CASE STUDY

AI-Powered Financial
Reporting

Our AI-driven dynamic solution for financial reporting saves days of processing time and enables intuitive interaction through chat-based querying.

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FINANCE

OVERVIEW

Quarterly Finance Reports in Seconds

Our client was spending a lot of time and effort to prepare quarterly financial reports for their investors and was looking for options to automate their process. With the dawn of GenAI solutions, we recommended both a conventional way or a more advanced solution powered by AI and they were very much interested in our GenAI solution. By leveraging advanced machine learning algorithms, our system dynamically creates mapping files based on the files provided by the user, automating the report generation process with the categorization of expenditures and incomes. Additionally, the integration of chat-based querying enhanced user interaction by enabling seamless data retrieval and analysis.

HIGHLIGHTS

No Cost POC to build the client’s confidence of our capabilities
Reduction of processing time by over 80%
Integration of chat-based querying for intuitive data retrieval and analysis
Automation of mapping file creation based on the input files

TOOLS

Tools we Used

PostgreSQL

GPT4

React.js

Python

PROBLEM STATEMENT

The Countless Challenges

Financial data often comes in various formats and structures, posing challenges in standardization and categorization to automatically generate mapping files.
AI models had to be adapted to diverse categorization guidelines and complex financial transactions involving multiple categories or entities and rules provided by the users during the training phase to facilitate the required flexibility and scalability.
In addition to the technical expertise, the level of domain expertise required was also a challenge in developing the solution as the model had to be trained with a lot of existing data, mapping files, and reports by understanding the nuances.
Ensuring compliance with financial regulations and reporting standards such as GAAP.
The system had to process user queries in real-time for efficient data retrieval and analysis with minimal latency during chat-based querying.
The generated reports had to be 100% reliable and accurate down to the penny as even a small deviation in reports with huge values could result in big losses.

PROBLEM STATEMENT

Our Flawless Solutions

Implemented robust data validation and cleansing techniques to ensure data quality and consistency before processing.
Develop custom AI algorithms and use it along with existing frameworks for dynamic mapping and categorization of financial data, ensuring adaptability to diverse guidelines.
Train AI models on large and diverse datasets of financial reports to enhance accuracy and relevance across various scenarios.
Optimize chat-based querying algorithms for real-time processing and efficient data retrieval, minimizing latency and enhancing user experience.
Utilized the DeepEval framework for comprehensive model evaluation and quality assurance to ensure reliable results.
Implement encryption and access controls to ensure compliance with financial regulations and reporting standards, safeguarding data integrity and privacy.
Provide comprehensive user training sessions, tutorials, and documentation to educate users on system functionalities and best practices.