Consider two young professionals Vikram and Priyanka, both with similar backgrounds in terms of income and education and living in a metro city such as Bengaluru. Both apply for credit to buy a laptop. But Priyanka gets a bigger credit line than does Vikram, and it may very well be because of her social media profile with several photos of her eating out at expensive restaurants and travelling to exotic locations. That indicates her spending ability, making her a worthy borrower.Bank statements and salary slips remain the most crucial factors considered in giving loans. But the hundreds of alternative data points being fed into risk analytics software developed by new-age lending companies are also significantly shaping an individual’s credit score. These data points keep growing and getting embedded deeper into a person’s life, spanning everything from their political affiliations to their friends list.Lending criteria essentially revolve around understanding a person’s repayment capacity, intent to repay loans and fraud risk. It is on evaluations of intention and fraud risk that digital lending companies are marching ahead of traditional lenders ; many boast of lower default rates than that of the banking sector. One primary criterion for evaluation is social media and the various data points these platforms throw up—for example, a person’s political activity.“If someone is politically active and engages in political campaigns, which are visible through their social media profiles, it is not a good sign since we do not want to go through the hassles that may come up in collection. It shows that the person can raise issues at the time of collection in these political groups,” said the founder of a lending platform, requesting anonymity. Having worked with a leading bank for several years, this person said banks also look at these factors but manually. “Usually, a bank official visits the house of a (potential borrower) and such photographs and signage of political parties at home are taken into consideration.” On social media, some of the big giveaways of a person’s involvement with political parties are photographs showing them posing with politicians, photos taken at political rallies and comments that may be political in nature.“While everyone can have an affiliation to a political party, the level of engagement and involvement can be a factor to consider,” the founder said, adding that his lending platform so far has not rejected anyone based on this criteria since it have not received such applications. “But it is a data point we consider.” As one of the fastest growing spaces that is touted to become a hot sector in 2017 given the explosion of data points that have come through the digitization post the cash crunch in the last two months, alternative lending has evolved on the back of home-grown technology. These companies collate thousands of data points, including the seemingly mundane aspects of a person’s daily life, to make the right lending decisions. For loan-seekers, there is nothing more crucial than getting the money when needed with minimum paperwork.“I wanted to buy a Macbook. The interest rate was very minimal, and the EMIs and down payment very pocket-friendly,” said Dhiraj Kumar Agrawal, a first-year BBA student at Bengaluru’s CMS Jain University, who secured a Rs 60,000 credit limit from SlicePay, a lending platform focused on students. Agarwal had to provide copies of his college id card and a few other documents digitally. “We also had to connect (SlicePay) to our Facebook account, click a selfie and give our digital signatures,” he says.At SlicePay, one of the data points taken into account is the number of photographs taken at restaurants or movie theatres that an individual puts up. “If a person checks into a particular restaurant, we know the price point. If a student puts up a lot of photos from restaurants and other such places, we can understand the person’s spending pattern and that they have an ability to pay,” said SlicePay cofounder Rajan Bajaj. Another indicator on social media is the friends list, to check if there are any people there who may have taken credit from SlicePay before and if they were good borrowers. The Blume Ventures-backed company, earlier known as ‘Buddy’, has given credit to students across 250 colleges in Bengaluru. “In some cases, especially for waitlisted applicants, we also consider academic scores to evaluate the borrower,” Bajaj said.Every lending company—including those that lend from their own books, operate as marketplaces for lending or only work on credit risk analysis—has its own algorithm that throws up credit scores based on the data points they gather. “The score is the probability of an individual being a good or bad borrower. The better the predictive power of the score the better it is to differentiate,” said Abhishek Agarwal, cofounder of credit risk analytics startup CreditVidya that provides credit scores to eight banks and non-banking financial companies. Agarwal says the startup’s proprietary algorithm calculates more than 10,000 data points for each applicant.One variable is the call history. “If we get references from the applicant, we check the frequency of their communication through telecom data,” Agarwal said. While it may seem an irrelevant data point to consider and may make a small part of the overall consideration for lending, call patterns, according