Managing Credit Risk: An Effective Approach with FinTech Data Science

Financial Technology, popularly referred to as FinTech, is one of the fastest growing areas in technological innovation. FinTech refers to a set of technologies that focus on new ways of delivering financial services in our society. This technological wave in the finance industry started with the use of computers in recording financial transactions, assessing tax payment, creating an optimal portfolio among many others. Today, everything from customers’ ability to pull up real-time information on account balance to apps that track customer spending, to tools that allow quick financial decisions are all examples of FinTech impact.

One of the most exciting trends in FinTech innovation is the use of big data to simplify financial decision and provide comprehensive solution to its stakeholders — FinTech Data Science. This is mainly due to large volumes of financial data that is available in recent times. Undoubtedly, one major area of concern in finance that has seen an unprecedented solution from leveraging big data with analytics is Credit Risk Management.

Credit Risk could simply be defined as the possibility of loss resulting from a borrower’s failure to repay a loan or meet contractual obligations on specified terms. Almost all financial institutions remain vulnerable to credit risk as far as lending forms an integral part of its services to the society. Managing Credit Risk has, therefore, become a top priority in the financial industry as firms need to protect themselves from loss of economic capital and bankruptcy.

The goal of Credit Risk Management is to eliminate or keep credit risk exposure within acceptable parameters to ensure the continued existence of the firm. It involves assessing the likelihood of defaulting, which entails real-time monitoring of various customer transaction across multiple channels, detecting suspicious activities and compiling a list of doubtful customers. In managing credit risk, most financial managers concentrate on how to balance potential revenue from lending activities with expected loss from defaulting. They are usually interested in the following parameters:

• Probability of Default: The likelihood that a loan (or credit amount) will not be repaid
• Recovery Rate: The rate at which loss could be recovered by selling customer collateral, given the customer defaults.
• Loss Given Default: The fractional loss due to default
• Exposure at Default: The amount owed at the time of default.

Over the years, various methods such as credit score cards, intelligent dashboard and reporting template, etc. were used in determining the aforementioned parameters. These methods are dependent on data but are inefficient in solving credit risk problems as they only signal credit risk whenever a credit event occurs. For example, when a payment is missed or a residual debt remains after selling a collateral property. The ambition of most financial firms is to signal such possible payment issues months or even a year in advance.

A typical intelligent dashboard for analyzing credit risk — www.quizzle.com

However, in recent times, FinTech Data Science has revolutionized Credit Risk Management by enabling financial institution to automatically detect and predict any lurking credit risk that may emerge before and after lending. The introduction of Big Data together with Machine learning algorithms and other data science techniques in finance has made it possible to develop predictive models that learn by analyzing customer’s historical data with peer group data and other relevant data across multiple platforms such as PayPal, MasterCard, etc.

The nicest thing about these predictive models is that they do not only to predict the probability of defaulting but also the rate at which the firm could recover loss given the customer defaults. This has empowered most financial institutions to make quicker and improved lending decisions, develop customized repayment methods to solve credit risk problems, venture into new lending options among many others.

Today, most financial institutions are expanding their client base by granting credit facilities to customers online without worrying much about credit risk. Thanks to FinTech Data Science!

Composed by Blossom Academy fellow, Philemon.

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