3 Keys to Success with RAG-enabled GenAI for KYC/AML

Data, Models, and Efficiency Are Essential Areas of Focus

This approach delivers big gains in terms of efficiency, accuracy, and adaptability, but there are critical considerations that must be taken into account to maximize the potential of GenAI with RAG while simultaneously mitigating risks and limitations. Broadly, there are three areas that require special attention:

Data

Security and quality are the two guideposts when it comes to data and GenAI with RAG.

For security, it is absolutely essential that sensitive, proprietary data such as personal information or proprietary analytical output be protected at all costs. Robust safeguards for data management, retrieval, and generation are essential.

With regard to quality, care should be taken when it comes to both data ingestion and the handling of conflicting or inconsistent information. In the first case, the best possible data is an absolute requirement as a raw input. In the second case, processes are needed to identify and mitigate instances when RAG data either conflicts with or is otherwise inconsistent with the data that was used to train the original GenAI model.

Models

As with data, care is required when it comes to the models that drive GenAI with RAG. Models should be customized to provide the best fit with the task at hand and consistently monitored and updated to maintain relevance and accuracy. The integration of RAG into an LLM may not always be straightforward either, necessitating work in the pre-training and fine-tuning stages of model development. Finally, the addition of RAG reduces, but doesn’t eliminate, challenges like hallucination or other inaccuracies, making model testing and monitoring all the more important.

Efficiency

There are three ways to look at efficiency when it comes to GenAI with RAG: data input, computational efficacy, and user interaction. With data input, there is a trade-off between RAG data and the LLM’s generative capabilities. Too much and there may be issues with overfitting or even an “echo chamber” effect, while too little may lead to suboptimal results. In terms of computational efficiency, it can be very costly to run non-optimized processes, especially with very large data sets. As Gartner highlights, it’s critical to choose the right type of storage deployment for your GenAI use case. Efficient indexing, retrieval algorithms, and caching help balance desired outputs against costs. Finally, user feedback and clarification requests are extremely valuable and should be considered when developing operational procedures.

Figure 1: Architectural overview of retrieval-augmented generation.

Making the Most of GenAI with RAG for KYC/AML with Pure Storage

Embracing GenAI with RAG represents a significant step forward in tackling the complexities of KYC/AML activities. While challenges exist, the ability to continually learn and improve offers the potential to build a scalable and dynamic solution that is evergreen. Financial institutions that navigate these considerations successfully will be well-positioned to protect their firm from financial fraud and reputational damage and comply with regulatory requirements.

Financial enterprises need a reliable and high-performance data platform to harness the potential of GenAI for KYC/AML activities. Pure Storage provides the domain knowledge along with a validated GenAI RAG solution for financial services with the necessary scalability, flexibility, and low-latency data retrieval to maximize across the full range of GenAI capabilities. With Pure Storage as a partner, institutions can ensure that they make the most of GenAI capabilities that are enhanced by RAG.

To learn more about GenAI with RAG for KYC/AML, download the latest white paper from Pure Storage: “Utilizing GenAI to Enhance KYC/AML and Fight Financial Fraud.”