The credit scoring and underwriting landscape is undergoing a paradigm shift with the use of enterprise and social data. The banks are planning on utilising the current organizational capabilities including the infrastructure and skills to offset the credit risk associated with borrowers. Scoring models can now use customer consent data from credit bureaus, social media, other lenders, enterprise customer data, publicly available data, location data, mobile data, social footprint, page views, web and behavioral data. These fragments of data together will provide a complete, coherent and a cohesive model that can score credit applications. This has led to the creation of new business models and in turn is leading to economic dependence on the data analysis.

These advancements are now being increasingly used by payday and student loan lenders. Loan applications are being evaluated real time within fraction of seconds, to provide a decision. At the same time, the credit risk posed by the customers to the banks can be mitigated.

The retail adoption of Big Data for efficiency, customized user experience, personalization, marketing and sales is on a high curve when compared to the financial industry. Banks are planning to catch up on this trend to provide a similar experience to the customers. Scoring efficiency coupled with straight through processing and mobility has made it much easier for borrowers to acquire credit products with comfort, similar to an experience in retail transaction.

When it comes to availability of data, there are around 10,000 to 15,000 variables that can be used to provide an input to the scoring models. The banks do have enormous amounts of structured and un-structured data that they are planning to use in underwriting.

Big Data scoring is changing the underwriting landscape as -

• Customers who do not have a credit score available with the bureaus can avail credit products.
• Geographies which do not have access to private bureaus can now underwrite loans with reduced credit risk.
• Efficiency of scoring and underwriting is increasing multifold.
• With mobility and apps, the customer experience is improved with less TAT.
• According to a recent study, the bank reduces the risk of default by 10% - 20%.

With every application decision, the model can be fine-tuned to achieve further efficiency. One can find co-relation between thousands of data elements with most of them making sense to analysts while some still remain a mystery. It is amusing to note that a Big Data scoring service provider mentioned that the probability of default in customers with a middle name is much lesser to the customers that do not have one. On a funny note, I don’t think that banks will stop lending to customers without a middle name or the new borns will be provided middle names to assist them in scenarios similar to this in future. Banks usually use Fico score with custom scores to decision an application. It is time for banks to look at the capabilities that a Big Data scoring solution will provide in scoring credit applications.

If banks are looking to foray into emerging markets, they will face a challenge of unavailability of credit bureau data. A recent survey states that 72% of the world’s population is not covered by credit bureaus. While some banks are leveraging the advancements in credit scoring, there are others who are yet to kick start acquiring these capabilities. My suggestion would be to improve the efficiency of their existing models, use feedback mechanism and thereby increase the business benefits arising from such solutions.