The banking industry continues to experience increased pressure from outside threats such as Fintechs seeking to replace their deposit accounts. In this post, we explore one unique advantage many banks have over their Fintech competitors and how they could use it to potentially revolutionize their value-added services to their customers.
Most established banks are sitting on a mountain of deposit transaction data for both their personal and business banking customers. With large amounts of data comes opportunity.
Banks could potentially use machine learning algorithms to analyze commercial deposit account data and identify cost savings opportunities for their customers. The process would involve training a machine learning model on a dataset of deposit account data, which would include information such as account balances, transaction history, and spending patterns.
One approach could be to use a clustering algorithm, which would group customers with similar spending patterns together. The bank could then analyze these clusters to identify customers who may be paying more than they need to for auto loans, mortgages, and consumer loans. For example, if the bank sees a cluster of customers with high car payments in relation to their income, it could assume that those customers may be paying more than the current market rate and could flag them as potential candidates for a refinance.
Another approach could be to use a decision tree algorithm, which would use deposit account data to predict which customers are most likely to be interested in a particular financial product or service. For example, the algorithm could use deposit account data to predict which customers are most likely to be interested in a mortgage refinance.
Once the bank has identified customers who may be able to save money through a refinance or other financial product, it could use automated emails or push notifications to communicate with them about the potential cost savings opportunity. These notifications could include information about current rates and how much a customer could potentially save by taking advantage of the offer. Additionally, the bank could also use the automated emails to guide customers through the process of taking advantage of the cost savings opportunity, including providing necessary forms and contact information for the loan officer.
It’s important to mention that using machine learning for such tasks requires a large and diverse dataset and also a process of continuous monitoring and fine-tuning of the models as the market and customer behavior change. Also, the bank should ensure that they comply with the data privacy and security regulations, such as GDPR and HIPAA.