Credit unions increasingly turn to artificial intelligence (AI) and machine learning (ML) to gain a competitive edge and improve their operations. However, not all credit unions should invest in these areas.

Leaders should first have a solid data management foundation and a proper use case before investing in building predictive AI/ML models.

AI and ML models rely heavily on extensive and accurate data sets. However, without a solid data management foundation, the data sets used for these models can be incomplete, inconsistent, and unreliable, leading to inaccurate predictions and impaired decision-making. Furthermore, a data management foundation should ensure that data is appropriately collected, cleaned, and organized so that it can be easily accessed and used for modeling purposes.

Also, Credit unions should invest in predictive AI/ML models if they have a good use case. A suitable use case is a specific problem or opportunity the model intends to address. Only with a clear use case the model may be able to deliver the desired results or provide value to the credit union.

Building predictive AI/ML models requires a significant investment of time and resources. Therefore, credit unions should carefully evaluate whether the model's potential benefits justify the cost of building and maintaining it. In order to justify this investment, the credit union should have a clear understanding of how the model will be used and how it will drive business outcomes.

Another important consideration is that predictive AI/ML models require ongoing maintenance and updates to ensure they continue to provide accurate predictions. Without a solid data management foundation, maintaining and updating the models can be difficult, leading to decreased effectiveness over time.

Moreover, credit union leaders should also consider the expertise and resources required to build and maintain predictive AI/ML models. Building and maintaining these models requires a high level of expertise in data science, machine learning, and software development. With these resources in-house, credit unions may be able to rely on external vendors, which can be costly and may not provide the level of ROI needed to ensure the success of the model.

It's also important to remember that AI/ML models can only be as good as the data they are trained on. If the data the model is trained on is biased, incomplete, or inaccurate, the model will not be able to deliver accurate predictions and may lead to poor decision-making.

In conclusion, credit union leaders should only invest in building predictive AI/ML models if they have a solid data management foundation and a valid use case. A solid data management foundation is essential to ensure that the data used for these models is accurate and reliable and that the credit union can easily access and use the data for modeling purposes. A valid use case is also crucial to ensure that the model will provide value to the credit union and drive business outcomes.

Additionally, credit unions should carefully evaluate the potential benefits of the model against the cost of building and maintaining it and have the necessary expertise and resources in-house or from external vendors.