Back

 Industry News Details

 
ARTIFICIAL INTELLIGENCE SOLUTIONS FOR BANKING Posted on : Sep 25 - 2020

AI solutions for business-related problems in the banking

Though banks don’t create AI strategies, they are increasingly using artificial intelligence and machine learning in their day-to-day business. We frequently work with them on ideation workshops, PoC, and solution implementation. Santander Consumer Bank, for example, is running workshops and researching how to use machine learning to boost the sustainability of loan portfolios.

Besides credit risk modeling, there is already an impressive range of use cases for AI in banking. It covers everything, from customer service to back-office operations. The most common AI solutions in the banking sector are listed below:

Customer service automation

Chatbots

Applying chatbots to automate customer service helps customers to satisfy. Moreover, simple issues can be solved entirely without human interference. In other words, automation significantly reduces customer service workloads.

Biometric identification

It enables detailed or unnoticed identity verification within remote channels. It can include voice identity verification in call centers or typing verification in online banking.

Customer insights

Customer 360 view

Using deep learning to customer analytics makes it easier to combine insights from various data sources such as transactions and online banking logs. It helps to understand a bank’s customers better and create personalized recommendations and intelligent customer assistants, making the business more responsive and efficient.

Churn prediction

Because of accurate AI algorithms, churn probability predictions improve customer retention. This is crucial as customers frequently stir without obvious warning signs. Therefore, it is challenging to run mainly targeted anti-churn campaigns. On the other side, retention activities can be costly, sometimes much more so than the value a potential customer may bring.

Customer lifetime value

Customers’ lifetime value is often used to analyze how valuable a particular relation is and to optimize other activities, such as by integrating customer lifetime value with a possibility-of-churn function to focus retention activities on the most valuable clients. View More