Can Social Data Provide an Improved View of Our Creditworthiness?
Historically, financial analysts in banks look at your credit score and other factors like loan duration and the interest rate, before they approve a loan. However, we know lives and our financial situation is never as simple as that. There are so many variables impacting your credit score that can’t be measured, so is there a better way to assess someone’s credit worthiness?
Time to rethink Credit Scores
A good scoring process shouldn’t only be used to determine the creditworthiness of an individual according to past credit.
Most credit score systems don’t consider individuals who don’t have credit cards. Given most companies are currently targeting millennials, a third of whom never had a credit card, and might not ever apply for a credit card, they won’t have a credit score. Millennials reportedly have increasing buying power as time goes by, especially as their disposable income increases. Therefore, it’s important that lending institutions consider revising their scoring protocols to capture this group.
Besides millennials, there are other people who earn very good salaries and are frequent spenders, but this doesn’t show up in their credit history. There’s also the category of people who earn a lot of money but barely spend any of it.
There are different factors that differentiate loan applicants such as their background, age, socioeconomic status, and even the reason for taking a loan. Other than that, most people don’t understand the way credit score companies work. In fact, a lot of people are not even sure whether they are eligible for loans or not.
Considering these factors and the reality that the majority of millennials barely consider financial products that affect their credit scores, like mortgages and credit cards, it might be time for financial institutions to consider other methods of rating credit scores.
It is possible to create products that give credit scores a personal appeal. These are products that consider the individual’s social appeal in aggregating their creditworthiness.
There are now ways to improve the current scoring intelligence systems and improve the ability to predict risk. Social Scoring, through data intelligence companies such as Lenddo, do just this and have been integrated into BLOCKLOAN’s infrastructure to provide a more well-rounded solution.
Enhanced credit scoring helps build on the conventional credit score systems. It optimizes the scores by adding new information from external sources that can help make predictions more accurate for different reasons.
For example, individuals who engage in high-risk behaviour, may pose a higher risk to lending institutions, over someone who earns the same amount but lives a reserved life. This introduces the concept of a behavioural credit score. Behavioural credit scores can be built on social media or from public data.
There are lots of resources out there that can be used to track the spending patterns of a customer. Buying things like a car or a house is public information. Government and official websites normally include data like school enrolment, consumer spending activity, housing survey and lots of other information from which insight into spending habits can be deduced.
One of the best ways of finding out more information about an individual is to check social media and study their lifestyle choices. This might provide some information on the possibility of defaulting on loans. It can also help lending institutions reduce the risk of default. The information that’s available on social media can help these institutions determine the spending and behavioural traits of individuals, hence present a realistic creditworthiness profile.
At the moment, many startups are using data from social media to determine the creditworthiness of their customers. We have witnessed an increase in financing startups and credit scoring companies that leverage their data on social network data. This has also seen a lot of low-income consumers come on board, creating opportunities for a lot of people who would have otherwise found it very difficult to get loans.
A Combined Behavioural Credit Score
By combining public and social data, it’s possible to create a powerful behavioural credit profile using machine learning algorithms to identify and predict trends. These can help determine high-risk loan accounts early on, and come up with relevant measures, such as offering debt-restructuring mechanisms to such customers.
Behavioural credit scores can also be used to create an accurate estimation of the customer’s risk exposure, which will go a long way in reducing the risk of bad debt. This is also a good way of determining the probability of recovering defaulted loans.
Traditional credit scoring systems lack this overall, more comprehensive, view of consumers behaviour. As consumers expectations and financial habits change, credit worthiness valuation mechanisms need to evolve and lenders will need to look at more than just credit scores. Would you be happy for lenders to understand your data for a more holistic view of your creditworthiness?
Check out the next blog on ‘The Rise of Social Scoring’..
- Tokenization of Real Assets and Loans
- Digital Transformation and Blockchain in Banking with Scott Bales