Finance and Risk Analytics in Banking

Banks are key economy driver with respect to Country like India. Market fluctuates, peoples sentiments change and liquidity is impacted when RBI announces any results.

So as to have a healthy economy Risk with respect to finance and banking should be kept in check. Still many risk parameters are used in banking still there is a leakage which impacts in the form NPAs, Fraud, Money Laundering and Fund Diversion.

So what is Financial Risk in Banking sector:

As we know main business model of banking is via lending money and earning interest.

So taking the money of the key and giving away credit at high risk enables banks to put in multiple checks.

With so much data around the banking sector, analytics is introduced to for early detection of these kind of Risks.

Summary of Risk Types:

Further Classification of Risks

How banks can use Analytics in Financial Risk and enhance the desired results.

Seven areas where predictive analytics works wonders While the use of predictive analytics has been limited in wholesale banking, its potential to deliver value across the entire spectrum of wholesale banking sub-functions is immense. Here are seven:

Some examples Use Cases for Risk Analytics in Banking

Machine Learning and Deep Learning in Financial Risk.

Machine learning has been explained as lying at the intersection of computer science, engineering and statistics. It has been highlighted as a tool that can be applied to various problems, especially in fields that require data to be interpreted and acted upon. Machine learning delivers the capability to detect meaningful patterns in data, and has become a common tool for almost any task faced with the requirement of extracting meaningful information from data sets. When faced with the requirement of extracting meaningful information from data, and the consequent complexity of patterns to be studied, a programmer may not be able to provide explicit and detailed specification on the execution process. Machine learning addresses this challenge by “endowing programs” with the ability to “learn and adapt”.

Conclusion

The future of machine learning in the banking and financial industry is well recognized, and it is expected that the field of risk management will also seek to apply machine learning techniques to enhance their capabilities. Despite being critiqued for operating like a black box, the ability of machine learning techniques to analyse volumes of data without being constrained by assumptions of distribution and deliver much value in exploratory analysis, classification and predictive analytics, is significant. This offers the potential to transform the area of risk management.