Transforming credit decisions using generative AI with Rich Data Co and AWS

This post is co-written with Gordon Campbell, Charles Guan, and Hendra Suryanto from RDC. 

The mission of Rich Data Co (RDC) is to broaden access to sustainable credit globally. Its software-as-a-service (SaaS) solution empowers leading banks and lenders with deep customer insights and AI-driven decision-making capabilities.

Making credit decisions using AI can be challenging, requiring data science and portfolio teams to synthesize complex subject matter information and collaborate productively. To solve this challenge, RDC used generative AI, enabling teams to use its solution more effectively:

• Data science assistant – Designed for data science teams, this agent assists teams in developing, building, and deploying AI models within a regulated environment. It aims to boost team efficiency by answering complex technical queries across the machine learning operations (MLOps) lifecycle, drawing from a comprehensive knowledge base that includes environment documentation, AI and data science expertise, and Python code generation.
• Portfolio assistant – Designed for portfolio managers and analysts, this agent facilitates natural language inquiries about loan portfolios. It provides critical insights on performance, risk exposures, and credit policy alignment, enabling informed commercial decisions without requiring in-depth analysis skills. The assistant is adept at high-level questions (such as identifying high-risk segments or potential growth opportunities) and one-time queries, allowing the portfolio to be diversified.

In this post, we discuss how RDC uses generative AI on Amazon Bedrock to build these assistants and accelerate its overall mission of democratizing access to sustainable credit.

Solution overview: Building a multi-agent generative AI solution

We began with a carefully crafted evaluation set of over 200 prompts, anticipating common user questions. Our initial approach combined prompt engineering and traditional Retrieval Augmented Generation (RAG). However, we encountered a challenge: accuracy fell below 90%, especially for more complex questions.

To overcome the challenge, we adopted an agentic approach, breaking down the problem into specialized use cases. This strategy equipped us to align each task with the most suitable foundation model (FM) and tools. Our multi-agent framework is orchestrated using LangGraph, and it consisted of:

1. Orchestrator – The orchestrator is responsible for routing user questions to the appropriate agent. In this example, we start with the data science or portfolio agent. However, we envision many more agents in the future. The orchestrator can also use user context, such as the user’s role, to determine routing to the appropriate agent.
2. Agent – The agent is designed for a specialized task. It’s equipped with the appropriate FM for the task and the necessary tools to perform actions and access knowledge. It can also handle multiturn conversations and orchestrate multiple calls to the FM to reach a solution.
3. Tools – Tools extend agent capabilities beyond the FM. They provide access to external data and APIs or enable specific actions and computation. To efficiently use the model’s context window, we construct a tool selector that retrieves only the relevant tools based on the information in the agent state. This helps simplify debugging in the case of errors, ultimately making the agent more effective and cost-efficient.

This approach gives us the right tool for the right job. It enhances our ability to handle complex queries efficiently and accurately while providing flexibility for future improvements and agents.

The following image is a high-level architecture diagram of the solution.

Data science agent: RAG and code generation

To boost productivity of data science teams, we focused on rapid comprehension of advanced knowledge, including industry-specific models from a curated knowledge base. Here, RDC provides an integrated development environment (IDE) for Python coding, catering to various team roles. One role is model validator, who rigorously assesses whether a model aligns with bank or lender policies. To support the assessment process, we designed an agent with two tools:

1. Content retriever tool – Amazon Bedrock Knowledge Bases powers our intelligent content retrieval through a streamlined RAG implementation. The service automatically converts text documents to their vector representation using Amazon Titan Text Embeddings and stores them in Amazon OpenSearch Serverless. Because the knowledge is vast, it performs semantic chunking, making sure that the knowledge is organized by topic and can fit within the FM’s context window. When users interact with the agent, Amazon Bedrock Knowledge Bases using OpenSearch Serverless provides fast, in-memory semantic search, enabling the agent to retrieve the most relevant chunks of knowledge for relevant and contextual responses to users.
2. Code generator tool – With code generation, we selected Anthropic’s Claude model on Amazon Bedrock due to its inherent ability to understand and generate code. This tool is grounded to answer queries related to data science and can generate Python code for quick implementation. It’s also adept at troubleshooting coding errors.

Portfolio agent: Text-to-SQL and self-correction

To boost the productivity of credit portfolio teams, we focused on two key areas. For portfolio managers, we prioritized high-level commercial insights. For analysts, we enabled deep-dive data exploration. This approach empowered both roles with rapid understanding and actionable insights, streamlining decision-making processes across teams.

