Transforming Document Classification for Enhanced Efficiency and Compliance
Client Overview
A leading global investor services group specializes in offering a comprehensive range of consulting solutions for alternative assets and corporate services. With a robust presence in the financial sector, the firm helps clients manage risk, optimize returns, and navigate complex regulatory landscapes. Their clientele spans across a diverse range of industries and sectors, and the firm prides itself on delivering high-quality, bespoke services designed to support their clients' financial strategies and long-term objectives.
Tackling Unclassified Documents and Streamlining Regulatory Compliance
Their legacy systems could not keep up with the growing need for efficient document categorization and compliance with ever-evolving regulatory standards. As a result, critical documents like financial statements, partnership agreements, and lease contracts were scattered across various systems, creating operational inefficiencies and regulatory risks.
The client was focused on delivering exceptional value to their stakeholders by optimizing document management workflows, improving accessibility, and ensuring compliance. The client sought to build an advanced, scalable document classification platform to meet these demands and keep pace with future growth.
Client Requirements:
Streamline Document Classification
Implement a robust system to classify a wide range of unstructured documents, including financial statements, contracts, and agreements, with high accuracy.
Multi-Level Classification Framework
Develop a scalable, multi-level classification model to categorize and subcategorize documents effectively across multiple departments and document types.
On-Premise Deployment for Scalability
Design and deploy an on-premise solution that can scale as the organization grows and can accommodate various types of documents.
Enhanced Data Security & Compliance
Ensure that the classification system complies with stringent global regulatory standards, reducing data risk and ensuring seamless auditing capabilities.
Improve Operational Efficiency
Automate the document categorization process to reduce manual efforts, accelerate workflows, and enhance user productivity.
Implemented Advanced AI and NLP for Robust Document Classification
Assessment of Foundation LLM Models
Indium began by evaluating various large language models (LLMs) for effective document classification. After rigorous testing, Indium identified the most suitable models for the client’s specific requirements, ensuring the solution could accurately classify diverse document types.
Unsupervised Clustering for Initial Feasibility Analysis:
As part of the exploratory phase, an experimental model was built using unsupervised clustering techniques. This step helped analyze the feasibility of classifying a wide variety of documents without initially needing exhaustive labeled datasets, laying the groundwork for the final classification model.
Strategized Roadmap for Classification Approach
A detailed roadmap was developed for implementing a hybrid approach to document classification, incorporating NLP techniques, LLMs (such as LLAMA2, Mistral), and a mix of supervised and unsupervised models. This allowed for flexibility in managing a range of document categories and document sub-classes.
Email Classifier Development
To streamline workflows and improve efficiency in classifying emails, we developed a neural network-based supervised model (LSTM). This classifier was designed to automatically categorize incoming emails related to financial documents and agreements, reducing the need for manual intervention.
2nd Level Classifier
To further enhance accuracy, a second-level classifier was implemented to capture workflows within the emails. This classifier leveraged advanced NLP techniques, with or without the assistance of foundation models (like LLAMA2/Mistral), to detect patterns in document types based on specific criteria, ensuring a deeper level of categorization.
Gen AI Model for Document Summarization
To maximize efficiency and improve user accessibility, Indium utilized Gen AI models built with LLAMA2 and other advanced models to summarize key content from emails. This allowed the system to automatically derive and summarize conclusions, improving the client’s document review processes.
Achieved Accuracy and Accelerated Document Processing with Scalable Solutions
98% Document Classification Accuracy
The solution achieved a 98% classification accuracy, significantly reducing the risk of misclassification and manual oversight.
3.5TB of Documents Indexed and Classified
Over 3.5TB of previously unstructured documents were successfully indexed and classified, improving document accessibility and operational efficiency.
Improved Regulatory Compliance
With a highly efficient and automated classification system, the client ensured that all documents were compliant with global regulatory standards, reducing risk and enhancing transparency.
Accelerated Decision-Making and Efficiency
By automating document categorization and improving document workflows, the client experienced faster processing times, empowering teams to make quicker, data-driven decisions.
Scalable Solution for Future Growth
The solution was designed to scale, ensuring the client could easily accommodate future document management needs as their operations expanded.
About Indium
Indium is an Al-driven digital engineering company that helps enterprises build, scale, and innovate with cutting-edge technology. We specialize in custom solutions, ensuring every engagement is tailored to business needs with a relentless customer-first approach. Our expertise spans Generative Al, Product Engineering, Intelligent Automation, Data & Al, Quality Engineering, and Gaming, delivering high-impact solutions that drive real business impact.
With 5,000+ associates globally, we partner with Fortune 500, Global 2000, and leading technology firms across Financial Services, Healthcare, Manufacturing, Retail, and Technology-driving impact in North America, India, the UK, Singapore, Australia, and Japan to keep businesses ahead in an Al-first world.
