The 4 Ways Banks Are Using Machine Learning

Banks have always had a close relationship with Data. From back in the days of the crusades when banks first came into existence  by the Knight Templars , right to this day; banks have always maintained and operated on an extensive collection of data sets for their customers as well as their transactions. The advancement in machine learning and artificial algorithms have now provided banks with the ability to surpass the limitations of the human mind and thus, work in a much more efficient manner.

A lot of banks are picking up the pace and hurriedly equipping themselves with machine driven solutions while some, however some are still staying behind with their old school systems.

An efma/Finacle study found that half of banks listed their legacy systems as the biggest hurdle they face, followed by a lack of unified vision (44%) and a shortage of skills and experience (38%).


But those who have overcome their problems have actively started picking up business cases they can use AI and machine learning to simplify. The competition is fierce and top level decision makers want to grab any advantage they can.

The following is a list of 4 things Banks are doing with increased efficiency by incorporating machine learning solutions:

1. Customer Recommendation

Banks have a lot of services to sell. Fixed deposits, credit cards, home loans, and much,much more. Offering all of these services to all their customers at all times is not an approach that works. Knowing what to sell, when to sell and whom to sell makes a huge difference in the conversion rate of customers buying new services or products.

Machine learning algorithms process all the data banks have about their customer like credit card plans, investment strategies, funds, etc. to make offers and recommendations based on the customer’s past behavior and financial status.

2. Fraud Detection

Identifying individuals who have a high risk for fraud is a task inherently suitable for machine learning. These machine learning solutions are able to comb through huge transactional datasets and identify all cases that might be prone to fraud.

By collecting data from various sources and then mapping them to trigger points, AI solutions are able to find out the rate of defaulting or fraudulence for each potential customer thus alerting the Bank beforehand that giving any credit to these individuals is risky.  



3. Algorithmic Trading

The biggest factor for someone to make it big in the share market was to be able to predict the future of the market. Machine learning is making this job obsolete. By employing high capacity machine learning algorithms Hedge fund firms are able to predict the market and make investments that always return a profit.

AI has removed the uncertainty factor in the buying and selling of stock. Banks are heavily moving towards the same as well because no one wants to shoot in the dark when others have night vision goggles.

4. Anti-Money Laundry


Anti-money laundering (AML) refers to a set of procedures, laws or regulations designed to stop the practice of generating income through illegal actions. Banks have always paid strict attention to the transactional behavior of their customers for any activity that might involve illegal money,

However, criminals frequently find ways to bend the system and find gaps they can slip into to make their illegal money look like it came in from legitimate sources. To fill these gaps banks are moving towards AI solutions that can track as well as predict illegal activity to stop any chances of money laundering


I agree to have my personal information transfered to MailChimp ( more information )
Join over 3.000 like minded AI enthusiasts who are receiving our weekly newsletters talking about the latest development in AI, Machine Learning and other Automation Technologies
We hate spam. Your email address will not be sold or shared with anyone else.

Leave a Reply