Arjun Pandey & Nishant Singh

After the financial crisis, the need to accurately predict loan defaults has increased tenfold. For example, federal education loan defaults today are being value at around approximately $98.1 billion. Other sources suggest, that in 2013 approximately 7.8% of loan amounts were written off as defaulted loans. This is aggravating the already increasing insolvency rates, forcing financing firms to gradually shift from traditional methods of default prediction to utilizing predictive analytic methods such as Machine Learning, Linear Regression etc. to achieve a more reliable source of forecast.

Source: Ernst & Young, 2013: EY Europe Financial Services Forecast

EY Europe Financial Services Forecast

Risk analytics utilizes analytical methods through machine learning , logistic regression etc. to develop models which predict the risk associated with activities such as lending a loan and the probability of default. Predictive risk analytics provides firms with appropriate models to make the right decisions cheaper and faster. The credit scoring results found through risk analytics is much more accurate than man-made decisions, as it considers a multitude of dimensions and variables to the same problem. The credit score calculated real-time, as and when borrower data is provided to the model, makes it even more useful.

Many firms are still in the process of adopting predictive analytics techniques and dependent on stochastic methods for decision making. Stochastic methods rely on the credit information report of a borrower to determine his/her credit score. This report comprises of borrower information such as cases filed on borrower, default(s) if any, amount due and the current job profile(to determine the EMI salary ratio). In such cases, risk analytics serves as a secondary check and helps rate the borrowers once the primary decision has been taken. Borrowers with a better rating are offered comparatively better interest rates and more relaxation in case of late payments. Predictive analytics methods determine the interest rates/discounts as a function of borrower’s associated risk and thus add to the firm’s ROI/profits by substituting intuitive decisions with quantifiable solutions.

Let’s assume two borrowers (L1 and L2). L1 provides an interest of 10% and L2 of 15%. Utilizing the parameters of L1, L2 in real-time, the expected default risk associated with L1 and L2 comes out to be 20% and 60% respectively. Despite a lower interest rate, L1 has an expected return rate of 8% and is more profitable than L2 with an expected return rate of 6%. Thus, risk analytics provides a definite methodology of choosing L1 which is an informed decision over L2 the intuitive decision.

Real-Time Analysis

Organizations utilize risk intelligence to effectively predict threats and alleviate them. Detecting identity theft and fraudulent threats related to loans and credit cards for banks or financial firms be more easily handled when they share a similar pattern and can be easily detected by a trained model. Real-time analysis of the associated risk at the point of a financial decision can help avoid probable losses in future.

The following scenario is an example of a case where predictive risk analytics methods have an edge over stochastic ones due to real-time analysis.

TIME PERIODS

Time Periods

Assume that Customer A takes a loan from Firm B.  With lack of historical information for the first four years the firm is unable to assess the default risk for Customer A. In case A defaults, the damage/loss would already have been done even before A could be analyzed for the associated risk. Risk analytics uses data regarding the borrower background, demographics etc. to determine the associated risk more quickly – at time interval 1 itself. Using this information Firm B could easily avoid the probable loss.

Thus, with growing risk and the explosion of data the importance of predictive risk analytics is burgeoning. With a holistic, 360 approach and data from multiple sources, it is becoming easier for analytics partner to develop more accurate probabilities of the risk associated with borrowers. These models are getting fine-tuned with every usage and becoming more apt to independently provide more accurate results.

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