Bridging the Credit Gap by Capturing MAIDs is Crucial for Indian Fintechs

India’s digital lending ecosystem is booming. According to a 2023 BCG report1, over 60% of all new loan disbursals in the country are now initiated through mobile apps, and this figure continues to rise with the growing appetite for micro-credit and instant personal loans. However, this rapid digital expansion brings with it a unique challenge: how do you assess creditworthiness when a user has no credit history?

Enter the mobile advertising ID (MAID), a critical identifier that is quietly becoming one of the most powerful tools in a Fintech’s data stack.

What Is a MAID, and Why Should Indian Fintechs Care?

A MAID (Android’s GAID or Apple’s IDFA) is a unique, resettable identifier associated with a mobile device. Originally created for ad tracking, the MAID is capable of unlocking behavioural insights that help paint a fuller picture of smartphone users.

By capturing MAIDs (with consumer consent), Indian Fintechs can tap into rich data from providers like Mobilewalla, enabling them to optimize their risk management processes and improve the accuracy of credit decisions in new-to-credit and thin-file environments.

Why This Matters for India Right Now

India has a large segment of first-time borrowers that falls outside the scope of credit bureaus like CIBIL. 

Meanwhile:

• 85% of mobile users in India are Android-first2, meaning MAIDs are easily accessible if captured correctly.

• Digital lenders are targeting Tier 2+ 
cities and rural India, where credit 
histories are even thinner

• Most micro-loans are now disbursed 
through mobile-only journeys

 

The gap between formal credit data availability and consumer demand for loans is growing, 
and MAID-linked third-party data from trusted 
data sources helps bridge this divide.

Third-Party Datasets Elevate Your First-Party Data

First-party data — such as PAN numbers, Aadhaar IDs, bank statements, and transactional histories — remain the foundation of digital lending because they offer verified, regulated, and often legally binding information that underpins onboarding, KYC, and basic credit assessment.

However, relying solely on first-party data presents challenges when underwriting new-to-credit or thin-file customers. This is where third-party data, especially MAID-enriched signals, becomes a powerful complement.

When combined with your first-party data, third-party mobile data  can significantly lift approval accuracy and optimize default risk, overcoming the limitations of your internal data.

Risk Modeling: The Most Critical Use Case

Fintechs in India are already leveraging MAID-based features to enhance credit risk models. With access to third-party device and behavioural datasets, companies can score users with no bureau footprint, using features that act as proxies for financial stability or default behaviour.

Data Features that Work in Indian Risk Contexts:

App Usage Behavior

Actively engaging with financial services utility billers, or payment wallets signal better payment intent.

Device Profile

High-end Android phones with ample storage, newer OS, and high screen time often correlate with stronger repayment behaviour.

Geolocation Data

A user’s location stability (e.g., consistent presence in one city vs. constant movement) can help to predict potential risk of default.

Ad Interaction Patterns

Engagement with high-risk categories like gambling or short-term loans may be red flags.

Mobility and Activity Time

Patterns such as late-night app usage or highly erratic screen time may indicate volatility.

For users with minimal or no credit history, this third-party data fills in the blanks left by traditional sources. While a PAN or bank statement shows what a person has done, MAID-derived insights show what a person is likely to do based on device usage, behavioral patterns, and digital activity.

For example, a potential borrower may have just opened their first bank account, with limited transaction history. But, their MAID-linked behavioral profile might show regular usage of utility apps, prompt payment reminders, and stable geolocation behavior—all strong signals for repayment intent. This contextual enrichment lifts the confidence level of credit models and can improve approval rates without increasing risk exposure.

More Use Cases: MAIDs Go Beyond Risk Scoring

While underwriting is a leading use case, Indian Fintechs are also seeing success with MAID-based data in adjacent areas:

Strengthening Fraud Detection

First-party documents can be falsified or manipulated. However, third-party data tied to MAIDs enables fintechs to detect subtle anomalies and uncover fraud schemes that might otherwise go unnoticed.

