India’s Shift from Collateral-Heavy to Data-Driven Lending: What It Means for Banks, NBFCs and Fintechs

For most of India’s banking history, lending started with security, not data.

If you walked into a branch, the conversation would quickly turn to:

·         “What property do you have?”

·         “Can you bring a guarantor?”

·         “Do you have gold, FDs, machinery?”

Cash flows, bureau scores and behavioural data were either invisible or secondary. The system protected itself by holding something tangible, even if it meant turning away many viable borrowers.

Over the last two decades, that logic has shifted.

India hasn’t abandoned collateral – far from it. But serious lenders are moving from a world where collateral was the primary comfort, to a world where data is the primary lens and collateral is one of many risk mitigants.

This article looks at how and why that shift happened – and what it really means for banks, NBFCs, HFCs, ARCs and fintechs today.

In our previous piece on India’s credit information timeline, we traced how shared credit data, bureaus and regulation gradually came together to make this shift possible. This article builds on that foundation and looks at how lending behaviour actually changed on the ground.

Who this article is for

·         Credit & risk leaders in banks and NBFCs

·         Fintech product and underwriting teams

·         Policy, analytics and collections professionals

·         Anyone building or modernising lending systems in India

 

1. What “Collateral-Heavy” Lending Looked Like in Practice

When we say “collateral-heavy”, we’re talking about a way of thinking, not just a checklist item.

In that world, decisions were driven by:

·         Security first – property, FDs, LIC policies, plant & machinery, inventory, gold

·         Branch relationships – long-standing accounts, local reputation, informal references

·         Static financials – audited balance sheets, IT returns, sometimes outdated by the time of appraisal

A typical decision flow looked like:

“If the collateral is strong and the promoter is known, we’re comfortable.

If not, we’re nervous – even if the business looks healthy.”

To be fair, this model wasn’t irrational , it was the safest option available at the time. In an environment with limited, fragmented data and manual processes, collateral was:

·         A buffer against default

·         A discipline mechanism for borrowers

·         A comfort factor for credit committees and boards

But it came with costs.

 

2. Why the Collateral-Heavy Model Hit Its Limits

Over time, this approach began to show structural weaknesses.

2.1. It Misread Risk

Collateral quality doesn’t always equal credit quality.

·         Strong collateral + weak cashflows can still end badly.

·         Modest collateral + strong, stable cashflows can be very safe.

A collateral-first mindset often underpriced weak behavioural risk and overpriced good borrowers.

2.2. It Was Slow and Inflexible

Collateral-heavy processes meant:

·         Physical valuation, legal checks, multiple site visits

·         Heavy documentation and long approval cycles

That works (barely) for large corporate or mortgage loans. It does not scale for today’s volumes of retail, MSME, consumer and digital credit.

2.3. It Excluded Entire Segments

If lending comfort is tied to:

·         Land ownership

·         High-quality urban property

·         Formal, documented assets

…then large segments of informal, first-generation and asset-light borrowers get left out, even when cashflows are strong.

2.4. It Hid Systemic Risk

With fragmented information:

·         A borrower could hold multiple facilities with multiple lenders.

·         Each lender saw collateral and cashflows in isolation.

·         No one saw the full leverage picture.

As credit expanded, this became a system-level risk, not just a branch-level issue.

These pressures set the stage for a data-driven rethink.

 

3. The Building Blocks of Data-Driven Lending in India

India didn’t jump from collateral to algorithms overnight. Several layers had to emerge first.

3.1. Shared Credit Behaviour: Bureaus and CICRA

Credit bureaus and the CICRA framework (which we covered earlier in this series) gave lenders:

·         A shared view of repayment behaviour across institutions

·         A regulatory backbone for reporting, access and dispute rights

·         The comfort to rely on something beyond “what we see in our own books”

This was the foundation for using behavioural data in a systematic way.

