Revolutionizing Financial Document Review with AI

Upcore developed an automated document review solution leveraging unsupervised machine learning for a leading U.S. multinational banking and financial services client. The COiN (Continuous Optimization and Incrementalization) platform was implemented to automate analyzing complex financial documents, extracting relevant information accurately while handling industry jargon. This solution delivered substantial results, including 10% cost reduction within 6 months by minimizing labor hours and errors, enhanced operational efficiency, exceptional customer experience with faster turnarounds, scalability to increasing volumes, maintained regulatory compliance, and a competitive edge by adopting cutting-edge AI technology. Technologies utilized spanned machine learning frameworks like COiN, TensorFlow, programming languages, containerization with Docker, databases, and DevOps tools.

Client:

Our client is one of the most successful multinational investing banking and financial services companies in the United States, with over 10,000 employees serving a vast number of customers across multiple regions. Upcore Technologies was engaged to create automated solutions using Artificial Intelligence (AI) and Machine Learning (ML) in their document review process to reduce billable hours, minimize human errors, and ultimately save their customers significant costs while improving overall efficiency and customer satisfaction.

Challenge:

The primary challenge was determining the most suitable type of machine learning approach that would work best for our client's requirements to create an automated document review process solution. The client's document review process involved analyzing and extracting relevant information from a wide range of financial documents, including loan applications, mortgage documents, investment portfolios, and regulatory filings. These documents often contained complex legal jargon, technical terminologies, and industry-specific nomenclature, making the review process time-consuming and prone to errors when performed manually.

Our experts conducted extensive brainstorming sessions, researched various machine learning techniques, and evaluated their suitability for the client's specific needs. After a thorough analysis, they concluded that the COiN (Continuous Optimization and Incrementalization) platform, an unsupervised machine learning technique, would effectively solve all their problems.

The COiN platform's unsupervised learning approach allowed it to automatically identify patterns, extract relevant information, and continuously improve its accuracy without the need for extensive manual labeling or training data. This capability made it an ideal choice for the client's document review process, where the documents could vary significantly in format, structure, and content.

Solution: 

1. Build a customized website:

Upcore developed a customized website for the client, enabling seamless access and integration with the automated document review solution. The website served as a central hub for users to upload documents, initiate review processes, monitor progress, and retrieve results.

2. Automated deployment:

To streamline the deployment process and ensure continuous delivery of new features and updates, Upcore implemented a robust CI/CD (Continuous Integration/Continuous Deployment) pipeline. This involved source control management, automated build and packaging processes, and orchestrated deployment workflows.

3. Implement security and CIS Benchmark:

To enhance the platform's security posture and ensure compliance with industry best practices, Upcore implemented the CIS (Center for Internet Security) Benchmark. This involved configuring secure network architectures, access controls, and monitoring capabilities to protect the system and client data.

Results:

After implementing the solutions, Upcore observed and drew conclusions based on the outcomes. We can confidently state that the firm made massive progress after incorporating the COiN platform to automate its document review process.

1. Cost Reduction:

Our client's company succeeded in cutting down costs by 10% in only 6 months, thanks to the automated document review process powered by machine learning. This significant cost reduction was achieved by reducing labor hours associated with manual document review and minimizing human errors that could lead to costly mistakes or rework.

2. Enhanced Operational Efficiency:

The automated document review process streamlined operations, increasing efficiency and productivity within the organization. Tasks that previously required manual intervention by teams of legal professionals and subject matter experts were now handled seamlessly by the machine learning algorithms, freeing up valuable resources for other critical operations. The COiN platform's ability to continuously learn and improve its accuracy further enhanced the efficiency of the document review process over time.

3. Exceptional Customer Experience:

By leveraging machine learning and automation, our client could deliver a phenomenal customer experience. The accurate and timely processing of documents ensured that customer requests and transactions were handled promptly and efficiently, fostering trust and satisfaction. Customers experienced faster turnaround times, reduced errors, and a more streamlined experience overall.

4. Scalability and Adaptability:

The COiN platform's unsupervised machine learning capabilities enabled the client to scale their document review process effortlessly as their business grew and document volumes increased. Additionally, the system's ability to continuously learn and adapt ensured that it remained relevant and effective in the face of changing regulatory requirements, document formats, or industry trends.

5. Regulatory Compliance:

The financial services industry is subject to stringent regulations and compliance requirements. The automated document review process, powered by machine learning, helped our client maintain compliance by accurately identifying and extracting relevant information from regulatory filings, legal documents, and other compliance-related materials. This reduced the risk of non-compliance and associated penalties or legal issues.

6. Competitive Advantage:

By adopting cutting-edge technologies like machine learning, our client gained a significant competitive advantage in the market. They were able to offer more efficient and cost-effective services to their customers, while also demonstrating their commitment to innovation and technological advancement.

Technologies Used:

The following technologies and services were utilized in the implementation of the automated document review solution:

• - Machine Learning: Continuous Optimization and Incrementalization (COiN) platform for unsupervised machine learning, as well as other ML libraries and frameworks such as TensorFlow, scikit-learn, and spaCy for natural language processing tasks.
  - Programming Languages: Python, Java, and Node.js (for web application development).
  - Containerization: Docker for packaging and deploying applications.
  - Databases: MySQL, PostgreSQL, and MongoDB (depending on the specific requirements).
  - DevOps Tools: Jenkins, Ansible, and Terraform (for CI/CD pipeline and infrastructure automation).
  
  

By leveraging Upcore's expertise in machine learning and financial services domain knowledge, our client successfully implemented an automated document review process that significantly reduced costs, improved operational efficiency, and delivered an exceptional customer experience. The solution's scalability, adaptability, and regulatory compliance capabilities ensure that it remains a valuable asset for the client's long-term business growth and success in the highly competitive and regulated banking and financial services industry.