Lenders have long relied on pre-disbursal risk assessments—credit scores, income proofs, and static FOIR—to underwrite loans. But the risk doesn’t stand still. Borrower behavior changes over time, sometimes quietly, and often long before a default. To truly understand how risk evolves after money leaves your books, Deepvue helped a leading lender monitor 1,000 borrowers over a 6–12 month period post-disbursal. This blog breaks down what we learned, what we tracked, and why continuous monitoring is essential to modern lending.
Kunal – The “Low-Income, High-Spender” Borrower
Kunal was a low-risk borrower. He was earning ₹43,000 per month and was paying an EMI of a mere ₹9,500. But our post-disbursal surveillance showed persistent financial stress. In 8 out of 12 months, his spending outweighed his income, with regular UPI spends on food ordering, e-commerce, and wallet recharges. His balance fell from ₹3.2 lakh to a mere ₹80,000 in one year.
During month nine, his balance fell below the EMI value five days before the due date. That one transaction initiated a low-buffer alert. We alerted the lender, they presented a structured solution to the borrower, and avoided a probable default. He is a classic case of how FOIR appears robust in static form, but dynamic performance speaks for itself.
The Framework for Monitoring
Data Sources Used: We utilized a mix of consent-based information from:
- Bank accounts (through Account Aggregator and direct integrations)
- EMI repayment history
- Salary credit habits
- Transaction categories
- NACH debit activity
Consent-Driven Access: All surveillance was done under India’s AA framework or borrower consent, full compliance with RBI data-sharing norms.
Key Metrics Tracked:
- Salary Credits: Frequency, delays, and amount consistency
- EMI Behavior: Timelines, bounce trends, FOIR
- Balance Trends: Whether borrowers maintained a buffer or consistently ran tight
- Spending Categories: Ratio of essentials to discretionary spends
- Credit Inflows: Periods of blackout and signs of alternative incomes
For instance, in Kunal’s case—a borrower who takes home ₹43,000 with an EMI of ₹9,500—we found through monitoring that while his income appeared sufficient on paper, he always spent more than he earned. Real-time data indicated high discretionary expenditure and a steady depletion of his savings, allowing us to act before default.
Why We Had to Go Beyond Onboarding Risk Scores?
Traditional Model Shortcomings: Underwriting decisions based on one-time data offer no visibility into post-disbursal dynamics. Fixed scores fail to capture risk creep, cash flow shocks, or behavioral deterioration.
The Fundamental Issue: Risk is evolving. A borrower with a flawless credit history can go into default in months if salary is delayed, expenses rise, or account activity plummets. Pre-loan assessment can’t detect this.
Objective of the Study: We monitored the real-time financial behavior of 1,000 borrowers to:
- Detect early signs of stress
- Classify post-loan risk behavior
- Identify predictive signals that traditional scoring misses
Segmentation of Risk: What Borrower Patterns Emerged
Risk Buckets After 6–12 Months:
- Low-Risk (35%) – Steady inflows, balance buffers, consistent EMI history
- Emerging-Risk (45%) – Delays, erratic cash flow, impulsive spending
- High-Risk (20%) – Defaults, income disruptions, sharp financial deterioration
A simple risk pie chart revealed that nearly 60% of borrowers showed signals that could not have been detected at onboarding.
Kunal’s path from strong buffers into the “Emerging Risk” bucket was clear—he started with good buffers, but repeated overspending and cash mismatch made him susceptible to falling into high-risk. Timely warnings allowed us to guide him back before that occurred.
The Behavioral Patterns That Predict Risk
Signal 1: Frequency of Salary Credit Falls
Appeared in 24% of future defaulters. Overdue or late salaries were the top precursor.
Signal 2: Account Balance < EMI for 2+ Cycles
When balances fell below the amount of EMI for two successive months, the chances of default increased manifold.
Signal 3: Spike in Discretionary Spending after Missed EMI
EMI bounce, followed by food ordering, shopping, or wallet spending—a counterintuitive but typical risky behavior.
