Tuesday, January 25, 2022
HyderabadFintech companies use alternate data to build credit profiles

Fintech companies use alternate data to build credit profiles

Published: 6th Dec 2021 12:00 am

Hyderabad: Small businesses in the unorganised segment are seldom in the good books of banks and lenders. Lack of financial transaction history to assess the creditworthiness of businesses or people is one of the reasons for banks shunning them. However, newage fintech companies are coming to the rescue by using alternate data for assessing the credit profile of such entities. The banks and NBFCs use the assessment report while underwriting new loans or enhancing the existing loans.

For instance, city-based CredRight uses chit transaction data to assess the credit worthiness of small businesses. “Many businesses subscribe to chits. This tells us about their cash flow and their repayment capacity. The same might not be captured by the credit bureaus,” CredRight founder Neeraj Bansal told Telangana Today.

CredRight’s customer segment is self-employed and micro businesses. These customers are referred to as ‘thin files’. Consumers who are just starting out and may never have taken a loan or had a credit card are said to have thin files. They face difficulty in accessing institutional credit. CredRight uses behavioural data, bill payment data and automated stock recognition through images to assess the customers and other points to assess them on more than 300 parameters.

Its machine learning-based credit assessment platform analyses the attributes and builds a credit profile of the customers. “Based on the credit profile, we are able to facilitate unsecured loans of up to Rs 7 lakh. About 90 per cent of our customers have never received loans of more than Rs 1 lakh. This increased loan limit will help them expand their businesses. Since the alternate data is accessed digitally, we are able to get the loan disbursed to customers’ accounts within three days,” he said.

Another player Algo360, which also has presence in Telangana, collects digital footprint from SMSes, device data, emails, credit bureau reports and account aggregator data. It generates Algo360 Score. A zero score depicts that the applicant has no usable data on their phone. A score of 240 plus is considered decent while 360 is the maximum score possible. Higher the score, lower is the risk. It also creates a Data Quality Score (DQS), which ranges from zero to 100. In this, it checks for the number of transactional SMS and data vintage. A DQS greater than 25 is considered good and implies that the data is reliable, said Amit Das, CEO and co-founder of Think360.ai, the parent company of Algo360.

Algo360 also considers monthly inflows, number of cheque bounces in the last three months, number of accounts held, average monthly balance, credit cards during their lifetime, average transactions and monthly spends. It also considers bill payment for utilities like the mobile phone, landline, internet and, DTH, monthly EMIs, number of wallets and others. Algo360 uses 800 plus data points to analyse the creditworthiness of the new-to-credit customers and enable them get loans. “Alternate data lowers payment defaults by 23 per cent and also improves loan approval rates by a similar percentage,” said Das on the use cases of the reports it generates.


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