Episode 5: The AA Dilemma – Can Microfinance Truly Ride the Digital Wave?

BEYOND RATIO SERIES

Introduction - Account Aggregator - Microfinance

Microfinance Institutions (MFIs) have long been the torchbearers of financial inclusion, extending credit to India’s most underserved populations. But in a world that's fast digitizing, MFIs face a fresh challenge: how to leverage the Account Aggregator (AA) framework for deeper, data-driven lending — when their core clientele lives largely outside the formal data economy.

Take Meena, who runs a tailoring unit in Tiruppur. Her monthly bank credits are regular but modest, interspersed with cash deposits from walk-in customers. She’s repaid five loans with her local MFI without a single delay. Yet, when her records are fetched through an AA platform, only 4 out of 12 months show meaningful transaction data. The system marks her as 'thin-filed'.

Account Aggregator for Microfinance

Is it a tech problem? A trust problem? Or a policy blind spot?

1. The Promise vs. Practice of AA

The Account Aggregator framework aims to offer secure, consent-based data sharing for improved lending decisions.

But here’s the reality for MFIs:

• Hit ratio in data fetch: ~40%, often lower in rural/semi-urban segments

• Limited smartphone penetration and digital literacy

• Most borrowers have single savings accounts used minimally

• MFIs not always FIUs or lack backend systems to interpret AA data

2. Juxtaposing AA Heatmap with MFI Segments When we compare who’s using AA effectively vs. who needs it most:

Segment	AA Adoption Level	MFI Overlap
Digital NBFCs	High	Minimal
Urban salaried/gig	Medium to High	Low
MSMEs (formalised)	Patchy	Moderate
Rural informal sector	Very Low	High

The mismatch is clear: the segment that MFIs serve the most — informal rural borrowers — is the least represented in AA-compatible data sets.

3. How MFIs Currently Validate Account Statements Popular methods include:

• Physical passbook verification during center meetings

• Field officers collecting and scanning physical statements

• Cash flow estimation based on income-debt mapping

• Alternative sources like SHG records or repayment history

  
  

These are trust-heavy and people-intensive models — and that’s where digital data, if accurate and complete, could help.

4. Legal Dimensions: Where Consent Meets Compliance

a. AA under DEPA: Built on the Data Empowerment and Protection Architecture, it enables consent-based data sharing. But MFI clients often:

• Aren’t aware of their data rights

• See consent as formality, not choice

• Lack grievance awareness

  
  

b. FIU Gaps: Many MFIs aren’t full FIUs, or lack systems to securely process AA data. This limits AA’s legal utility.

c. Liability Risks: If data is incomplete or misunderstood — who’s accountable: the AA, FIU, or MFI?

d. Data Protection: With the Digital Personal Data Protection Act, 2023 gaining traction, improper consent or storage could attract legal scrutiny.

Consent isn’t just a digital step — it’s a legal commitment.

5. Red Flags in Bank Statements – What to Watch For When data is available (via AA or otherwise), MFIs and NBFCs look for warning signs:

🔺 Cheque bounces, even non-EMI 🔺 EMI bounce charges = poor discipline

🔺 Credit surges without GST 🔺 GST NIL returns inconsistent with banking 🔺 High cash withdrawals post-credit 🔺 Related-party credits, circular transactions 🔺 Thin average balances despite high turnover 🔺 Over-leverage via NBFCs / Fintechs (check bureaus)

6. Where Do We Go from Here?

Stakeholder	What Needs to Change
MFIs	Awareness campaigns, FIU enablement, tech integration
Policymakers	Incentives for AA usage in JLG/SHG/MFI lending
Tech Providers	Build AA tools usable by field officers, not just apps
AAs	Better vernacular UIs, assisted journeys for consent

🔹 Segmenting AA Adoption Through a Generational Lens

While much of the conversation around AA adoption has focused on system integration, data hit rates, and bank participation, one silent but powerful layer is the generational gap among borrowers, bankers, and intermediaries.

👶 Gen Z (1997–2012):

“Comfortable with digital, uncomfortable with institutional pushback.”

• High potential AA users — digitally native, quick to consent if value is clear.

• Barriers: Distrust in institutions, impatience with red tape, often lack stable income trails.

• AA implication: Must simplify consent UX and build relatable trust cues.

  
  

🧑 Millennials (1981–1996):

“Tech-friendly, but wary of over-sharing.”

• Likely adopters if benefits are well-articulated.

• Want clarity on how data is used, stored, and benefits accrue.

• AA implication: Messaging must focus on control, security, and personalized credit improvement.

  
  

🧓 Gen X (1965–1980):

“Skeptical and privacy-conscious.”

• May resist digital consents unless absolutely required.

• Prefer face-to-face conversations and paper trails.

• AA implication: Needs deeper financial literacy, and strong banker advocacy.

  
  

👴 Boomers (1946–1964):

“Institutional trust, but low digital confidence.”

• Least likely to initiate AA consents.

• May confuse consent with data breach risks.

• AA implication: Needs human enablers – DSA, branch staff – to walk them through the process and reassure them.

  
  

🧭 Why This Lens Matters

Understanding generational behavior helps:

• Design borrower journeys that resonate

• Train bankers and field staff to tailor AA communication

• Avoid one-size-fits-all messaging or rollout strategies

  
  

💡 In AA adoption, tech-readiness isn't just about infrastructure. It's also about people.

Conclusion

AA can be a force multiplier for MFIs — but only if its use cases are designed for the grassroots, not just the digitally savvy.

If we continue measuring the informal economy with formal yardsticks, we’ll miss the real picture.

AA is promising, but not plug-and-play for MFIs. Without ecosystem strengthening (bank participation, digital education, co-browsing consent), the AA model remains more aspirational than operational in grassroots lending.

Disclaimer:

The views expressed in this article are personal and intended for informational and educational purposes only. They do not represent the official stance of any financial institution, regulator, or affiliated organization. Examples and case references are illustrative and drawn from publicly available data or simulated contexts. Readers are advised to exercise discretion and consult qualified professionals before making any credit, investment, or policy decisions based on the content herein.