June 26, 2024

Case Study: Sinpex Provides AI-Powered KYB Automation Technology Using Custom Gradient LLMs

Case Study: Sinpex Provides AI-Powered KYB Automation Technology Using Custom Gradient LLMs

Overview

In the fast-paced world of financial services, financial institutions are constantly seeking innovative ways to enhance efficiency and ensure regulatory compliance. For many, KYB (Know Your Business) is one of the most challenging processes to manage due to the role it plays in preventing fraud while maintaining regulatory compliance and efficient corporate client onboarding. In an effort to enhance this process, Sinpex partnered with Gradient to develop an AI-powered data extraction automation within the KYB process - creating significant ROI and ease-of-use for Sinpex customers.

The Challenges

One of the main challenges in KYB efforts is extracting data and information from official documents and comparing it with the data and self-declarations provided by the customer. Moreover, performing this step manually, as most companies do, is highly prone to errors and leads to significant quality issues. While the use of AI seemed like a viable solution to reduce the noticeable friction, getting there proved to be more difficult than anticipated.

  • Unstructured Data: The team needed a way to handle different documents from official registries, all with a different structure. Some in a very poor quality and difficult-to-read and some totally unstructured like e.g. shareholder lists.  Primarily in PDF format - making data extraction even more challenging.
  • Inaccuracy in LLMs:  The existing Large Language Models (LLMs) are not well-suited for KYB/Compliance topics, resulting in inaccuracies. Making it difficult to rely on the responses generated for KYB-related queries where even the slightest error could lead to significant compliance issues.

These challenges are not only straining on resources, but it also poses a risk to the team’s demand for complete smart automation and operational efficiency.

The AI-Driven Solution

Working together with Gradient, we concentrated on tackling the challenges directly and finding a way to seamlessly integrate the solution into our existing infrastructure. Within just two weeks, we successfully launched the initial implementation with minimal effort required from our team. The quick time to value was crucial for us, as it allowed us to focus on other business priorities.

Automated Data Extraction

The Sinpex Team's first step of the process involved deploying an AI-powered tool specifically designed to parse and extract data from the unstructured PDF documents. This tool utilized advanced machine learning algorithms to identify, extract, and organize data from these documents - turning a previously manual and error-prone process into a streamlined, automated workflow.

Custom Gradient LLM for KYB

To address the issue of accuracy in KYB responses, Sinpex leveraged Gradient’s AI Foundry to develop a custom LLM that could fulfill the KYB requirements that their customers frequently encounter. This model was trained on a vast dataset of regulatory compliance materials and commercial register excerpts and shareholder lists to ensure that it could provide precise and reliable answers to KYB queries without the risk of hallucination. Hallucinations generally occur when an LLM generates a response that is either factually incorrect, nonsensical, or disconnected from the input prompt.

Integration into Existing Infrastructure

To ensure a smooth transition and minimal disruption to our team, Gradient integrated the solution seamlessly into our existing GCP stack. This way Sinpex could leverage its existing infrastructure, while benefiting from the advanced capabilities of Gradient's platform for our solution.

The Impact

  • Improved Response Capabilities: Likelihood of the system not being able to respond (e.g. returning no answer to the user) decreased from 45% to 1%. This means users can expect a response to their question 99% of the time, enabling a better customer experience.
  • Increased Accuracy: Number of correct answers improved by 30 percentage points, drastically reducing the need to modify the answer provided in comparison to before. The precision of the answers provided also improved by 35 percentage points, drastically reducing the work required for the team. 
  • Expanded Coverage on KYB Related Questions: Customers are now able to ask any question that they might have via the KYB Questionnaire. Previously, Q&A was limited to predefined questions that were established via KYB documents. With the expanded coverage, customers are now able to receive more clarity and resolutions at their own convenience. 

"Partnering with Gradient was an important step forward for us. The impact of their custom AI-powered solutions on our KYB processes has been transformative. Not only have we seen a significant improvement in accuracy and efficiency, but the ease of integration into our existing infrastructure was seamless, allowing us to leverage our current GCP stack without disruption. Working with the Gradient team was perfect - they were responsive, knowledgeable, and truly committed to our success. This collaboration has enabled us to deliver a better customer experience and achieve operational excellence in a remarkably short timeframe", says Camillo Werdich, founder and CEO of Sinpex.

The Conclusion

The work between Sinpex and Gradient showcases the transformative potential of AI to help create better customer experiences and ensure a safer financial ecosystem. By automating data extraction and using a fine-tuned model that can meet the specific needs of their KYB processes, Sinpex not only enhanced its compliance accuracy but also achieved significant operational efficiencies. This case study serves as a compelling example of how AI can address complex challenges in the financial services industry, paving the way for more innovative and efficient compliance solutions in the future.

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