Risk Weighted
When narrative scenarios first became a standard tool for risk management, around the time of SCAP in 2009, I was frankly skeptical that the technique would last. Having emerged from academia, I was used to more rigorous methods and had spent years coding up very detailed and complex Monte Carlo experiments. I always thought that simulation methods, aided by advances in computing power, would eventually replace narrative scenarios for stress testing.
When it comes to both the present and the future, which financial institutions are in a better position: those that rely on traditional, structural credit risk models or those that lean toward machine-learning models? Accuracy, intuitiveness, adaptability and behavioral shifts are among the factors that should be evaluated when answering this question – but we must first consider how different types of models have performed in 2020.
Can disruptive technologies ever truly and completely replace human judgment in finance?
We can begin our search for answers by considering automation progress in other industries. In the world of self-driving car technology, for example, it is common to talk of five levels of automation.
In recent years, attention has increasingly turned to the promise of artificial intelligence (AI) to further increase credit availability and to improve the profitability of banks and other lenders. But what is AI?
A few years ago, I was lucky to hire an excellent summer intern from a leading economics PhD program in Europe. At the time, Lending Club made their historical performance data public and they included in the file a brief written request (likely penned by the prospective borrower), urging investors to fund their loan. I asked my intern to explore whether a quantitative treatment of the text would be useful in assessing the subsequent credit risk of the observed consumers.
Let’s say I recently ventured to a Himalayan mountaintop. There I met a strange hermit who bestowed on me a precious gift: the specification of the true model for predicting credit card default.
Risk Weighted 