Moneyview automates 65%-70% of collection calls as credit cost hits 4.9%

Moneyview said AI voice bots now handle 65%-70% of collection calls, while annualized credit cost declined to 4.9% from 5.3% a year earlier. Management said its near-term objective is to keep credit cost below 5%, using customer-scoring models, automated collection queues and human teams.

How does Moneyview automate loan collections?

Moneyview automates 65%-70% of its collection calls through in-house artificial intelligence, or AI, voice bots, according to Puneet Agarwal. Management said the bots undertake much of the early-stage collections work and are trained using the approaches of the best-performing human agents. The stated aim is to add calling capacity beyond what the company could achieve solely by hiring more staff.

The automated calling process sits alongside a large human collection operation that includes in-house callers and outsourced agencies. Moneyview did not provide the number of employees or agency staff in its collection team during the call, because the moderator interrupted the follow-up question on team size. The disclosed 65%-70% automation figure therefore describes calls rather than the share of collection recoveries completed without human involvement.

Agarwal linked the AI voice-bot approach to early intervention. The company said it can contact customers very early in the collection cycle and apply a tailored strategy based on the customer’s predicted level of distress. That design matters because the company’s stated credit-cost target is below 5%, rather than a target based on call volumes or headcount reduction.

What happens before a Moneyview collection call is made?

Moneyview uses models at the beginning of each month to estimate whether a customer may miss a payment and how severe that missed payment could be. Management said every customer is enabled for automated repayment collection, and the models classify customers into low-, medium- and high-distress groups. Its technology then automatically routes each group into specialised queues and collection strategies.

This routing system seeks to match the intensity of collection activity with an individual customer’s predicted risk. Management described the process as automated from the scoring stage through queue assignment, rather than a manually built calling list. It did not disclose bounce-rate figures for the first or second quarter of FY27, but said collection-efficiency data through the 30-days-past-due stage had been provided in its presentation.

Moneyview also uses the data from each digitised call to revise its systems. Agarwal said calls are recorded and digitised, allowing feedback to flow back into the models to improve bot efficiency and effectiveness. That feedback loop is a disclosed mechanism, not a quantified performance measure: the company gave no recovery-rate comparison between AI calls and calls handled by human agents.

Is Moneyview's credit cost below its 5% target?

Moneyview reported annualized credit cost of 4.9%, below management’s stated target of keeping the measure under 5%. Saurav Goyal defined the measure as credit cost divided by average assets under management, or AUM, across the platform. The 4.9% reading was down from 5.3% a year earlier, a 40-basis-point improvement, where 100 basis points equal one percentage point.

The quarter-on-quarter movement requires a different reading. An analyst calculated that annualized credit cost had increased by roughly 10 basis points, from 4.8% to 4.9%. Goyal said annualisation explained that 10-basis-point effect and that the movement on an absolute quarterly basis was about 2 basis points, with seasonality from January and February contributing to the normalisation at 4.9%.

Management said it expected credit cost to stay around a similar rate for the rest of the year and did not see a material change in collection efficiency, which it said continued to improve month on month. The sub-5% objective is management’s target, not a guarantee. Its longer-term expectation is for further improvement through better models, a higher share of repeat customers and the ability to attract better-quality customers as funding costs decline.

Which cost and customer metrics support Moneyview's plan?

Moneyview said collections automation is one part of a broader operating-cost plan that has reduced customer acquisition cost, or CAC, to 1.6% of disbursals in the quarter ended September. That compared with roughly 1.9%-2.0% a year earlier. Goyal attributed the decline to a growing direct business, repeat customers and mix changes, while agreeing that CAC for a newly acquired customer was probably unchanged.

Repeat business accounts for almost 65% of Moneyview’s business, management said. Of new customers, about 75% are acquired directly, with roughly half of that direct flow organic and half paid marketing. The remaining 25% of new customer acquisition comes through embedded journeys with consumer-internet partners, where Moneyview’s risk-assessment systems are integrated with partner data.

Management also corrected its description of the operating-expense ratio during the call. The metric is operating expenses as a percentage of total income, not as a percentage of AUM. It fell to about 35% in the first half from about 39% in the first half of the prior year, and to 34.5% in the current quarter from 35.9% in the preceding quarter.

Goyal cited lower collection cost, lower CAC and technology and workforce efficiencies as reasons for the trend in operating expenses. Agarwal added that almost 80% of code written by engineers is now AI-assisted, while servicing is also shifting toward AI-assisted channels. Those measures are part of management’s operating-leverage explanation, but the company did not quantify the individual savings attributable to voice bots, coding tools or automated servicing.

Conclusion

Moneyview’s disclosed collections architecture combines monthly payment-risk scoring, automated routing, AI-assisted calling and human agents. The central reported outcome is that AI voice bots now make 65%-70% of collection calls, while annualized credit cost declined to 4.9% from 5.3% year on year. The company also reported lower CAC and a lower operating-expense ratio, indicating that its automation strategy extends beyond collections.

The next measure to watch is whether Moneyview maintains annualized credit cost below its 5% target for the rest of the year while collection efficiency continues to improve month on month. Management also expects operating expenses as a percentage of total income to improve in the second half, while it continues to add lending partners and seeks lower funding costs that could influence customer quality and platform margins.