As we discussed in the earlier article on CICRA and credit bureaus, the Act didn’t just enable data sharing , it created a regulatory spine around reporting discipline, borrower rights and information symmetry.

3.2. Better Internal Data: Core Banking and LOS/LMS

As banks and NBFCs upgraded technology:

·         Core banking systems became more standardised

·         Loan origination and loan management systems started capturing richer data

·         Transaction histories, EMI patterns and utilisation behaviour became easier to analyse

Data that earlier lived in files and branch ledgers started to live in systems.

3.3. Digital Public Infrastructure

India’s digital rails added another layer:

·         Aadhaar and PAN streamlined identity and KYC

·         UPI and digital payments exposed transaction behaviour

·         The Account Aggregator framework enabled consent-based access to bank account data

Lenders could now start to combine credit history + cashflows + digital behaviour.

 

4. What “Data-Driven Lending” Actually Means Inside a Lender

“Data-driven” is a comfortable phrase. Inside a lending institution, it translates into four real layers.

Anyone who has sat through a credit committee knows this tension , the numbers say yes, the collateral says maybe, and no one wants to be the person who approved the loan that later goes bad.

4.1. Multiple, Structured Data Sources

A data-driven lender systematically uses:

·         Bureau data (across CICs) – scores, tradelines, enquiries, delinquencies

·         Internal performance data – repayment, roll rates, vintage performance

·         Banking & cashflow data – via statements or AA

·         Application and device signals – where relevant and permitted

The key is structure: data fields are consistent enough to feed models and rules, not just eyeballing.

4.2. Models and Scorecards

Instead of purely judgmental calls, the lender uses:

·         Generic bureau scores as a base signal

·         Custom internal scorecards that reflect its own experience and risk appetite

·         Segment-specific models (e.g., salaried vs self-employed, new-to-credit vs deep-file)

Collateral may still be considered, but it’s one input into a broader risk view, not the main story.

4.3. Policy and Automation

Credit policy becomes a codified, testable set of rules, such as:

·         Minimum score thresholds

·         Maximum FOIR / leverage based on bureau + AA

·         Restrictions on recent delinquencies or high enquiry intensity

·         Collateral requirements that adjust with observed risk

Parts of this are automated in the LOS; exceptions are consciously reviewed, not accidentally tolerated.

4.4. Monitoring and Feedback

Data-driven doesn’t end at disbursal. It extends to:

·         Vintage and cohort analysis – tracking how different segments behave over time

·         Early warning systems (EWS) – payment behaviour shifts, bureau downgrades, score migrations

·         Collection prioritisation – deciding who needs high-touch vs low-touch strategies

This feedback loop lets lenders refine policy and pricing continuously, not once in a few years.

 

5. Collateral vs Data: How the Balance Is Really Changing

Let’s be clear: data-driven lending does not mean collateral is dead.

It’s “collateral plus behaviour plus cashflows” – with relative weights shifting by segment.

5.1. Secured Retail (e.g., Home Loans)

·         Collateral (property) remains key.

·         But pricing, LTV and approval now depend heavily on:

o   Bureau performance

o   Income stability and variability

o   Existing leverage across lenders

Two borrowers with the same flat as security may see very different decisions.

5.2. Unsecured Retail and Cards

Here, collateral is almost absent. These portfolios live or die on:

·         Credit bureau quality

·         Internal performance data and scorecards

·         Acquisition filters and line management strategies

This is where data-driven is not optional – it’s existential.

5.3. MSME and Business Lending

Traditionally very collateral-heavy, this segment is slowly shifting to:

·         Cashflow-based assessments (GST, bank statements, AA data)

·         Behaviour-based segmentation (bureau, internal trades, supplier payments)

·         Risk-based collateral requirements (stronger data → lighter security, and vice-versa)

The logic is moving from “no collateral, no loan” to “no visibility, no loan”.

 

6. What This Shift Demands from Risk and Credit Teams

Moving from collateral-heavy to data-driven lending isn’t just a tech upgrade. It changes how teams work.

