How to Approach Deposit Churn in Banking Using Machine Learning and AI

On average, retail financial institutions lose about 15% of their customers every year.

This results in a significant drain of resources, leading to considerable losses for the bank. How can banks prevent deposit churn?

With recent advancements in machine learning and AI, banks can now leverage these technologies to mitigate deposit churn effectively. Banking churn prediction models are built, which when applied, help identify customers who are likely to churn.

The Cost of Deposit Churn

Deposit churn is when customers withdraw their funds from one bank and transfer them to another. This not only leads to a loss of deposits for the bank but also incurs significant costs in customer acquisition and onboarding to replace these lost deposits.

Banks are estimated to lose billions of dollars in revenue each year due to deposit churn. The latest industry report estimates that top U.S. banks will likely have lost about $1 trillion in deposits since last year. When customers withdraw their deposits and switch to another bank, the bank loses out on the interest income and potential fee revenue associated with those deposits.

At the same time, additional costs are incurred with onboarding new customers to replace the lost deposits. This includes various administrative expenses, compliance procedures, and customer support efforts.

In addition, long-term customers can generate much more profit than just that from their initial deposit. Losing such valuable customers due to churn can significantly impact a bank’s profitability.

Ultimately, deposit churn affects not only a bank’s bottom line but also its market share. As customers switch to competitor banks, the bank’s market share decreases, potentially leading to a loss of reputation and a decline in investor confidence.

Utilizing Machine Learning and AI

Machine learning and AI offer banks powerful tools to proactively predict and address deposit churn. These technologies can identify patterns and indicators that signal a customer’s intention to churn by analyzing vast amounts of customer data.

Here are a few key steps to leverage machine learning and AI in combating deposit churn:

Data collection and integration

Gather customer data from various sources, such as transaction history, demographic information, online behavior, and perception data. This data should be integrated into a centralized system to enable comprehensive analysis.

Defining parameters

Identify relevant features from the collected data that can contribute to predicting churn. These features could include account balances, frequency of transactions, customer complaints, customer satisfaction scores, etc.

Model development

The next step is to deploy machine learning algorithms to build predictive models. Train models using historical data on churned customers, enabling them to learn patterns and identify potential churners.

Real-time monitoring and intervention

Continuously monitor customer behavior and transactional data. When a customer exhibits signs of potential churn, take proactive measures to retain them. This could involve personalized offers, improved customer service, or targeted marketing campaigns.

Final Thoughts: Considering a Partnership

Implementing AI solutions to address deposit churn may pose challenges regarding data availability and expertise. However, banks can overcome these obstacles by considering partnerships.

Collaborating with specialized data analytics firms can provide access to advanced analytics tools and expertise. By partnering with Finlytica, we can help you develop a customized formula to fit your bank’s specific churn situation. A partnership with Finlytica will allow you to harness the power of machine learning and AI without investing heavily in building the capabilities in-house.

Posted on June-13-2021