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Is a Credit Model Causal or Correlated?

Aroop Gupta, 9/6/2016


One of the reasons why majority of the practitioners find the disruption theory very effective is the causal nature of these theories. Causality is much more powerful tool than correlation. In line with this thought, we looked at one of the important modules of the banking system - the credit models.

Majority of the credit models today rely on data of the past to predict the future. For instance when we ask the question ‘what is the creditworthiness of the individual?’ is his/her re-payment history of credit causal or correlated. In majority of the circumstances the consumers actions are a reflection of their circumstance. What the credit models of today miss out on is the circumstance that drive the consumer’s behavior.

As an example let us take a person who has filed for bankruptcy in the past and is trying to recover. The data from the past would in a way financially handicap the individual. Also given the impact that these models today have on our day to day lives, the individual may face much more problems than just financial. I had an opportunity to interview an individual who was in a similar situation and what I figured out was exactly opposite to what correlated data would tell you.

The individual had gone through a rough patch in life and had to file for bankruptcy. Due to this his credit history was completely destroyed at one stage he was scared that it may even impact his Job. However the individual was able to balance himself and from then on displayed very good financial behavior. He has never defaulted on his payments, never has paid any interest on any credit cards, had enough savings for emergency and had a sound plan for his future. Looking at this case, a credit model would paint a picture of high risk (at least till he builds his credit back). On the contrary, when you understand the circumstance under which he had to file for bankruptcy, he appears to be of least risk (because he is unlikely to repeat his mistakes).

According to me, the challenge with the credit models is that it is built on data and heavily relies on the theory ‘success in the past guarantees success in the future’. While this theory is highly flawed, on the other hand doing the causal analysis (or Jobs approach) for the masses may not be easy. For the financial institutions given that this has been carved out as a module, majority of them have in a way lost control to tweak this component to better understand consumers. It is always challenging to go down to the next level of data behind the scores and dig deeper.

Hence can a credit model ever be causal or it would always be built on data that are only correlated. Also given that a credit model can at best be correlated who can potentially disrupt this space? Can it be the social media and tech giants that monitor our lives closely (likes of Apple, Google, Facebook etc.). think about it.