Leveraging AI for Financial Data Extraction
By utilizing Generative AI, we automated the data extraction process for 3,000 to 7,000 financial documents each year for a global professional services firm, resulting in an annual savings of 3,100 hours.
🔸 The Challenge
Our client, a worldwide professional services provider across various sectors, faced difficulties in extracting and analyzing data from a large volume of financial reports. Their main requirement was to extract specific fields from these documents, a labor-intensive process that demanded considerable time and resources.
🔸 Solution
Collaborating with our client’s team, CloudX harnessed Generative AI to create an advanced data pipeline focused on extracting and analyzing information from financial reports. We employed Azure OpenAI to produce a classification dataset, which was used to train a classification model utilizing embeddings and Random Forest classification. This model reliably categorizes new, unseen documents. We then designed a solution using Retrieval-Augmented Generation (RAG) along with prompts tailored for extracting accurate fields from the categorized documents. After that, we conducted iterative User Acceptance Testing (UAT). Additionally, we developed a user interface (UI) integrated into Microsoft Teams, enabling users to manage the extracted fields, make manual edits, and adjust the prompts to enhance the extraction process. Once the necessary data is compiled, it can be exported as a CSV file.
🔸 Results
The deployment of our AI-driven solution marked a significant achievement for our client, resolving a challenge they had faced for almost a decade. Building on this achievement, we are now expanding the solution’s capabilities to include an agreement analysis tool. Development and launch of a minimum viable product (MVP) took 8 months, processing 3,000 to 7,000 documents annually and saving 3,100 hours each year.