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\title{Has Machine Learning Arrived for Banking Risk Managers?}
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\begin{document}

             \author[1]{Manoj  Reddy}

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\date{\small \em Received: 15 December 2017 Accepted: 4 January 2018 Published: 15 January 2018}

\maketitle


\begin{abstract}
        


Machine learning is one of the most exciting and powerful cognitive levers out there in the Industry and today Risk managers are grappling to make sense of whether it is just a hype or does it really have a value to add in Banking Risk Management. The article attempts to give a brief introduction into foundational concepts of machine learning and highlights some of the problems with the current predictive models and also some of the most popular pilot or candidate Use cases for Machine learning adoption. It also highlights the critical success factor which one needs to consider or be aware of in in adoption of Machine leaning to solve business problems. The paper intends to demystify the conundrum called as Machine learning and elucidate it to an extent which will enable the new age Risk & Regulatory managers to carefully evaluate and decide on an adoption of Machine learning techniques and solutions to solve specific Business problems.

\end{abstract}


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\let\tabcellsep& 	 	 		 \par
powerful cognitive levers out there in the Industry and today Risk managers are grappling to make sense of whether it is just a hype or does it really have a value to add in Banking Risk Management. The article attempts to give a brief introduction into foundational concepts of machine learning and highlights some of the problems with the current predictive models and also some of the most popular pilot or candidate Use cases for Machine learning adoption. It also highlights the critical success factor which one needs to consider or be aware of in in adoption of Machine leaning to solve business problems. The paper intends to demystify the conundrum called as Machine learning and elucidate it to an extent which will enable the new age Risk \& Regulatory managers to carefully evaluate and decide on an adoption of Machine learning techniques and solutions to solve specific Business problems. 
\section[{I.}]{I.}\par
Background and Business Context he Digital revolution is in the process of transforming the Banking Industry like never before.\par
From understanding the customer proactively and deciding your next best action to gaining greater efficiencies and scale in running the Business, technology has played a huge role in enabling delivery of maximum business value to the customer in the shortest possible time. Artificial Intelligence is challenging the very realms of possibilities in how you run your Business and what seemed fiction or theoretical a few years back is now becoming a reality which Banks will need to consider seriously to stay in the game.\par
Machine Learning is powering the Artificial Intelligence revolution through its effort to make the predictive capabilities of Business lot more accurate, repeatable and scalable. Though the early adopters of Artificial Intelligence have been the customer centric functions of Bank, there has been a gradual progression towards exploring its potential to solve Banking Risk Management problems. 
\section[{II. So, ARE THERE ANY Limitations with HOW Things Work NOW?}]{II. So, ARE THERE ANY Limitations with HOW Things Work NOW?}\par
One of the foundational principles of Risk management is the need to not only assess or quantify your risk, but also to do it early enough to effectively mitigate it. The Industry has relied very heavily on T algorithm based and conventional statistical models driven by knowledge, experience and judgment of the Business Subject matter experts across banks globally. Though the Conventional Risk models have stood the test of time and have helped Banks identify and manage their risk, they are yet to achieve highest levels of accuracy and additionally they are not equipped to handle huge chunks of structured and unstructured data which potentially hold critical risk insights.\par
For Instance, The Industry average of the accuracy of a PD model or a Loan Default prediction model typically could be anywhere between 80 to 90\%, and in most cases we are unable to scale up the model to process greater volumes of historical data due to technology and infrastructure limitations. Additionally since the variable selection and its corresponding Significance in prediction is largely humanly defined with some statically inferences, we at most times are not able to fully derive the benefit of drawing insights from the breadth and depth of historical data. Similarly Industry is still battling with high number of false positives in the Anti-Money Laundering space and is excessively erring on the side of safety which is hurting operating margins and efficiencies.\par
There are number of such other predictive outcomes in Risk management where leveraging of additional insights could very positively increase the accuracies. 
\section[{III. What is Machine Learning in the}]{III. What is Machine Learning in the}\par
Context of Risk Management?\par
At a very high level there are two most popular approaches to machine learning they are namely supervised and Unsupervised learning. Supervised Learning: This is an approach to Machine learning where the historical Inputs data is tagged with its corresponding Business outcomes and the Machine learning solution is expected to identify and learn the patterns in the inputs data associated with a Business outcome and self-develop an algorithm based on this learning to predict a Business outcome for a future instance. There are two primarily utilities of Supervised learning:\par
Classification: This is used for Business problems where the prediction deals with classification.\par
For Eg: Whether a Loan will default or not. (Yes or No). Regression: This is used for Business problems where a quantified value is being predicted. For example what is the Probability of Loan default? (Expressed in percentage).\par
In case of Supervised Learning it is important to have a good mix of historical data supporting both the positive and negative hypothesis both for the training set and a test set. It is recommended that we have them almost equal. Additionally it is recommended to have 80\% of your historical data as Training and 20\% as the Test set.\par
Unsupervised Learning: This is an approach to Machine learning where the historical Inputs data is fed into the machine learning solution without any tagging of the Business outcomes and the solution is expected to decipher or self-develop an algorithm for prediction based on its own interpretations of the patterns in the data without any guidance or indicators. The Primary difference between supervised and unsupervised learning is that the historical Input training data is tagged with Business outcomes as well in cased of supervised learning. There are two Primary utilities of Supervised learning:\par
Clustering: This is used for Business problems where records with homogenous characteristics are clustered together.\par
For Eg: Customer Segmentation for Consumer Credit Risk Calculations.\par
Association: This is used for Business problems where the prediction is looking at the association of variables. For Eg: Impact of increased draw-down on credit lines prior to default.\par
Depending on the problem we are trying to solve any of the above mentioned techniques can be used. The end objective is to leverage the ability of machines to crunch huge amounts of historical data and uncover the hidden insights which are beyond human compression and conventional statistical analysis. 
\section[{IV. So What ARE Some of the Problems Machine Learning can Solve?}]{IV. So What ARE Some of the Problems Machine Learning can Solve?}\par
There is definitely a lot of buzz and excitement around leveraging Machine learning for predictive models needed by Risk Managers, but a clear understanding of the business value and the specific need for leveraging it should be carefully understood and evaluated before zeroing in on the USE cases. Leveraging machine learning for processes which are already fairly accurate with a Business rule or statistical models may not be good candidates for early adoption.\par
Following are some of the top candidate processes or USE cases being explored in the Industry for Machine Learning Adoption in the Banking Risk management space. over the years. However, clustering techniques in machine learning provide tremendous scope and potential for the consumer loan segmentation using methods such as KNN.\par
V  
\section[{Conclusion}]{Conclusion}\par
Machine learning is here to stay and is making rapid strides across multiple Industries.\par
Though Machine learning as a concept has existed for more than three decades now, it's the advancement in Data storage and data management powered by the Big Data and Digital revolution has made Data mining more affordable and easier. There are number of Banks and financial institutions which have commenced their exploratory journey into machine learning world and for all new age Risk Mangers Machine learning is no more a work of fiction but is a reality with tremendous potential to proactively and effectively predict and manage their Risk. The Journey has just begun. \begin{figure}[htbp]
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 			\footnote{© 2018 Global JournalsHas Machine Learning Arrived for Banking Risk Managers?} 		 		\backmatter  			  				\begin{bibitemlist}{1}

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\end{document}
