Tips for Getting Started with Artificial Intelligence Predictive Modeling

Artificial Intelligence Predictive Modeling (AI) isn't a nice-to-have anymore. It's a competitive advantage that financial institutions (FIs) should begin harnessing right now.

Using AI allows FIs to make sense of all the data produced by their account holders every day.

The fact is: as of 2021 only 10% of small to mid-sized banks and credit unions identified as using AI data frequently. Last year, despite 75% of surveyed banks with over $100 billion in assets actively using and implementing AI, only 46% of surveyed banks with less than $100 billion in assets said they were actively using AI strategies. That is likely because in the past, for an FI to successfully implement AI predictive modeling, an in-house team of data professionals would need to endeavor for months in development. This perpetuated the common misconception in smaller banks and credit unions that AI modeling was too costly or too complicated of a project for them to take on.

Alkami challenges that mindset. AI modeling is no longer out of reach for regional and community FIs.

Tips for Successfully Launching AI at your FI

Getting started with using AI as part of an FI's regular business model may seem a bit daunting, but all it takes is the right tools.

Lead with the challenge, not the AI. Instead of starting with a broad brush of the technology itself — what can AI do for us? — start with one business challenge or goal you want to focus on. Reducing attrition, for example. Narrowing the focus to something you'd like to improve helps make the technology less intimidating.

Partner with the right vendor. The right data partner will use automated processes to sort through tens of thousands of data points predictive of whatever business operation your FI wants to model a solution for. It will feel like magic, but trust us, it's science.

Put one person (or a team) in charge. This could mean creating a position or designating a chief technical officer to interface with your vendors and drive tech innovation at your FI. The best part about finding the ideal vendor is, your in-house team member does not need to be a data analytics wizard themselves.

Top Use Cases for AI Predictive Modeling

Attrition and cross-selling are two common business challenges FIs tackle with the help of AI.

Attrition

The loss of account holders causes a reduction in gross revenues every year for FIs, just how much depends on the organization. An attrition model analyzes key indicators such as banking behavior, account history and merchant spend patterns to identify account holders that have a high risk of leaving the FI in the near future. Flagging those account holders as Attrition Risk Positive gives an FI the chance to take action and deploy offers to turn those high-risk individuals into long-term account holders.

Cross Selling

Using customer insights, FIs can identify and target the account holders who are most likely to positively respond to new product offerings. Through this AI process which spots behavior trends, FIs are given an ideal audience which would feel understood and catered to when offered a mortgage refinance, credit card, auto loan, or other product or service that is relevant to them. With this model, FIs can choose what factors are important to them to pick the best strategy that aligns with their goals.