In today’s digital lending landscape, speed is everything. Approvals are fast, and disbursals are faster.
The paperwork checks out, the credit score is clear, and the stated intent, such as renovation, education, or emergency, sounds perfectly reasonable.
And yet, many lenders still find themselves asking the same question days or weeks after the money has left their system:
Where did the loan actually go?
The uncomfortable reality is that declared intent is no longer reliable enough to determine whether the loans are being used for the stated reason.
And in the absence of deeper behavioral intelligence, lenders often operate with one eye closed, approving loans based on surface-level signals - bureau scores and income details - while overlooking other risk signals that are hiding in plain sight.
Here are some case studies that shed light on the current lending loopholes.
1. The personal loan taken to “Upskill” and how it funded a Crypto Gamble
- Take Rita, a 26-year-old software engineer from Pune.
- Personal loan amount: ₹1.5L
- Stated purpose: Skill development
- Supporting documents: All present and verified
- Disbursed: Within 24 hours
But historical banking activity suggested otherwise:
- Previous transactions on domestic and offshore crypto trading platforms
- Multiple wallet loads followed by a sudden inflow of money
- A pattern of short bursts of trading, particularly around market dips
There were no payments to any ed-tech platforms. No exam fees. No tuition fees.
Just a financial trail pointing clearly toward speculative behavior.
Business Impact:
- Rita defaulted in under 90 days
- No collateral, no alternative income to absorb the loss
Had prior transactional behaviors been assessed, this risk would’ve been evident before the loan was disbursed.
A ₹1.5L write-off could have been avoided with deeper visibility. Now multiply that across a cohort. The impact becomes portfolio-wide.
2. When an 'Early Salary' Loan Fuels a Match-Day Wager
Then there’s Vinni, a product manager in Delhi.
In April 2024, he took three small-ticket loans - each declared as an advance salary requirement, each under ₹20K,
Surface-level indicators showed nothing alarming. But the underlying activity told a different story:
- Recurring wallet top-ups to fantasy gaming platforms
- Such similar borrowing patterns can be seen across hundreds of accounts in the same time frame
Business Impact:
- In this case, the borrowers defaulted not due to intent, but due to lack of ability. Something that could have been caught before loan disbursal.
- Vinni had no sustained income or repayment buffer
- Tiny tickets added up to a large-scale risk that is entirely avoidable
A ₹20K ‘early salary’ loan may seem harmless. But in clusters of 10,000 borrowers, it’s a ₹20 crore exposure hiding in plain sight.
3. The Home Renovation That Became a Disappearing Act
- Then came Sujith, a boutique owner from Cochin.
- Loan sanctioned: ₹4L
- Purpose: Wedding expenses
But Sujith had already taken out two other loans within the same month. None had reflected in the bureau data yet.
Prior account activity showed:
- Wire transfers to offshore accounts immediately after previous disbursals
- High-velocity movement of funds before going dark
What appeared as an isolated loan was, in fact, part of a larger pattern of loan stacking across lenders before anyone could detect it.
Business Impact:
- Recovery was impossible once the funds left the country
- Traditional checks failed to flag anything
- The absence of consolidated behavioral insights enabled this loan sanction.
This was an intent of fraud accompanied by a failure of insight. And the cost was ₹ 4 lakh in one case.
Beyond the Application: Lending in a Pattern-Rich World
Lenders today operate in a world with rising NPAs, and heightened borrower expectations.What separates leading institutions from the rest isn't just the speed of decision-making, but the quality of intelligence behind those decisions. But the real question is, how to identify these red flags before it’s too late? How to ensure that the loan is being disbursed for a genuine purpose and not otherwise?
The ideal lending model goes beyond credit scores and stated intent to evaluate. Here are a few measures that must be followed:
- Past financial behaviors
- Recurring fund usage patterns
- High-risk transaction trails
- Overlaps between fresh loans and prior obligations
Not to reactively chase defaults, but to preempt them with precision
This isn’t about hindsight. It’s about building a forward-looking view, powered by past behavior.
Turning Patterns into Protection
At IDfy, we believe responsible lending doesn’t start at the point of approval; it starts by understanding a borrower’s financial patterns before that moment.
Our Transaction Intelligence Platform enables lenders to identify high-risk behaviors such as crypto speculation, BNPL misuse, international transfers, and loan stacking not as anecdotes, but as early indicators.
Because in today’s lending environment, the smartest move you can make is to see the full picture before the loan goes out the door.
Ready to turn borrower patterns into business performance?
If you're looking to understand your borrowers beyond credit scores and build a lending engine that’s smarter, safer, and more sustainable, reach out to us at anika@idfy.com.