Analytics In The Field Of Applied Credit Risk

An indispensable part of the business of banking is taking risks and training in risk management has been the center of banking since the very beginning. Credit risk management, especially, has been parallel to the rise and fall of banking institutions. 

According to bis.org, credit risk is described as the potential a bank borrower can fail in meeting its responsibility in accordance with the accepted terms. Banks require to manage their exposure to risk within adequate parameters while optimizing their rate of return as adjusted by the risk. Credit risk, if not managed well or if lenders are too lenient or borrowers can’t repay, can lead to a crisis that causes a credit crunch.

The Institute of International Finance found out at by the end of 2015, international household debt has increased by $7.7 trillion from 2007 to more than $40 trillion, and $6.2 trillion is estimated to rise in the coming years. Therefore, to survive and succeed in such an economic situation, professionals have to consider new ways of reducing default rates and augmenting the accuracy with the way credit is issued. One way to do so in applying analytics, especially using data analytics for Big Data.

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Applying Analytics to Credit Risk Management

What is Big Data? It is a huge datasets that are collected and enforced on platforms to detect preferences, patterns, and trends that inform enterprises about their customers. According to the Economist Intelligence Unit report, many banks are either now in support of using big data analytics or are planning to implement as such as soon as possible. Here are some ways analytics can help credit risk management:

Minimize Non-repayment and Fraud

The Economist Intelligence Unit did a survey in 2014 where they found that 45% of bankers found analytics to be helpful in avoiding both non-repayment and fraud. When customers apply for a loan, the data collected during the application process ultimately becomes antiquated. Until a few years ago, it was difficult to identify how and whether the customer’s situation has changed. Now, data from payment behaviors, interactions with providers (contact through call centers or websites) and even activity on social media can be analyzed to affirm their legitimacy and comprehend their financial positions more efficiently. 

Expand the Market

By evaluating social media and mobile data, insights can also make it easier for people who lack a credit history to gain credit while reducing the provider’s risk. This consequently expands the market, creates new consumers and streams of revenue.

Market to Low-risk Consumers

Another elemental use of analytics includes marketing. Customer patterns and habits can be determined, offering insights into behavioral patterns and triggers. Providers of financial service can get to know their consumers and their requirements better, and customize products subsequently. For instance, many customers spend a lot during certain seasons, so such patterns when identified will allow providers to target offers better and they can target those customers who will be able to repay within the accepted timeframe.

Conclusion

Big Data Analytics has massive value to offer to the banking industry and it has the potential to change the industry in the same way computers changed everything in the 80s and 90s. Influencing insights in the domain of credit risk provides businesses the flexibility to counter opportunities and protect themselves better against the risk that is so common in this industry.

 

NULEARN offers courses on how to apply credit risk analytics and data analytics courses.