AI-powered loan applications boom in India, but some borrowers are overlooking



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(Reuters) – As the founder of a non-profit consumer advocacy organization in India, Karnav Shah is used to seeing harsh behavior and disgruntled customers. But even he was surprised by the sheer number of complaints against digital lenders in recent years.

While most of the complaints relate to unauthorized lending platforms, misusing borrowers’ data or harassing borrowers for missing payments, other complaints relate to high interest rates or loan requests that were rejected without explanation, Shah said.

“These are not traditional banks where you can talk to a manager or file a complaint with the head office. There is no transparency and no one to ask for remedies, ”said Shah, founder of Jivanam Asteya.

“It hurts young people starting their lives – refusal to issue a loan could lead to a credit rating downgrade, which would later negatively affect larger financial events,” he told the Thomson Reuters Foundation.

Hundreds of mobile lending apps have sprung up in India as the use of smartphones has increased, while the government has encouraged digitalization in banking, and financial technology companies (fintech) are rushing to fill the credit gap.

Unsecured loan apps that promise quick loans even to those with no credit history or collateral have been criticized for high lending rates, short maturities, and aggressive recovery methods and misuse of customer data.

At the same time, their use of algorithms to estimate creditworthiness of new borrowers Analysts say women and other traditionally marginalized groups are disproportionately excluded.

“Credit scoring systems were designed to reduce subjectivity in loan approval by reducing the discretionary role of the loan officer credit solutions“Said Shehnaz Ahmed, head of financial technology at the Vidhi Legal Policy Center in Delhi.

“However, because alternative credit scoring systems use thousands of data points and complex models, they have the potential to be used to mask discriminatory policies as well as perpetuate existing forms of discrimination,” she said.

Newbie in credit

Globally, about 1.7 billion people do not have a bank account, leaving them vulnerable to money lenders and at risk of being deprived of vital government and social benefits, which are increasingly distributed electronically.

Nearly 80% of Indians currently have a bank account, in part as a result of government financial inclusion policies, but young people and the poor often do not have official credit histories that lenders use to assess an applicant’s creditworthiness.

According to TransUnion CIBIL, a company that generates credit ratings, almost a quarter of loan requests every month come from people with no credit history.

The authorities supported use of AI to create credit ratings for so-called newbies in lending, which account for about 60% of motorcycle loans and more than a third of mortgages.

Algorithms help assess the creditworthiness of new borrowers by scanning their social media footprints, digital payment data, number of contacts, and call patterns.

TransUnion CIBIL recently launched an algorithm that “displays credit data from peers that have credit history and are comparable,” said Harshala Chandorkar, the firm’s chief operating officer.

Women made up about 28% of retail borrowers in India last year, up three percentage points from 2014, and have a slightly higher average CIBIL score than men, she said, without answering a question about the risk of discrimination from outside. algorithms.

CreditVidya, a credit reporting company, uses an artificial intelligence (AI) algorithm that uses “over 10,000 data points” to calculate its scores.

“A clear, unambiguous consent screen, which indicates what data is collected and the purpose for which it will be used, is displayed to the user in order to obtain their consent,” the message says.

EarlySalary, which claims its mobile lending app has over 10 million downloads, uses an algorithm that collects text and browsing historyas well as information from social networks, including Facebook and LinkedIn.

People who don’t have a significant social media presence could be at a disadvantage with such methods, Ahmed said, adding that many online lending platforms provide little information on how they assess creditworthiness.

“There is always an element of subjectivity in determining creditworthiness. However, this is amplified in the case of alternative credit scoring models that rely on multiple data points to assess creditworthiness, ”she said.

Free practices

Personal lending apps in India, which are mostly intermediaries linking borrowers to lending institutions, are now falling into a gray area of ​​regulation.

Lawmakers are debating a long-delayed personal data protection bill that would provide conditions for requesting and storing personal data, as well as penalties for the misuse of such data.

Authorized lending platforms are encouraged to collect data with the informed consent of the client and publish detailed terms and conditions, said Satyam Kumar, a member of the Fintech Association for Consumer Empowerment (FACE) lobbying group.

“Regular audits and internal reviews of the credit process are carried out to ensure that there is no discrimination based on gender or religion, either manually or through machine analysis,” he said.

India’s central bank said it will develop a regulatory framework that “supports innovation while ensuring data security, privacy, privacy and consumer protection.”

This will help drive the cost of digital lending to $ 1 trillion in 2023, according to the Boston Consulting Group.

Digital lending will continue to be biased towards historically privileged groups, with credit rating systems also more likely to lend to men than women in India, said Tarunima Prabhakar, a researcher at Carnegie India.

If the algorithm ranks credit ratings based on the number of phone contacts, it is likely to find that men are more creditworthy, as Indian men are more socially mobile than women.

Thus, women may face loan rejection or higher interest rates.

“There is virtually no transparency on how to achieve these results,” she said.

“Digital lenders” justify secrecy on competitive advantage, but some clarification is needed, including an explanation for when loans are declined, she added.

“If these platforms make it easier for men, but not women, to start small businesses, it could reduce female influence in an already asymmetrical power dynamic,” Prabhakar said.

“In the absence of strong controls and institutions, alternative lending can perpetuate the same arbitrary lending practices in the informal credit markets that they seek to permit.”


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