Our solution required natural language understanding of structured portfolio data stored in Amazon Aurora. This led us to base our solution on a text-to-SQL model to efficiently bridge the gap between natural language and SQL.

To reduce errors and tackle complex queries beyond the model’s capabilities, we developed three tools using Anthropic’s Claude model on Amazon Bedrock for self-correction:

1. Check query tool – Verifies and corrects SQL queries, addressing common issues such as data type mismatches or incorrect function usage
2. Check result tool – Validates query results, providing relevance and prompting retries or user clarification when needed
3. Retry from user tool – Engages users for additional information when queries are too broad or lack detail, guiding the interaction based on database information and user input

These tools operate in an agentic system, enabling accurate database interactions and improved query results through iterative refinement and user engagement.

To improve accuracy, we tested model fine-tuning, training the model on common queries and context (such as database schemas and their definitions). This approach reduces inference costs and improves response times compared to prompting at each call. Using Amazon SageMaker JumpStart, we fine-tuned Meta’s Llama model by providing a set of anticipated prompts, intended answers, and associated context. Amazon SageMaker Jumpstart offers a cost-effective alternative to third-party models, providing a viable pathway for future applications. However, we didn’t end up deploying the fine-tuned model because we experimentally observed that prompting with Anthropic’s Claude model provided better generalization, especially for complex questions. To reduce operational overhead, we will also evaluate structured data retrieval on Amazon Bedrock Knowledge Bases.

Conclusion and next steps with RDC

To expedite development, RDC collaborated with AWS Startups and the AWS Generative AI Innovation Center. Through an iterative approach, RDC rapidly enhanced its generative AI capabilities, deploying the initial version to production in just 3 months. The solution successfully met the stringent security standards required in regulated banking environments, providing both innovation and compliance.

“The integration of generative AI into our solution marks a pivotal moment in our mission to revolutionize credit decision-making. By empowering both data scientists and portfolio managers with AI assistants, we’re not just improving efficiency—we’re transforming how financial institutions approach lending.”

–Gordon Campbell, Co-Founder & Chief Customer Officer at RDC

RDC envisions generative AI playing a significant role in boosting the productivity of the banking and credit industry. By using this technology, RDC can provide key insights to customers, improve solution adoption, accelerate the model lifecycle, and reduce the customer support burden. Looking ahead, RDC plans to further refine and expand its AI capabilities, exploring new use cases and integrations as the industry evolves.

For more information about how to work with RDC and AWS and to understand how we’re supporting banking customers around the world to use AI in credit decisions, contact your AWS Account Manager or visit Rich Data Co.

For more information about generative AI on AWS, refer to the following resources:

About the Authors

Daniel Wirjo is a Solutions Architect at AWS, focused on FinTech and SaaS startups. As a former startup CTO, he enjoys collaborating with founders and engineering leaders to drive growth and innovation on AWS. Outside of work, Daniel enjoys taking walks with a coffee in hand, appreciating nature, and learning new ideas.

Xuefeng Liu leads a science team at the AWS Generative AI Innovation Center in the Asia Pacific regions. His team partners with AWS customers on generative AI projects, with the goal of accelerating customers’ adoption of generative AI.

Iman Abbasnejad is a computer scientist at the Generative AI Innovation Center at Amazon Web Services (AWS) working on Generative AI and complex multi-agents systems.

Gordon Campbell is the Chief Customer Officer and Co-Founder of RDC, where he leverages over 30 years in enterprise software to drive RDC’s leading AI Decisioning platform for business and commercial lenders. With a proven track record in product strategy and development across three global software firms, Gordon is committed to customer success, advocacy, and advancing financial inclusion through data and AI.

Charles Guan is the Chief Technology Officer and Co-founder of RDC. With more than 20 years of experience in data analytics and enterprise applications, he has driven technological innovation across both the public and private sectors. At RDC, Charles leads research, development, and product advancement—collaborating with universities to leverage advanced analytics and AI. He is dedicated to promoting financial inclusion and delivering positive community impact worldwide.

Hendra Suryanto is the Chief Data Scientist at RDC with more than 20 years of experience in data science, big data, and business intelligence. Before joining RDC, he served as a Lead Data Scientist at KPMG, advising clients globally. At RDC, Hendra designs end-to-end analytics solutions within an Agile DevOps framework. He holds a PhD in Artificial Intelligence and has completed postdoctoral research in machine learning.

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