6.1. From Individual Judgement to Designed Frameworks

·         Relationship managers and credit officers still matter.

·         But their decisions are now anchored in data and policy, not just instinct.

·         Risk and analytics teams become designers of frameworks, not just approvers.

6.2. Stronger Collaboration Across Functions

Data-driven requires coordination between:

·         Risk & Policy – defining rules and appetite

·         Analytics & Data – building, validating and monitoring models

·         Technology – integrating data sources and systems

·         Operations & Collections – using insights in day-to-day actions

When these teams work in silos, you get data-rich, insight-poor institutions.

6.3. New Skills and Governance

Institutions need:

·         People who can read scorecards and ROC curves, not just financial statements

·         Clear governance over which data is used, how, and with what checks

·         Regular reviews of policy performance, not just regulatory compliance

 

7. Myths About Data-Driven Lending in India

There are a few common misconceptions worth clearing.

Myth 1: “Data-driven means no collateral.”

Reality: Collateral still matters, especially for long-tenor and high-ticket loans.

Data-driven simply means collateral is part of a structured risk view, not a substitute for it.

Myth 2: “Data-driven lending is only for big banks and fancy fintechs.”

Reality: Even mid-sized NBFCs and specialised lenders can use bureau, AA and internal performance data to materially improve:

·         Loss rates

·         RoA / RoE

·         Collections efficiency

The barrier is usually focus and execution, not license or scale.

Myth 3: “If we go data-driven, we will start rejecting more.”

Reality: Done well, data-driven lending helps you:

·         Say ‘no’ faster to truly risky profiles

·         Say ‘yes, but priced correctly’ to marginal ones

·         Say ‘yes’ more confidently to strong segments you were under-serving

It’s about precision, not just rejection.

Myth 4: “Once we’ve built a model, we’re done.”

Reality: Portfolios evolve. Customer behaviour changes. Regulations shift.

Data-driven lending is a continuous discipline, not a one-time project.

 

8. The Arth Data Solutions View: Moving Beyond “Pulling Bureau”

At Arth Data Solutions, we see many lenders sitting in an uncomfortable middle:

·         They pull bureau, sometimes even AA.

·         They have basic scorecards in place.

·         But day-to-day decisions still feel collateral-first and instinct-led.

Typical symptoms:

·         Policy documents that haven’t been updated to reflect current behaviour.

·         Limited or inconsistent vintage, EWS and cohort analysis.

·         Collections strategies that don’t fully leverage live data.

·         Bureau reporting and dispute handling treated as a back-office chore, not a strategic input.

Our view is simple:

The real journey is not from collateral to data.

It’s from comfort in assets to confidence in information.

That confidence comes when:

·         Your bureau and AA data is cleanly integrated into systems

·         Your models and scorecards are transparent and continuously monitored

·         Your portfolio dashboards actually change decisions on the ground

That’s the layer Arth Data Solutions wants to strengthen:

turning available credit data into practical risk intelligence for day-to-day use by credit, risk and collections teams.

This is also why simply “pulling bureau” is not enough , a theme we’ll return to in the next article when we map how credit data actually flows (and breaks) across lending systems.

 

9. Where the Series Goes Next

In this article, we looked at India’s move from collateral-heavy to data-driven lending, and what that means inside a lender.

In the next part of this series, we’ll go more operational:

·         How credit data actually flows across systems – from application to bureau, to monitoring and collections

·         Where information gets lost, delayed or distorted

·         And what risk and data teams can do to tighten those weak joints

If you’re already pulling bureau and banking data but still feel your lending is “old-world with new data”, that’s exactly the gap this series is designed to explore – quietly, practically, and from an Indian credit lens.

 

If you’d like, I can now create the supporting material pack for this blog too (LinkedIn post, carousel outline, 60-sec YouTube script, emailer version) in the same format as the earlier articles.