Ensuring Compliance and Efficiency in Transaction Reporting
The FR 2052a regulation mandates that large financial institutions provide accurate, transaction-level data to monitor their liquidity positions. This regulation is crucial for maintaining financial stability and market transparency. However, current manual processes in extracting and reconciling data from PDFs and spreadsheets are inefficient and prone to errors, posing challenges to regulatory compliance. The Risk Analyst Co-Pilot utilizes AI-driven automation to simplify these processes, reducing time and enhancing data accuracy.
Problem Statement
Financial institutions face significant challenges in complying with FR 2052a due to
- Manual extraction of transaction data from PDF statements.
- Time-consuming reconciliation of this data against source databases using spreadsheets.
- Heavy reliance on human effort, leading to potential errors, inconsistencies, and inaccuracies in the reported data.
- Difficulty in ensuring full regulatory compliance due to inefficiencies in the current manual workflow.
Solution
The Risk Analyst Co-Pilot for FR 2052a leverages advanced generative AI technology to automate data extraction and reconciliation processes, ensuring more accurate and efficient reporting. With this Co-Pilot, institutions can upload PDFs, automatically extract and match transaction data to databases, and generate downloadable reports for streamlined compliance. It supports batch processing, enabling high-volume document handling and making it easier to meet regulatory timelines. The solution also uses secure data masking and encryption, safeguarding sensitive financial information throughout the workflow.
Key Features
Application
A Generative AI application developed with a foundation in Google Geminai multi-modal technology.
Input/Output:
Input/Output:
- Users can upload PDF documents through an intuitive AngularJS GUI interface.
- The application generates a downloadable reconciliation report.
- Supports batch jobs for bulk data uploads, allowing for efficient processing at scale.
Process
- The application uses prompt engineering and agents to extract specified content from PDFs and reconcile this data against database entries.
- All sensitive data is masked and encrypted to ensure compliance with security standards before being processed by the LLM.
Technology Stack
- Prompt Engineering with Agents
- Google Geminai
- Langchain
- FastAPI
- AngularJS
- Red Hat OpenShift
- HashiCorp Vault (for data encryption and masking)
Benefits
Efficient Data Extraction and Reconciliation
Automates the previously manual task of extracting and reconciling transaction data, saving time and reducing the risk of human error.
Improved Regulatory Compliance
By ensuring data accuracy and consistency, the system enhances compliance with the FR 2052a regulation, helping financial institutions avoid penalties.
Increased Processing Speed and Accuracy
The automated process improves overall speed and accuracy, reducing error rates by 20%.