I came across a recent news item in Orlando Sentinel about how a Florida judge stopped a debt collections agency from pursuing an auto loan customer who was late on payments. Apparently the collections agency tracked the customer’s Facebook account and contacted her as well as sent messages to all her friends on Facebook. The judge’s ruling in the case may have highlighted privacy concerns, but what is of interest is that social network intelligence are opening up new and creative ways of understanding and managing consumer behavior.
Now, if debt collections agencies can use social networks in recovering payments - well at least until the Florida judge’s ruling – it would be of interest to know how these networks can be used in larger credit risk management initiatives by banks and financial institutions. While the regulatory / compliance / privacy angle is evolving, there is no doubt that social network intelligence provides powerful yet undiscovered dimensions to our understanding of consumer behavior.
Banks and financial services companies have always sought to better understand their customers. Their primary resource has been the credit bureaus (Experian, TransUnion, Equifax) who have a comprehensive record of credit activity of consumers. From marketing to risk management, the credit bureaus continue to be the single go-to place for insights on customer’s past and also predicting future behavior. Using their vast store of data, the credit bureaus created credit scores that were designed to predict the probability of a credit outcome such as default on a loan.
Conventional risk analytical paradigms overlay credit bureau data on set of existing customers whose performance (example payment behavior) is fully tracked internally. This would provide insights on external behavior - such as payments as agreed to other lenders or shopping for more credit - that may be useful. Matching this insight with internal data would provide risk intelligence that would be used in granting or denying additional loans / lines to the existing customers.
But what type of risk intelligence can be generated using social network intelligence? While this is unchartered territory, definitely we can identify social network behavior of customers whose payment behavior we already know. We can use the same analytical paradigm of taking a set of existing customers and match it with social networking data – travels (location), payment transactions to glean insights. For example we can track delinquent customers’ travels and see if their delayed payments arise from travel and travel related splurges. Frequent holiday travel alerts emanating from social network intelligence can lead lenders to proactively minimize / reduce / freeze credit lines. While the legal and regulatory ramifications of such actions are evolving and will be tested in courts or when regulators take a stand, it is well known that Banks have used and will not hesitate to use customer intelligence in creative ways to manage their profitability.
I see a new dimension to credit risk management opening up by using social network intelligence. There is no stopping this genie.