Five Building Blocks of a Generative AI–Led Credit Union

If your credit union is looking to make use of the myriad advantages that GenAI can offer, there are five building blocks that can help support your implementation process:

1. Data: Any technology project, including GenAI, relies on data to produce desirable outcomes efficiently. The key to ultimate GenAI success is working with clean data that has been properly prepared for training and use; without this step, your GenAI may not produce helpful, relevant answers. For example, ill-prepared data could produce immaterial outputs, cross-reference illogical scenarios, or even share non-sensical information. Some common scenarios that you might need to address to get the most from GenAI include partial and aging data, improper configuration of your management database, data resources that have not been mapped to applications and owners, and not having appropriate data storage in place.

2. AI models and algorithms: AI in banking is progressing from natural language processing (NLP), machine learning (ML), and AI models, which can provide users with the information they need in response to prompts given in simple language, to creating a process around those prompts for GenAI to execute on, to the rise of AI agents that can analyze and implement the programed processes.

Different use cases could require different large language models (LLMs) to analyze and generate correct outputs; this means first choosing the right LLM for the scenario and, second, possibly training the LLM for each use case. Documented workflow protocols are helpful in determining and fine-tuning the algorithms for GenAI models for optimal application throughout your organization.

3. Infrastructure and tools: GenAI models require specific infrastructure and tools to be useful, like coding and software to support GenAI delivery, content synthesis to be a virtual expert, and customer engagement applications. These tools need to be integrated into the larger system that delivers the performance and compute efficiency needed to drive productivity for a wide range of complex workloads. Choosing the right infrastructure and tools helps determine resources and cost; determining when to use which tool or ecosystem can help minimize expense. Beyond the right architecture, having documented policies can help program the GenAI LLM. Gaining insight into the collected data through central management capabilities and ensuring proper operation through prescribed maintenance protocols are important infrastructure components to a healthy GenAI infrastructure.

4. Security and compliance: Incorporating cybersecurity measures at the beginning of your GenAI implementation is crucial for protecting member data and maintaining a robust credit union infrastructure. When training LLMs, data preparation for use in fine-tuning your AI model is essential for optimal performance. Credit unions must ensure AI tools maintain member privacy and confidentiality while delivering relevant results. This requires implementing security measures like encryption, access controls, and ongoing vulnerability assessments. Monitoring GenAI decision-making processes helps verify compliance with security protocols, while third-party attestation can validate system configuration and code trustworthiness.

5. Integration with existing systems: First, a quick definition of the term technical debt, which is the accumulation of inaction, shortcuts, compromises, or suboptimal design decisions made over time that now undermines system functionality. Technical debt becomes important when considering new infrastructure investments. While it is important to get the most from your legacy infrastructure, there is a balance to be struck between systems that create technical debt and those that modernize existing investments by using APIs and microservices. As a precursor to your implementation journey, take a look at how your current architecture is set up, then determine how your system will fit together with GenAI both in terms of existing infrastructure and what new applications will be needed.

Altogether, by becoming a GenAI-led credit union, your organization can benefit from increasingly efficient and future-proof operations while delivering greater member value and driving member growth, which can enhance your competitive edge.