AI in Banking: Revolutionizing Operations and Customer Experience

By John Walubengo

Artificial Intelligence (AI) is no longer just a futuristic concept for banks—it has become an essential tool in improving business efficiency, boosting revenue, and managing risks. The use of AI is driving innovation across various banking functions, from customer retention, and forecasting to lead generation.

Let’s dive into four common AI models and use cases in banking: churn management, cross-selling, forecasting and lead generation.

Churn Models: Retaining Customers and Revenue

Customer churn—where customers either gradually or abruptly leave a bank—is a significant issue for financial institutions. AI-powered churn models can help banks predict and manage churn by identifying patterns in customer behaviour that suggest a customer may leave.

For banks, this is crucial because they rely on customer deposits (liability customers) to lend to other clients (asset customers). A loss of deposits directly impacts the lending capabilities and profitability of banks.

By using AI models to detect early signs of churn, banks can act swiftly, offering incentives or personalized offers to retain customers before they decide to leave. Proactively managing churn ensures the bank retains valuable deposits and maintains a stable flow of funds for lending activities, protecting both revenue and customer relationships.

Cross-Selling Models: Targeted Offers that Boost Revenue

Cross-selling in banking refers to selling additional products to existing customers, like offering a loan to someone with a strong savings profile. AI models can help banks identify customers who are likely to purchase additional services based on their financial history and behaviour.

For example, a customer who consistently pays their mortgage on time is flagged by the AI model as a candidate for a personal loan offer, perhaps for home improvements. The same goes for personal account holders who may benefit from SME loans or investment products.

These AI-driven recommendations ensure that the bank offers relevant products to the right customers, increasing the likelihood of acceptance and improving customer satisfaction.

The benefit here is clear: cross-selling helps banks maximize revenue from existing customers, but with AI’s predictive power, they can do so in a more focused, personalized way.

Forecasting Models: Planning for Financial Success

Forecasting models driven by AI are used to predict everything from how many customers will renew their fixed deposits to how much savings will grow month-to-month. These forecasts help banks manage liquidity, plan their lending strategies, and ensure they are ready for future customer demands.

For example, if a large portion of fixed deposits are set to mature or expire soon, the AI model can predict how many of those may be renewed. With this information, banks can estimate how much liquidity they will have available for lending or loans.

Similarly, forecasting can help predict how much cash needs to be on hand to cover withdrawals and balance with the growth of the Savings to establish cash flow trends.

Banks can also predict loan demand, determining in advance how many customers might apply for loans and the average loan sizes by analysing historical customer transaction data. This enables more accurate resource allocation and financial planning, ensuring that the bank is always ready to meet customer needs without overstretching its resources.

Lead Generation Models: Focusing on High-Value Customers

AI-powered lead generation models are designed to identify potential high-value customers for a specific product or service. Instead of casting a wide net – the infamous spray and pray approaches, AI models instead allow banks to target the most promising prospects, resulting in better conversion rates and more efficient use of marketing resources.

For example, if a bank is looking to promote a new loan product, AI can analyse data such as salary, credit scores, and transaction history to identify the top 50 customers most likely to take out that new loan product. With this data, the bank’s sales teams can then launch targeted campaigns that are more likely to succeed.

The power of AI in lead generation lies in its ability to increase the return on investment (ROI) for marketing efforts. By focusing on a refined group of customers, banks can increase the likelihood of conversion, reduce wasted marketing efforts, and build stronger relationships with high-value clients.

AI is no longer just an add-on for banks—it is a strategic tool that drives better decision-making, improved customer retention, and increased revenue. From churn management, forecasting, and cross-selling to lead generation, AI models are revolutionizing how banks operate.

The transformed Bank is already here, indeed has been way before the ChatGPT revolution and if there is any bank not yet having a Data science team, then they are playing a losing game.

John Walubengo is an ICT Lecturer and Consultant. @jwalu.