There are many, many applications for BI in the world today. They are very varied, and to show another I’m going to pick on the sensitive subject of credit worthiness.
Credit Worthiness Myths
Three of the most talked-about phenomena in the telecommunications industry today are fraud, churn and credit worthiness. Whilst Data Warehousing is often seen as a potential solution to the first two, it is rarely seen as a vehicle for tackling credit worthiness. This post seeks to address this and to show that a Business Intelligence environment holding historical and detailed data regarding usage, billing and payment information is the ideal place to detect and predict credit exposure. In fact, we can see that the three phenomena mentioned are all part of a bigger picture. Ability and willingness to pay bills is a key factor in determining churn – people committing fraud usually churn very quickly, and of course, anyone deliberately using services with no intention of payment is in fact committing fraud. It would seem that a solution to all three might lie in the same area.
Let’s start by examining some misconceptions about credit such as:
- Ø All ‘poor’ people have a propensity to default on payments.
- Ø Certain types of people are born with the inherent propensity to default on payment.
- Ø Once a credit risk, always a credit risk.
- Ø All people who default on payments are ‘bad’ customers.
The perceptions above are very common in a world that doesn’t understand credit worthiness except in the most rudimentary terms. From experience we find that the truth is a little different. In fact:
- Ø A great deal of the credit risk a company must contend with is a calculated result of sophisticated fraud – nothing to do with people’s individual propensity to pay bills at all.
- Ø People go through phases in their lives when they move toward and away from the propensity for payment default. Whilst it may be true that a hard core of people will never default because of a strict moral code, one simply cannot pay if funds are not available, no matter what ethics apply.
- Ø Similar to the above, we often find that as students and young adults, defaulting on payments doesn’t seem to have any real consequences (moral or otherwise) and so such people are often very lax when making payments, whether funds are available or not. As age brings experience, such people usually become model and profitable customers.
- Ø Traditional credit risk systems provide processes to deal with people after the risk is realised. However, the real state of the art is to predict who will default and take action before it happens.