India's microfinance institutions are expanding the use of artificial intelligence across customer calls, cybersecurity and internal operations as lenders look to improve efficiency, reduce costs and strengthen risk management, according to executives.
The adoption marks a shift from experimental deployments to operational use cases, although firms remain cautious about applying AI to lending decisions, where much of the assessment still relies on field verification and human judgment.
While lenders are increasingly buying AI capabilities, some are avoiding software-as-a-service (SaaS) models for customer-facing applications, preferring to host AI models within their own infrastructure to retain control over sensitive borrower data.
"We are not taking SaaS-based AI solutions. The agents are hosted on our own infrastructure. We are using Azure, but the hosting is on our infrastructure itself, so no data is flowing outside," said Avinash Yadav, chief information officer at Spandana Sphoorty Financial Ltd. The listed microlender has a market capitalisation of ₹2,117.37 crore. On Thursday, the company's shares closed up 0.8% at ₹265.55 on BSE.
Belstar Microfinance—an arm of Muthoot Finance Ltd with assets under management of ₹8,222 crore—follows a similar approach. Dhanasekaran S., chief technology officer at Belstar Microfinance, said the company keeps its AI running within systems it controls rather than sending customer data to external AI providers, giving it greater control over sensitive information.
Another microlender, Fusion Finance, meanwhile, stores customer information on Amazon Web Services (AWS), with additional safeguards around sensitive information. Fusion has assets of ₹7,407 crore.
- Microfinance lenders deploy AI for calls, fraud detection, and cybersecurity operations.
- Companies prefer hosting AI on their own servers, avoiding third-party SaaS data risks.
- AI now handles most collection calls, improving loan officer efficiency by 15-20%.
- Credit assessment stays manual; household income data remains too unstructured for AI.
- Industry body sees early AI underwriting pilots, but stresses human judgment is essential.
"All customer data is on our AWS cloud in a secure environment. Aadhaar information is encrypted. We have also developed an in-house AI model to automatically mask historical Aadhaar records so auditors only see masked data," said Sushil Menon, chief information officer and interim CISO, Fusion Finance.
Automated collection calls
One of the biggest applications of AI has emerged in customer calling, particularly collections, where lenders previously relied on large teams of callers and field staff.
Fusion Finance explained that AI now handles nearly all collection calls, replacing manual calling with multilingual AI systems.
At Spandana Sphoorty, AI acts as the first point of contact before field officers visit borrowers.
"Instead of sending field staff directly, AI can first call customers, check whether they have funds, ask when they will be available, and even send a payment link if they are ready to pay. Only after trying all these channels does the physical visit happen," said Yadav.
He estimates that if even 30-40% of overdue accounts can be resolved remotely, field staff spend less time travelling and more time serving customers.
The gains are already visible. "Earlier, if my loan officer was handling 50 customers, now that loan officer is able to handle 60 customers. There is a straightaway 15-20% improvement in efficiency," he said.
Lenders are also using AI to detect fraud before and after loans are disbursed. At Spandana Sphoorty, AI-powered welcome calls verify loan details directly with borrowers.
"The AI confirms the loan amount, tenure, interest rate and whether any additional amount was asked for during disbursement. Customer responses are logged, and a deviation report is generated automatically," Yadav said.
Fusion Finance scans loan portfolios for anomalies before triggering automated customer calls that feed into its fraud investigations. The lender is also introducing AI-based face matching and liveness detection during centre meetings to automatically verify attendance and ensure borrowers are physically present rather than represented by photographs. Liveness detection verifies that a face scan comes from a real, live person rather than a photo or video.
Belstar uses AI for early warning signals, customer ratings, risk assessment and productivity monitoring.
Where firms draw the line
Despite wider adoption, executives said AI is not yet ready to replace credit assessment in microfinance.
"For now, credit assessment is something where we are not exploring AI. In our segment, everything has to be analysed manually. Household income, living conditions and family details are still largely cash-based and cannot easily be captured through AI," Yadav said.
Belstar echoed that view, saying the lack of structured customer data creates unique challenges.
"The majority of MFI (microfinance institution) customers may not have active alternate data or consistent financial data available. This may expose or lead to hallucinations that adversely impact AI outcomes. Hence, additional guardrails, filtering and continuous training are needed in our industry's AI adoption," Dhanasekaran said.
Jiji Mammen, executive director and chief executive officer of Sa-Dhan, said AI adoption is in the works, citing an MFI tool that questions borrowers in any language to assess eligibility, assets, repayment capacity and intent.
"There is huge potential, but I'm not going to say AI can substitute the human touch. Human involvement will continue, and AI can only provide a better assessment in addition to it," said Mammen.
A faster cyber threat landscape
As firms adopt AI, they are also using it to defend against increasingly sophisticated cyberattacks.
"Earlier, we would see one or two cyberattack attempts in a quarter. Now those attempts have increased," Yadav said, adding that AI had become necessary to counter emerging threats.
Fusion Finance has integrated AI into its security operations centre, where Google SecOps—a cloud-based cybersecurity platform—analyses network and cloud logs to detect anomalies and issue real-time alerts, while Belstar has deployed AI-based monitoring tools as part of its cyber defence strategy.