AI-powered loan apps are booming in India, but some borrowers are missing

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(Reuters) – As the founder of a non-profit consumer rights organization in India, Karnav Shah is used to seeing sharp practices and unhappy customers. But even he was surprised by the sheer volume of complaints against digital lenders in recent years.

While most of the grievances relate to unauthorized lending platforms misusing borrower data or harassing them for missed payments, others relate to high interest rates or loan applications that have been rejected without explanation, Shah said.

“It’s not like traditional banks, where you can talk to the manager or file a complaint with the head office. There is no transparency and no one to ask for redress, ”said Shah, founder of JivanamAsteya.

“It hurts young people who are starting out in life – a rejected loan can lead to a low credit rating, which will negatively affect larger financial events afterwards,” he told the Thomson Reuters Foundation.

Hundreds of mobile lending apps have mushroomed in India as the use of smartphones increased and the government encouraged digitization in the banking industry, with financial technology (fintech) companies rushing to fill the access gap to loans.

Unsecured loan applications, which promise quick loans even to those without a history of credit or collateral, have been criticized for their high lending rates, short repayment terms, as well as their methods of repayment. aggressive retrieval and misuse of customer data.

At the same time, their use of algorithms to assess the creditworthiness of first-time borrowers disproportionately excludes women and other traditionally marginalized groups, analysts say.

“Credit scoring systems were aimed at reducing subjectivity in loan approvals by decreasing the discretionary role of a loan officer over loan decisions,” said Shehnaz Ahmed, head of FinTech at the Vidhi Center for Legal Policy in Delhi.

“However, since alternative credit scoring systems use thousands of data points and complex models, they could potentially be used to mask discriminatory policies and can also perpetuate existing forms of discrimination,” he said. she declared.

New to credit

Globally, an estimated 1.7 billion people do not have a bank account, making them vulnerable to loan sharks and the risk of being excluded from vital government and social benefits, which are increasingly dispersed by means electronic.

Almost 80% of Indians now have a bank account, in part because of the government’s financial inclusion policies, but young people and the poor often lack the official credit history that lenders use to assess creditworthiness. ‘an applicant.

Almost a quarter of loan applications each month come from people with no credit history, according to TransUnion CIBIL, a company that generates credit scores.

Authorities have supported the use of AI to create credit scores for new credit consumers, who account for around 60% of motorcycle loans and more than a third of mortgages.

The algorithms help assess the creditworthiness of first-time borrowers by analyzing their social media footprint, digital payment data, number of contacts, and call patterns.

TransUnion CIBIL recently launched an algorithm that “mapped the credit data of similar subjects who have credit histories and whose information is comparable,” said Harshala Chandorkar, chief operating officer of the company.

Women made up around 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 answer a question about the risk of discrimination of algorithms.

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

“A clear and unambiguous consent screen that articulates the data collected and the purpose for which it will be used is displayed to the user for consent,” he said.

EarlySalary, which claims its mobile loan app has garnered more than 10 million downloads, uses an algorithm that collects text and browsing history, as well as information from social media platforms, including Facebook and LinkedIn.

People who do not have a substantial social media presence could be disadvantaged by such techniques, 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 accentuated in the case of alternative credit scoring models which rely on multiple data points to assess creditworthiness, ”she said.

Arbitrary practices

Personal loan applications in India – which are primarily intermediaries connecting borrowers to lending institutions – now find themselves in a regulatory gray area.

A long-delayed personal data protection bill being discussed by lawmakers would have conditions for requiring and storing personal data, and penalties for misuse of that data.

Authorized lending platforms are advised to engage in data capture with the informed consent of the client and to publish detailed terms and conditions, said Satyam Kumar, member of the Fintech Association for Consumer Empowerment (FACE) lobby group.

“Regular audits and internal checks of the loan process are carried out to ensure that no discrimination based on gender or religion is carried out manually or through automated analysis,” he said.

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

This will help increase the value of digital loans to $ 1,000 billion in 2023, according to the Boston Consulting Group.

Digital lending will continue to favor historically privileged groups, with credit rating systems also granting loans more often to men than women in India, said Tarunima Prabhakar, researcher at Carnegie India.

If an algorithm assesses credit scores based on the number of contacts on a phone, it will likely find men more creditworthy, as Indian men have greater social mobility than women.

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

“There is almost no transparency as to how these scores are achieved,” she said.

Digital lenders justify secrecy on the basis of competitive advantage, but there needs to be some clarification, including explanations when loans are rejected, she added.

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

“In the absence of strong supervision and institutions, alternative lending can perpetuate the same arbitrary lending practices of informal credit markets that they aim to address.”


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