Potential of Alternative Data in Credit


Credit is something which nowadays has become a necessity. It is required to build a home, start a business or even go for higher education and the list goes on. One can’t imagine leading a life without requiring a credit at least once in a lifetime. And, accessing credit is not easy. There are multiple reasons because of which people face barriers to accessing the credit. The reasons for facing such barriers include late payments, pending payments, no or a little credit history etc. These all affect the credit report negatively which is calculated as per the parameters set by the Credit rating agencies. These parameters have made the maximum population of India deprived of this amazing lending feature.

The Need for Alternative Data


Now, the Traditional parameters set by the leading agencies have made it impossible for the maximum Indians to take advantage of credits. According to a survey, more than 75% of Indian citizens (which comes out to be fairly 500 million) are still not having access to the formal credit. This is happening because the credit bureaus do not have a proper and sufficient credit-history of the applicants. Lending only to the 25% population doesn’t make any sense and hence, the government and FinTech companies are coming forward with a solution to this so that credit is available for all the citizens.

Alternative data, therefore, came as a solution to solve this problem of lending the credit. Alternative data is used to evaluate the credit-worthiness of the applicant using non-traditional sources like transaction data, telecom, and rent data, social media profile data etc.

Alternative data is gaining popularity among the FinTech companies especially after the decision of the World Bank to introduce a legal framework for using the Alternative data.

Many countries like U.S., Africa, and Philippines etc. have benefitted by using Alternative data for financially including the unbanked and underbanked population. It is high time for India even to go for these basic practices of data acquisition.

Sources of Alternative Data


Let us take a look at the non-traditional sources using which Alternative data is calculated:

Transaction data: Transaction data includes the behavior of the applicant regarding the usage of his/ her debit card and credit card. It includes evaluation of all the transactions made by the person. Collecting Transaction data may be a little time consuming but it gives a very clean and valuable data that is helpful in deciding whether to grant credit to the person or not.

Social media profiles: Evaluating the Facebook, LinkedIn, Instagram, Twitter and other social media profile also gives some useful insights on the credit applicant’s behavior. The number of posts and their frequency may lead to understanding the lifestyle and expenditures of the applicant. This data may not be very useful but cannot be ignored either.

Social Network: Delving a little more into the social network of the credit applicant is an extremely helpful way to check the creditworthiness of the applicant. The advancement in the technology easily enables to check for the people related to the applicant and all the accounts active or not of the applicant. One can check the behavior of the related connections of the applicant in case the applicant is having no credit history. Social network evaluation helps to identify the risks involved with the applicant.

Telecom/ rental data: One can even check the history of the applicant by checking the records of a payment history of the telecom bills and other rents. The behavior followed in making payments of these bills will help you to know the risks involved.

These were just a few sources of Alternative data but there are still others which are used to evaluate the creditworthiness of the credit applicant.

Interpretation of Alternative Data


Analytical technologies and machine learning like neural networks, stochastic gradient boosting etc. are the basis of Alternative data. The information is available in the form of huge and unstructured data sets which are sorted out and processed using this smart technology.

Data scientists very smartly implement the AI so that the development process functions smoothly and identify the data patterns relating to the credit risk. They make sure the output is completely reliable, strong and explainable.

Using Alternative data has the potential of making the credit accessible to the 75% population of India who lacks traditional credit score. Many FinTech companies have started using the Alternative Data approach while granting the credits to the applicants and as a result, it has increased financial inclusion within the country. There should be no delay in constructing a framework for Alternative data for the consumers to be treated fairly and responsibly.

  • May 10, 2018