to Agarwal, show the “level of stability” of an individual—in this case, the stability in their relationships. “The consistency of interaction over a period of time shows stability.” Asked if this could turn into a privacy issue, Agarwal said all data points are looked at after taking consent. Call data is accessed through phone bill records.Lending to small and medium businesses is considered a bigger risk in India because of a paucity of data points and credit scores. Traditional lending institutions use a few key data points to gauge the financials of the company such as books of accounts, balance sheets, bank statements as well as the applicant’s loan repayment history, Cibil scores if any, and collaterals provided for securing the loan. However, new-age lending platform such as Capital Float, which is a registered NBFC, also consider several unconventional data points over and above these traditional ones.“Some of the direct data points that we consider are the time of day when an application is submitted, the number of words that an applicant uses while describing his business and use of personal email id vs business email id. These points give us a sense the seriousness of the person and indicate how they approach planning of the business. We also use digital footprints of the applicant like software updation of his mobile, especially if the applicant is an Uber driver or a seller on an ecommerce platform. It shows whether they are tech-savvy since the key part of their business will be the ability to use technology,” said Capital Float cofounder Sashank Rishyasringa. He said that over the last three years, thousands of data points have been added to their decision-making algorithms, which currently uses approximately 12000 data points.A banker for over two decades with HDFC Bank and Yes Bank, Manavjeet Singh, founder of SME lending marketplace Rubique, has built a scorecard called the ‘Rubique magic score’ to give a report of alternative data points over and above the data policy that banks put forth while lending through the platform. “In SME lending, each product category has its own set of risks and hence a separate set of data points that shows the viability of the business. For example, we recently got an application for a Rs 2 lakh loan by a person who wanted to start a bakery. For such loans, we consider data points such as the vicinity in which the person wants to start the bakery in, profile of customers, the income groups in the area, existing bakeries in the area, etc.,” Singh said. All these data points are collected digitally, through information available online.One of the important factors SME lending startups look at is mobile GPS data, which shows the locations visited by an applicant. One such trend this data throws up is if the business owner regularly goes to his place of work. Online lending platforms such as Capital Float also conduct psychometric tests of applicants to evaluate the person’s entrepreneurial ability and financial planning behavior. Lending marketplace CoinTribe peeps into LinkedIn for details about employees as well as companies that SMEs work with. “We rate the companies according to our own rankings, based on the number of followers they have and the employees working there and their backgrounds. This gives us a good understanding of the businesses that an SME works with,” said CEO Amit Sachdev.Some alternative lending companies, however, believe that despite this explosion of data points, it all boils down to a handful of key factors. “There is a popular myth in the industry that we need thousands of data points. What we are finding is that if we can spot the right data, we can rely on very few data points to do successful underwriting. In our view, it comes down to 15 to 20 data points when it comes to credit relevance,” said Alok Mittal, cofounder of Indifi Technologies, a platform that connects institutional lenders to SME borrowers.“The key factors to assess are business health, for which we get data points from income tax returns, customer and supplier data, bank statements. Supply chain data is unconventional and we look at the purchase history, how much they are buying and from whom,” Mittal said. “Another factor to consider is the potential growth of a business, for which we look at the macroeconomic view of the industry. We also assess the promoter of the business on their credit behaviour and asset ownership. The data points come from credit bureaus, social media networks, etc.”But the rate at which such credit lines and loans are being extended by alternative lending platforms has raised worries about bad loans piling up. “We have to be careful about the possibility of rise in bad loans, especially in the student loan categories. There is inherent risk in these loans. Transparent lending practices are important,” Mittal said, recalling the crisis the microfinance industry underwent before forming a selfregulating body. Mittal’s Indifi Technologies, along with nine other digital lending firms, recently came together to form a formal association to represent the sector and engage with the government. This body will also prescribe industry fair practices. “We need to set the right lending practices. We are trying to learn from the MFI industry,” he said. It may be a while before loans start souring, given that many of these startups have been lending only in the last two-three years.