Covid-19 pandemic has ignited an unprecedented risk for the economy. Banks and financial institutions across the globe are expected to register unusually high default rates on loans once the moratorium and forbearance imposed by the governments and the regulators are lifted. Scientific tools such as collection and recovery scorecards offer a mechanism to predict defaults
Artificial intelligence and cognitive computing have started to make their presence felt in the business community with the promise to improve businesses like never before. Data science models in some sense are the precursor to artificial intelligence. Data science and predictive analytics have already improved processes significantly and decision making for several organizations. Companies with sincere investment in
Predictive analytics is considered one of the sexiest professions of our times. But where did it all start? What was the first business application of predictive analytics? It is hard to pin down one particular application but one of the earliest and highly successful applications is certainly credit risk models and retail credit scorecards. Credit scorecards help banks assess
Analytics Lab – R Welcome back to Analytics Lab on YOU CANalytics! In our last article, we learned the procedure to visualize risk across a parameter (age groups) in Excel. In this part we will generate the same visualization in R. This lab exercise is a part of the banking case study we have previously
Analytics Lab Malcolm Gladwell in his book ‘Outliers‘ says that it takes approximately 10,000 hours of training or practice for anybody to master a subject. There is nothing in the world that could beat hands-on practice for learning. Keeping this in mind let me start a new series on YOU CANalytics called ‘Analytics Lab’ where
This is a continuation of the banking case study for the creation of application risk scorecards we have discussed in some previous articles. You could find the previous parts of the series at the following links (Part 1), (Part 2), (Part 3) and (Part 4). In this article, we will discuss the Population Stability Index (PSI),
This is a continuation of the banking case study for the creation of application risk scorecards that we have discussed in some previous articles. You can find the other parts of the series at the following links (Part 1), (Part 2), (Part 3), (Part 4), and (Part 6). Reject Inference is a topic that separates credit
This is a continuation of our banking case study for scorecards development. In this part, we will discuss information value (IV) and weight of evidence. These concepts are useful for variable selection while developing credit scorecards. We will also learn how to use weight of evidence (WOE) in logistic regression modeling. The following are the
The Beautiful Formula Mathematicians often conduct competitions for the most beautiful formulae of all. The first position, almost every time, goes to the formula discovered by Leonhard Euler. Displayed below is the formula. This formula is phenomenal because it is a combination of the five most important constants in mathematics i.e. 0 : Additive Identity 1 :
Sherlock Holmes & Data Visualization As a kid, a friend of mine used to own a Sherlock Holmes toy kit – the source of envy for all the other friends. The kit had a Sherlock Holmes cap, a pipe, a watch and a magnifying glass. The magnifying glass was the most coveted item in the