How artificial intelligence in banking helps financial institutions grow revenue

Artificial intelligence (AI) can seem complex. But at its core, it’s really a combination of two fundamental components: data and models. When it comes to applying artificial intelligence in banking, these two components unlock data insights from predictive AI, enhance digital experiences, and drive business growth. 

The Foundation of AI: Data

Data is the fuel that powers AI, enabling it to run effectively. 

For AI to deliver meaningful insights, data must meet four essential characteristics:

1. Cleansed: This involves removing corrupt or duplicate data and filling in any missing pieces to ensure quality.
2. Maintained: Data needs to be up-to-date and accurate, ensuring ongoing relevance.
3. Normalized: Data should be transformed to a common standard.
4. Curated: Finally, data from different sources must be organized and integrated into a unified set for AI to analyze.

Without clean, maintained, normalized, and curated data, any AI model runs the risk of making incorrect predictions or decisions.

The Engine of AI: Models

The second essential part of AI is the model, which is a structured series of steps that processes or analyzes data. Different models serve different purposes, and the financial industry has been using various types of models for years. 

Below are a few common types of AI models and how they apply to banking:

Retrieval models: These models search and retrieve information, like a Google search. In financial services, virtual assistants help account holders retrieve information or answer common questions.

Generative models: Generative AI models create new content, such as ChatGPT creating text. For banking, generative models can respond to regulatory requests and generate reports

Predictive models: Predictive AI models forecast future events. In banking, they can predict which products account holders might buy next, such as identifying those most likely to open a certificate of deposit (CD).

In the financial services industry, using the right model depends on the business problem you’re trying to solve. Once the model is selected, it’s critical to ensure it’s fueled with high-quality data.

One example of predictive AI in action involves identifying account holders who are most likely to open a CD. By analyzing account holder behavior six months before they opened their last CD, the model can pinpoint similar account holders who might consider opening one soon.

Alkami is also committed to AI compliance, taking measures to exclude protected class data, such as race and gender, from its models. Monthly compliance reports are provided to customers, allowing them to monitor the AI’s performance while ensuring the AI tools remain aligned with regulatory and ethical standards.

Tracking Engagement and Retention with Predictive AI

Alkami’s predictive AI models don’t just predict account holder interest in new products—they also assess engagement and risk of attrition. For instance, by monitoring changes in recurring transactions, banks and credit unions can spot when an account holder might be preparing to leave. If an account holder stops paying their auto insurance through the financial institution’s account, it may signal a change in loyalty, prompting financial institutions to take retention actions.