A decade ago, a leading international bank invested in Data Science Services and Machine Learning to bring about a revolution in the banking sector of India. They had put in years of effort to build the framework and once they achieved it, every minute detail about customer information became crystal clear to them.

Now, this detailed picture of their customer data helped them to determine who was a loyal customer and who was an active one. Moreover, identifying those clients who were simply associated with the bank just for the sake of holding an account,could also be figured out without putting much effort.

And the best thing about their one-time investment is that it still continues to fetch them long-term benefits.

Effect of data science Services and machine learning on the banking industry

Effect of data science and machine learning on the banking industry

Soon after this, banking organization charted out the path of success by incorporating Machine learning customer products, other banks also followed suit. Truth be told, it is hard to deny the multifarious Benefits of data science in the banking and financial sector today.

Here are some ways in which Data Science Services and Machine Learning can bring favorable changes in the finance sector:

1-Customer lifetime value generation

Customer lifetime value or CLV is used to evaluate the information of a bank’s customers. It helps in predicting the overall business benefits a bank will be able to acquire throughout its association with a particular customer. Projection models are built by data scientists where heaps of user data are first made to go through a few rounds of cleanup, after which data is manipulated and segmented to generate the final predictions.

2- Better customer profiling

Classification of customers based on diverse parameters is not only performed to build models but also to extract insights and trends about the customer behavior. This further helps banks to target potential customers as well as boost their relationship with the existing client base. Various methods like decision trees and random forest are utilized in this process.

3- Extended customer support

If you are getting offers from your bank via phone banking and you have no clue about how your bank came to know that you are hunting for a long-term investment plan, then there here is a secret that you must know!

With the aid of Machine learning applications, your bank has built a robust customer support system that tracks your past investment patterns and also monitors your current search history online. Hence it is well aware of the investment plans you have opted earlier and the ones you plan to avail. And that is why it is bombarding you with offers that you have not yet subscribed but might find useful.

4- Risk modeling

Risk modeling
Source: Pixabay

Companies using Machine learning have hugely benefited from advanced and more accurate risk modeling.Using various technologies like python and SAS financial organizations can keep tabs on their financial well being and also examine their overall performance across parameters in a much more developed manner.

Besides, it is also possible to gauge the probability of the occurrence of unfavorable and unforeseen events and devise appropriate plans to prevent them. So, it goes without saying that Data Science Services and Machine Learning help banking companies improve their decision-making abilities.

5- Scam detection

Banks suffering from huge losses owing to fraudulent activities is a matter of past. By implementing various Data science Services projects banks can now classify user data, monitor user behavior and also predict its future. Data science principles specifically applied to analyzed data can reveal any suspicious behavioral patterns displayed by users, and raise awareness of possible scams.

Scam detection

With more and more opting for machine learning it has been a cut throat competition among banks to serve customers with best in class services. And the end result is, that customers come out as the winner.