Technology is interesting and attractive always! It inspires technocrats to work more and innovate new ideas. But what if the technology has no applications in the real world? People will ignore it then. Machine learning is buzzword nowadays and it is important to know what are its real-time applications and how it is useful for you. Machine learning has wide use cases in banking domain. Let us see what are they.
It is needless to say that banks need to have strong and quality intelligence. It is high time that performance management departments must go away from the action since they are outdated – as they are a group of people responsible for things manually such as mine data and produce insights into excel files. Very inefficient and time-consuming indeed! It’s time to go ahead and automate this process of extracting insights.
Some of the important scenarios in banking that I think machine learning can heavily contribute are explained below. I have already elaborated credit card fraud detection use case in my previous papers with practical machine learning scenarios. Here we will just see what machine learning can do in this domain.
1.Credit card /Debit card Fraud Analytics
Banks face major problem is frauds in transactions. Maybe, prime chance lies here in noticing fraud online and avoid by leveraging analytics and machine learning to gain a complete assessment of customers. recognize forms in data, group information, and differentiate fake action from usual action.
2.Risk Analysis – Perception of Credit Worthiness of a Customer
Detecting a risk score of a customer built on nationality, profession, salary range, knowledge, corporate experience in the business he/she works for, credit history etc. is very perilous for banks before even offering a credit service to the customer. This risk score is a significant KPI for banks to adopt interest rate and other serve offerings for the customer. More the risk score is, more are the chances that that customer has a probability to commit frauds.
The solution to compute risk score has a central, integrated finance and risk mechanism that contains all the past data of customers and keeps on processing the credit history and generate risk score and apply a ranking to customers based on the risk score. Banks lack today to have such mechanism and hence fail to quickly compute risk score of a customer and many times take wrong decisions unknowingly that they are providing service to prospect robber!
3.CRM – Treasury
CRM is very protuberant in Retail Banking domain. Here I am talking about spot transactions. When the question arrives that asks about Treasury space within banking, customer relationship management is not present. This is not recommended. Treasury has an assorted product palette available like FX, Options, Swaps, Forwards and more prominently Spots. The solution is to use Machine Learning to combine a strong exchange rate pricing reinforced by prompt risk sanity check and then placing a deal online – wow, this would be wonderful! Happy banking!
In this era of generation-X, generation-Y, traditional mechanism follower banks are really stressed to cluster customers into diverse segments to support sales, promotion, and marketing campaigns by gathering and examining all obtainable data and using Big Data technology to mine for intelligence from underlying data.
Applying machine learning help bank to offer their services better and without having any risk since the smart ML based solutions predict the risks in advance!
There is fact that technocrats should understand and which is really important is, no matter what fresh insights machine learning excavate, only human managers are able to decide the vital queries, such as which life-threatening business problem a business is really trying to solve. As of now, machine learning cannot replace humans. But yes, it makes human life easy, powerful, intelligent smart and happy!
Hey banks, looking forward to adding some spicy machine learning to your processes and see how delicious the life becomes!