Apply Big Data Techniques to Credit Risk Management

Blog 18-4 - AVeluz

by Aldrich Veluz, February 9, 2018
Taking risk is an integral part of the banking business and practicing risk management has been at the core of banking ever since the beginning. Managing credit risk, in particular, has been tantamount to the rise or fall of banking institutions.

So what is credit risk? As mentioned in the bis.org – “Credit risk is most simply defined as the potential that a bank borrower or counterparty will fail to meet its obligations in accordance with agreed terms.” Banks need to maintain their risk exposure within acceptable parameters while maximizing the bank’s risk-adjusted rate of return. When credit risk is not managed and lenders are overly lenient if offering credit and borrowers are unable to repay, this leads to a credit crisis which leads to a “credit crunch”.

The credit crunch is one of the factors that led to the financial crisis. After the global financial crisis, credit risk management has been in the regulatory spotlight. A response to the crisis was the development of the Basel III regulations.

It is not just about adhering to tougher regulations but building a more robust and sound financial system along with continuing to increase the scale and complexity of financial institutions demand sophisticated risk management techniques and monitoring of rapidly changing risk exposures. Advances in information technology particularly in the Big Data spectrum have lowered the cost of acquiring, managing and analyzing data, and have enabled considerable and ongoing advances in risk management.

Banks have acknowledged that in order to do better business, they need to be able to make better data-driven decisions and in an age where data is everywhere, it makes sense to jump into Big Data implementations. Financial reporting has evolved but still falls short as mentioned in an article in Harvard Business Review so continuing to invest and move forward with more complex risk management is critical to the survival and growth of banks.

Traditionally, credit risk management is verifying the debt-to-equity ratio and analyzing where the credit is concentrated across the portfolios, geography and other attributes so that the bank can make decisions whether to take on more risk or take on less risk. With the emergence of Big Data technologies, there are some best practices that can be implemented faster and in a more effective manner. I have listed some of them below and how Big Data can help.

  • Know Your Customer – Customer segmentation and behavior analytics are some of many different ways that banks can know their customer more. Retailers have been at the forefront of the KYC revolution but banks and financial institutions can also employ this to know what customers are looking for and what their appropriate risk rating is.
  • Monitor the Relationship – Like any business, banks would want to keep their existing customers. Monitoring a relationship is cheaper than trying to get new customers. Sentiment analysis and predictive analytics can be leveraged to identify clients who are trying to shop for different products and those that should have a higher or lower risk rating.
  • Analyze Nonfinancial Risks – Banks are essentially financial consultants of their customers so there is an investment to know as much about the customers as possible and that includes risks associated with the customer’s industry, business and management. With machine learning, one can find possible patterns with customers that can lead to more intelligent questions that could potentially anticipate an upcoming risk and more importantly, mitigate it.

However, jumping into Big Data for the sake of getting into the technology is counterproductive. One needs to identify where they currently are and understand the journey that has to be taken in order to make better decisions. If an institution currently uses mainframe and DOS terminals to handle all their reporting, it is not wise to expect them to jump to sentiment analysis and advanced analytics in a month. The data journey can be started with that end in mind but remember that it is a marathon and not a race.

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