Smile, you are being scored!

We are gradually getting used to the idea that our data are being analyzed whenever we go to the bank to apply for a new loan or whenever we try to buy a more expensive item such as a car. But the truth is that we are being scored far more often than those instances when we ourselves take some form of initiative.

I would guess every person is scored at least three times every day. Think of the following examples. You are being scored in a behavioral scorecard at your bank to predict whether or not you are going to default in the next 12 months for each of your credit products. You are being scored by your telco operator to predict whether or not you are going to leave them in the next three months. And of course you are being scored by your credit card provider each time you use your credit card.

Social network analytics

All of the above examples are perfect illustrations of the power of logistic regression and the numerous applications in everyday life. Many companies are continuously using analytics and big data as a means to monitor behavior, to accurately predict future behavior and to modify their approach accordingly.

It can also be used to perform so-called social network analytics. Social networks, in this context, are not just the social media sites such as Facebook, Twitter and LinkedIn. You should rather think of any network of nodes which are connected in a certain way. In a telco setting, this network could consist of the customers being the nodes and the phone connections being the edges. In the banking sector, the nodes would be the bank accounts and the edges would be the money transfers. Representing these as a social network can contribute significantly to churn prediction and fraud detection respectively.

Big opportunities and big challenges

However, as the famous philosopher Spiderman once said: “'with great power comes great responsibility”. If we are able to know so much about the people we do business with, we should also be aware of the consequences, such as the impact on privacy. This is a concern for the organizations: you need to comply with local regulations in some cases, and with industry-specific constraints in others. And moreover you want to act as a sustainable and reliable partner within your ecosystem. But it is even more a concern for consumers and individuals. Especially the younger generation is hardly concerned with the challenges and dangers related to big data. Many applicants are completely surprised when they realize how much their future employer can find out about them just by checking all publicly available information. Big data means: big opportunities, but also: big challenges, that everybody should be aware of.