Consider if multiple loan applications originate from different user accounts but share the same MAID or suspiciously similar device characteristics (e.g., OS version, device make, usage patterns), it can indicate synthetic identity fraud or device farming activity. Layering this intelligence with first-party KYC information helps flag risky profiles for manual review or automated rejection.

Performance Marketing

Enriched MAID profiles empower performance marketing teams to build more precise audience segments, enabling smarter spend allocation. You can use enriched MAID profiles to:

• Build high-LTV lookalike audiences for Meta and Google.
• Reduce marketing CAC by avoiding high-risk or duplicate profiles.
• Personalize re-engagement strategies based on behavioural signals.

By combining the authoritative nature of first-party data with the behavioral richness of third-party MAID data, fintechs gain a multi-dimensional, verifiable, and predictive view of users.

The India Advantage: MAIDs + UPI + Smartphone Growth

India’s Fintech ecosystem is unique in that most financial journeys now begin on mobile devices, with:

• 80% of new-to-credit customers being acquired digitally.

• Over 659 million smartphone users4, and growing rapidly.

Capturing MAIDs early in the user journey allows Fintechs to layer device-level data onto first-party data. The result is a more complete, dynamic, and privacy-compliant profile that fuels better credit decisions.

A simple framework for using MAIDs effectively includes:

1. Capture MAIDs During App Install or Onboarding

   Prompt users for consent to collect their MAID at the start of the user journey. This is typically done via app SDKs and must align with privacy regulations like India’s DPDP Act.

2. Partner with a Trusted Alternative Data Provider

   Work with a provider like Mobilewalla to enrich MAIDs with anonymized, feature-level intelligence.

3. Enrich Internal Profiles with MAID-Based Signals

   Match MAIDs to your first-party data to build deeper customer profiles for scoring, segmentation, or targeting.

4. Integrate Into Risk Models and Fraud Engines

   Feed enriched data into your credit decision-making systems or machine learning models to improve accuracy, reduce defaults, and detect suspicious behaviors early.

5. Continuously Monitor and Refine

   As user behavior evolves, so should your MAID-driven models. Use periodic updates to refine risk signals, optimize marketing ROI, and stay compliant.

A Note on Compliance

India’s DPDP Act (Digital Personal Data Protection Act, 2023) reinforces the need for transparency and consent in data capture. The good news is:

• MAIDs do not contain data such as names, phone numbers, emails or addresses.

• With proper user disclosure and opt-in, MAID-based enrichment is compliant with user privacy frameworks and regulations.

Partnering with a trusted alternative data provider like Mobilewalla, who adheres to strict data privacy and consent practices and provides anonymized feature-level datasets, ensures you stay compliant while gaining deep intelligence.

Final Thoughts: Alternative Data is India’s Credit Enabler

The future of credit access in India lies in non-traditional data signals, which is why the demand for alternative data continues to grow. MAIDs give Fintechs the ability to expand their reach to millions of underserved users: responsibly, intelligently, and in a compliant, scalable way.

Whether you’re a digital lender, a BNPL player, or a neo-bank building financial products for Bharat, capturing MAIDs and enriching them with third-party intelligence is your best bet to win in the next phase of India’s Fintech evolution.

Want to learn more about using MAID-enriched features to supercharge your credit scoring in India? Download our quick guide to see how to capture MAIDs and the alternative data features Mobilewalla offers to power MAID-driven risk models.

Ready to integrate third-party risk signals into your credit engine? Talk to our team today.

References:

1. BCG x FICCI Report, 2023 – "Unlocking the Potential of Digital Lending in India"

2. StatCounter Global Stats – Mobile Operating System Market Share in India

3. Experian India & Invest India (2022), “Digital Financial Inclusion and the Role of Credit Bureaus”

4. Priori Data How Many People Own Smartphones in the World? (2024-2029)