million customers worldwide, has more than 85,000 employees, and has a net asset value of close to $1.5 trillion with annual revenues of approximately $50 billion. It is a member of the Financial Stability Board’s list of global systemically important banks. The bank was using a report repository connected to dozens of applications and database systems used across the enterprise, from different geographies and multiple business units. The bank was facing challenges in managing and standardizing these reports, which were manually downloaded and processed.
The Solution
The bank turned to Altair to add automatic report modeling and standardization to its Robotic Process Automation (RPA) architecture. The team created a 'bot' to interact with Altair® Monarch®, a software previously used in large projects that reduced reconciliation tasks by more than 1500 hours per month. The bot was designed to automate the download of relevant application and database files from the central file repository to a target folder and complete the bulk extraction of data from those files. Altair Monarch Server Automation Edition was used to automate the transformation of this extracted data into thousands of standardized report formats that meet end-user and regulatory requirements. Using its no-code interface, users could quickly design data models that define the report structures they need, and set up the automation processes so that newly extracted data is made consistent with reporting requirements.
Operational Impact
  • The implementation of Altair Monarch to augment the bank's RPA solution resulted in significant operational improvements. The bank was able to save over 3000 person-hours per month, freeing up personnel from the shared finance services team to work on other pressing business architecture and analytic tasks. The self-service capabilities of Monarch also allowed the bank’s IT team to focus their attention on a major new security initiative. The bank designed its implementation to make it easy to scale up as needed to support its growing customer base, demonstrating the solution's flexibility and scalability. The reduction in manual tasks also likely improved the accuracy and consistency of the bank's reports, reducing the risk of human error.
Quantitative Benefit
  • Saved over 3000 person-hours per month
  • Freed up personnel from the shared finance services team to work on other pressing business architecture and analytic tasks
  • Reduced the amount of IT engineering time required to support the financial analyst team by more than 300%

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