Chapter 1.5Employment, Wages and Livelihoods
Income is the first term of the household macro balance sheet, and employment is where income originates. The microfinance borrower household is rarely composed of employees but of own-account workers, casual labourers, unpaid family workers and circular migrants — categories conventional employment statistics were not primarily designed for.
Indian Microfinance: Resilience, Responsibility & Inclusion Ramesh Srivatsava Arunachalam
1.5 Employment, Wages and Livelihoods
Income is the first term of the household macro balance sheet, and employment is where income originates. Any labour market analysis for this readership must begin by acknowledging what it is analysing: the microfinance borrower household is rarely composed of employees but of own-account workers, casual labourers, unpaid family workers and circular migrants — categories conventional employment statistics were not primarily designed for. This produces a persistent measurement asymmetry in which formal employment is well measured and is not where most borrowers work, while informal self-employment is where most borrowers work and is measured with the greatest difficulty.
Labour force participation, particularly among rural women, has risen significantly, driven largely by self-employment. However, participation alone does not indicate economic empowerment; it may reflect enterprise formation, better measurement, distress participation or unpaid family work. The evidence points mainly to the first two.
For microfinance, the critical question is whether participation translates into economic agency. Five factors matter: type of work, regularity of income, control over earnings, ownership of productive assets, and household decision-making power. Progress has been strongest in participation and collective engagement, but women’s ownership of enterprise assets remains the weakest link between credit, income and lasting economic empowerment.
Table 1.5: Five questions about women’s work, and their underwriting counterparts
| Question about the work | Why it matters developmentally | Underwriting counterpart |
|---|---|---|
| Nature of the work | Distinguishes enterprise from unpaid family contribution | Whether an independent income stream exists at all |
| Level and regularity of earnings | Regularity determines whether income is usable | Fit between cash-flow pattern and repayment frequency |
| Control over income | Determines whether the loan reaches its intended user | Whether the obligor is the economic decision-maker |
| Ownership of enterprise assets | Determines whether gains accumulate into wealth | Durability of the enterprise and of the income stream |
| Position in household decision-making | Shifts with sustained income and collective membership | Resilience of repayment through household disagreement |
Two further developments matter. Circular migration remains one of the principal income transmission mechanisms into high-borrower-density districts, and it is now substantially digitised, which has compressed both the cost and the lag of the transfer and made remittance income visible as a transaction record for the first time. It is also seasonally patterned in knowable ways, so repayment schedules aligned to a source district’s liquidity profile perform better than schedules that ignore it. And platform-mediated work occupies an unusual position from a credit standpoint: its earnings are informal in security but formal in record, since the platform worker has no employment contract but possesses a complete, verifiable, digitally recorded income history. The traditional obstacle to lending against informal income was unverifiability, and platform work removes that obstacle without removing the underlying volatility.
The strategic implication is that the sector’s underwriting is presently calibrated to a labour market that is being superseded. Assessment built on the assumption of unverifiable informal income leaves on the table the growing share of borrower households whose income is now digitally observable — from platform work, digitised remittances, direct benefit transfers and enterprise receipts through instant payment rails. Building the capability to read those records, rather than any change in product terms, is the most valuable investment available to the sector over the coming three years.
Box 1.2: Judgment to Intelligence: How AI is Transforming MFI Credit Underwriting
For decades, microfinance has relied on human interaction for credit assessment. Credit officers typically meet borrowers, visit their households or businesses, assess income and repayment capacity, and combine these observations with available bureau and household information to make lending decisions. While this approach has enabled MFIs to serve customers with limited formal credit histories, it can also result in differences in judgement, inconsistent appraisal, and human bias.
For informal borrowers, particularly women, smallholders, and first-time borrowers, reliable digital data is often limited, making field appraisal an important part of credit assessment. However, such observations can be difficult to standardize, verify, and audit. In response, Pahal Financial Services explored technology to make credit underwriting more scientific, consistent, and evidence-based, while also improving operational efficiency.
An important capability acquired through Sub-K
One of the significant capabilities that came to Pahal through its acquisition of Sub-K Portfolio was VISyst (Virtual Intelligence System). The technology offered an opportunity to rethink the way customer interactions could be used in credit underwriting. Pahal has applied VISyst largely across its BC operations and now for its own portfolio as well, where the scale and distributed nature of customer acquisition and appraisal make consistency in underwriting particularly important. Rather than replacing the human interaction, VISyst seeks to make that interaction more structured and useful for decision-making.
Each appraisal is captured as a recorded video interaction and conducted through a standardized rubric. A multimodal AI layer analyses the interaction, checks consistency with the prescribed Standard Operating Procedures (SOPs), and converts relevant observations into structured credit insights. These observations can include factors such as asset ownership, housing conditions, the consistency between stated and observed repayment capacity, evidence of business activity, hesitation while discussing income, and the presence of a third party or proxy respondent. These are then considered alongside socio-economic, geo-spatial and officer-level variables to generate a composite risk score and confidence level. The objective is not to allow an algorithm to make decisions in isolation. Rather, technology helps bring consistency to the information on which the underwriter makes a decision.
What Pahal has gained
The experience with VISyst indicates benefits in both portfolio quality and operational efficiency. Early evidence shows that appraisal turnaround time has reduced by approximately 45%, while AI-assisted cohorts have recorded lower early-stage delinquency and NPAs compared with manually assessed cases. For Pahal, the technology can support more consistent underwriting, early risk identification, improved portfolio quality, and reduced administrative work for field teams.
Keeping technology responsible
AI in financial services must ultimately be built around customer trust. VISyst incorporates explicit informed consent before the interaction, with data protection principles including encrypted storage, role-based access, audit trails, purpose limitation and defined retention and deletion policies. Automated scoring operates alongside human oversight, with explainable outputs helping underwriters focus on cases requiring attention. This is particularly important in microfinance, where the customer relationship is central to the model. Technology should strengthen that relationship rather than make the credit process impersonal.
The road ahead: making underwriting more consistent, not less human
The emergence of AI in MFI underwriting reflects a shift towards making human judgement more consistent, evidence-based, and accountable rather than replacing it. Pahal aims to retain the human interaction central to microfinance while using AI to structure, verify, and analyse field information. As MFIs serve customers with limited formal credit histories, this approach can help convert field observations into reliable and actionable credit insights, supporting more responsible and disciplined lending.
Courtesy: Pahal Financial Services Pvt. Ltd.