In 1998, an article in the Chicago Tribune succinctly articulated how loan applicants manipulating their credit score simply by writing a letter to bank officials, had now become passé. Owing to the statistical modelling technique known as credit score, the article believed that there was barely anything a borrower could do to improve their chances of receiving a loan once they have applied but there was hope. The article went on to suggest numerous possible ways in which credit scores could be improved, thus outwitting the black box of sorts. As common knowledge, borrowers were aware that factors like repayment delinquencies, high outstanding debt, length of credit history, types of credit, credit inquiries and application to new credit were the main constituents of a score. The puzzle was that nobody knew how much weight was assigned to each of these components that could directly affect their credit score. So one could never really decipher why their score was 796 and not 800!
The design of this model lacked clarity on how this system of lending would be inclusive of first time borrowers. In fact, the usual prescription was as follows: if an applicant did not have an established credit track record then opening a new account may work against your score as short-lived credit record is an indication of risk.
Time-travelling back to present day, first-time retail loan borrowers from banks count for half of all new retail applications in India. To cater to this segment credit scoring institutions like CreditVidya, Lenddo, LenDenclub (P2P) have begun measuring credit risk by using alternative credit scoring methods. These methods closely analyse borrower’s internet footprint like social media behavior and/or SMS based information as a surrogate for traditional scores that are more or less based on name, age, location, marital status to give credit scores to first-time borrowers.
How does alternate credit score exactly work?
b. Ability to repay, based on income flow and current outstanding debt
c. Willingness to repay (to avoid moral hazard)
These data points tap into payment records, credit utilization records, credit mix from past transaction to assess the credit worthiness of a borrower. Alternative credit scoring companies use these data points along with a digital footprint left through social media interactions on Facebook, Twitter, and LinkedIn to measure credit worthiness of borrowers (both fresh and old borrowers). This information stretches beyond EMI and credit card dues. One way of articulating this is the data from trusted professional networks speaks volumes about a person employment history, reputation (personal and financial) of their social network, academic and non-academic achievements, among many other factors. The fundamental idea is that a person’s day-to-day lifestyle can echo their creditworthiness more holistically than relying solely on financial transaction history as maintained by CIBIL.
Another lacuna of traditional scoring methods is that they are not very effective and inclusive in emerging economies, especially among the low-income segment and first time borrowers. These segments often do not have access to formal financial sources or have a preference for non-institutional financial sources and thus are unable to produce a record of past transactions. Moreover, the nature of these businesses is fraught with seasonality and untimely labour attrition leading to a portfolio of income-earning activities that are inconsistent in nature.
To overcome these problems, nontraditional lenders (as stated above) are tapping mobile operators, utilities, retailers, social media businesses and government to get access to new forms of data. These data points tend to be much more voluminous than traditional sources. Nevertheless, despite being novel in their approach, these methods have inherent challenges like decision makers lacking the expertise to collect, analyse, aggregate data accurately to base lending decisions on them. This is besides the ethical risks of sharing confidential data without the individual’s knowledge.
Thus, sharing of network implicitly requires consent from both borrowers and entities that hold the data (telecommunications companies, retailers, social media firms etc.) and are in a position to share these data points. Regulatory and privacy laws may prohibit lenders to get access to these data points. The advantage of having a strong privacy regulatory framework which is actively being updated enables consumers to be involved in making their own data available and accessible to the lenders and become financially included.
Globally, data science and mobile technology companies along with financial institutions are collaborating to enable their users access to a new kind of credit score, which is accurate and can be used by various lenders. For instance M-Shwari is a collaboration between the Commercial Bank of Africa and mobile network operator Safaricom through its mobile money service M-Pesa, demonstrating how a ubiquitous mobile money service can leverage to offer wider range of products to those held back due to traditional credit scores or otherwise. Today, M-Pesa is used by two-thirds of Kenyan adults, has more than 80,000 agents, and processes nearly $20 million in daily payment transactions. M-Shwari has used M-Pesa’s permeating mobile money network to enable millions of unbanked Kenyans access and benefit from banking products (savings, insurance and credit products) at scale. Currently the emerging market witnesses a rising number of organizations like Tala, Zinobe, Saida, Kiva and M-Shwari that uses a plethora of information points and analyses them through advanced data models that can instantly evaluate an applicant’s creditworthiness and repayment capacity.
In conclusion, the advent of advanced technological innovation that can financially help the masses must ensure that policymakers and regulators ascertain a fine balance between the need for consumer protection yet provide leeway for innovation that can improve access to affordable credit for the masses.
Monami Dasgupta is a research scholar at the Tata Institute of Social Sciences (TISS, Mumbai), and works at a Chennai-based financial research firm.