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Automation of DFAST Processes Saves Days of Manual Work
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
- Functional Applications - Enterprise Resource Planning Systems (ERP)
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
- Quality Assurance
Use Cases
- Process Control & Optimization
- Regulatory Compliance Monitoring
Services
- Software Design & Engineering Services
- System Integration
The Challenge
A large bank was collecting and managing DFAST-related data from multiple sources using uncontrolled Excel spreadsheets before manually aggregating all received data. The collection of uncontrolled spreadsheets through email proved extremely challenging. It was virtually impossible to confirm the accuracy of the data because any user could add, delete or alter spreadsheets. Files were stored in a bulky, multi-tab workbook that was at risk of becoming too large and complex to function. Otherwise, files were stored in locked-down shared drives. The bank needed a solution to streamline the collection and analysis of DFAST data, ensure data accuracy, and comply with regulatory requirements.
About The Customer
The customer is a large bank that was facing significant challenges in managing DFAST-related data. The bank's data analysts were responsible for collecting information from regression models, other analysts, and various lines of business, and inputting everything into spreadsheets. This process was highly manual and time-consuming, involving several iterations to aggregate all necessary data. The data from any one line item could come from up to four regression models, requiring the team to break down a single line into four pieces before aggregating the data. The aggregated data then needed to go through multiple review stages, including line of business review, executive committee review, and bank board committee review. Qualitative adjustments were often made at the review stage, and the team had to manually paste data into a separate workbook to perform adjustments and compare historical data with macroeconomic factors. This cumbersome process took time away from the team's core roles and made it difficult to ensure data accuracy and compliance with Fed requirements.
The Solution
Vena optimized the data collection process by breaking up a single Excel file into smaller, more manageable files. Data from these files are automatically rolled into a central data repository. The repository stores multiple versions of templates, including Fed scenarios, bank scenarios, etc., in addition to multiple versions of each scenario and the results of what-if analyses. The graphical process designer can break processes into a series of repeatable steps that include instructions, due dates, and the ability for contributors to attach supporting documentation to their submission. Vena automatically notifies managers and contributors via email when a step has been completed and associated templates and notes are available for review. Vena’s audit trail records user activity (who did what, when), allowing the firm to instantly differentiate between qualitative adjustments and model-driven entries. Vena’s playground environment has business rules and other features that allow users to adjust assumptions and immediately visualize their effects on a projection scenario.
Operational Impact
Quantitative Benefit
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