Balancing AI Speed and Human Oversight in MSME Underwriting
Non-banking financial companies are leveraging artificial intelligence to compress underwriting timelines to minutes while retaining manual oversight to manage credit risk.
- AI is primarily being used to structure unorganised business records for faster SME assessment.
- Full automation remains limited by data quality issues and diverse MSME business models.
- Human-in-the-loop protocols are essential for final credit decisions to mitigate repayment risks.
- Underwriting speed has shifted from days to minutes through automated data processing.
The Shift from Manual to Hybrid Underwriting
The MSME lending landscape in India is undergoing a structural shift in how creditworthiness is evaluated. Traditionally, underwriting for small businesses involved lengthy manual verification of physical documents, ledger entries, and bank statements. According to industry leaders at the ETBFSI NBFC Leaders’ Retreat 2026, AI is now compressing these timelines from days to mere minutes.
This change is driven by the ability of AI models to ingest and structure unorganised business records. By automating the data entry and initial analysis phases, NBFCs can process a higher volume of applications without a proportional increase in headcount. However, the shift is not toward total automation, but rather a hybrid model where technology handles the data preparation and humans handle the final risk adjudication.
Structuring Unorganised MSME Data
A significant hurdle in Indian MSME lending is the lack of standardised financial reporting. Many small enterprises operate with informal accounting practices, making it difficult for traditional credit models to assess risk accurately. AI tools are now being deployed to bridge this gap by converting disparate data points into structured formats.
Lenders are using these technologies to scan GST filings, bank statements, and even informal digital ledgers. This structured output allows for a more granular view of cash flows and business health, which was previously too time-consuming to perform manually for small ticket sizes.
AI acts as a bridge between informal business records and formal credit scoring models.
Toolyt Pulse analysis
Limits to Full Automation in Credit Decisions
Despite the gains in processing speed, NBFC leaders emphasise that credit decisions are not yet ready for fully autonomous AI. Several factors necessitate human intervention, particularly the diversity of business models within the MSME sector. A model trained on urban retail data may not accurately predict the risk of a rural manufacturing unit.
Data quality remains a primary concern. AI can process data quickly, but if the underlying records are inconsistent or incomplete, the automated output may be flawed. Human underwriters provide a layer of qualitative judgment that algorithms currently lack, especially when evaluating the character of the borrower or the local market conditions.
Managing Repayment and Portfolio Risks
The volatile nature of the SME sector makes repayment risk a moving target. While AI can identify historical patterns, human oversight is required to interpret macroeconomic shifts or sector-specific headwinds that might not be reflected in past data. NBFCs are using AI to flag potential risks, but the final 'yes' or 'no' on a loan often remains a human responsibility.
By maintaining a human-in-the-loop approach, lenders aim to balance the need for rapid disbursement with the necessity of maintaining low Non-Performing Asset (NPA) levels. This dual approach helps in fine-tuning credit policies in real-time as market conditions evolve.
- Implement AI for initial screening to filter out ineligible applicants instantly.
- Use automated alerts to highlight discrepancies in unorganised data for human review.
- Standardise data ingestion formats to improve the accuracy of AI-driven risk scoring.
What this means for execution
For NBFCs and HFCs, the immediate priority is integrating AI into existing workflows rather than replacing them. The goal is to remove the 'drudge work' of data entry and basic validation, allowing credit officers to focus on complex risk assessment. Execution must focus on the interoperability between digital lead sourcing and automated underwriting engines.
Operational success depends on how well the field force and credit teams can interact with these AI outputs. Platforms like Toolyt Pulse can assist in this execution by ensuring that data captured at the source—on the field—is seamlessly fed into these underwriting engines, maintaining the balance between speed and compliance-ready human oversight.
Frequently asked questions
Can AI completely replace human underwriters in MSME lending?
No, industry leaders suggest that while AI can reduce underwriting time to minutes, human oversight is still required due to data quality issues and the diversity of MSME business models.
What is the primary benefit of AI in the current MSME lending context?
The primary benefit is the ability to structure unorganised business records and speed up the initial phases of underwriting, making the process significantly faster than traditional manual methods.
What are the main risks of fully automated credit decisions?
The main risks include poor data quality, the inability of AI to account for diverse business models, and the inherent volatility of repayment risks in the SME sector.
This briefing is written by the Toolyt Pulse desk with AI assistance, based on publicly reported Indian BFSI news. Facts and figures are limited to what the cited source reports; everything else is clearly framed as analysis. We do not publish unverified numbers, forecasts presented as fact, or quotes that were not reported. Primary source: ETBFSI. Spotted something inaccurate? Write to hello@toolyt.com.