Signal 4: Wallet Top-Ups During Low Balances
Borrowers started topping up wallets or UPI apps when their balance dipped below ₹2,000. A growing indication of a liquidity squeeze.
Signal 5: 30+ Days Without Credits
One of the best indicators. When there hadn’t been any credit inflow for more than a month, default was extremely likely.
For Kunal, we observed a number of these signals: his balance consistently fell short of his EMI value, his discretionary spends were elevated even once payment stress had arisen, and he often topped up wallets to make ends meet with meager funds.
Early Warning Signs: How Soon Can We Know?
In 72% of defaults, risk signs appeared 30 to 45 days before the event. This provided lenders with an effective window for action. Signals triggered were:
- Abrupt decline in the balance buffer
- Expected salary does not match
- Spending pattern away from historical pace
Borrower self-cure rates were enhanced by more than 20% when these triggers were addressed within 7 days.
Kunal received a restructuring offer within days of his low-balance alert. This helped him stabilise and avoid the default path many others followed.
How do Different Borrower Profiles Behave?
Salaried vs. Self-Employed:
- Salaried borrowers showed stress around pay cycles and NACH hits.
- Self-employed consumers needed tailored thresholds since they have a variable income.
Urban vs. Semi-Urban:
- Urban borrowers used their wallets more heavily and stacked EMIs more.
- Semi-urban borrowers evidenced dependence on a single source of income and, therefore, any disruption was more significant.
Our Monitoring Model: What We Tracked, and How
- Frequency: Five times in a month, tracking over 6–12 months
- Access: Consent-based through the AA framework or direct integrations
Signals Tracked:
- Salary credit rhythm
- Account balance relative to EMI
- Spending categories (essentials vs. discretionary)
- Wallet usage vs. bank reliance
- EMI debit outcomes (full, partial, failed)
- Other liabilities: BNPL, overdrafts, cards
How Kunal’s Transaction Insights Were Created?
- Data Extraction: We began by analyzing Kunal’s 6-month bank statement, pulling out credit and debit entries, transaction dates, categories, and balance movement.
- Detecting Transaction Modes: We begin by classifying each transaction into its payment mode:
- UPI (P2P and P2M)
- NEFT/IMPS
- Cash withdrawal
- Card/POS
- Wallet top-up
- Income Identification: Salary credits were identified through consistent monthly NEFT entries from a known employer (ElectroMart Pvt. Ltd.), averaging ₹43,000–₹46,000. Only these were treated as core income.
- Categorical Classification of Debits: Each debit transaction was mapped to a category—e.g., food delivery, e-commerce, P2P transfers, wallet top-ups—using merchant names and UPI tags (e.g., Zomato, Amazon, PhonePe).
- Monthly Cash Flow Summaries: For each month, we calculated,
- Total income (sum of credit entries)
- Total expenditure (sum of debit entries)
- Net cash flow (income – expenses)
- Expense-to-income ratio
- Balance Tracking: Daily and monthly end balances were used to assess the financial cushion. Specific attention was paid to:
- Whether the equilibrium remained above the EMI value
- Patterns of erosion over time
- Behavioral Flags & Risk Signals: Points of insight were indicated where:
- Spending outpaced earnings for two or more successive months
- Balance fell below EMI five days in advance of the due date
- A high incidence of discretionary spending occurred despite adverse cash flow
- Aggregate Trends: Across the year,
- 8 of 12 months showed negative cash flow
- 75% drop in balance from ₹3.2L to ₹80K
- Over 35% of the spending was discretionary
This helped differentiate between peer transfers, bill payments, shopping spends, and fund movements
Action Beats Observation: Our Intervention
Tactical Interventions:
- NACH reattempts synchronized to salary credits: +38% recovery boost
- Pre-emptive restructuring based on behavior signals
- Automated notifications to the collection team 48 hours after the trigger
Results:
- 9.6% projected reduction in NPAs from the pilot group
- 21% higher self-cure when borrowers were nudged within 7 days of the first signal
Implications for Lenders: What We’d Do Differently Now
- Make borrower monitoring a native capability, not an add-on
- Refresh FOIR and affordability quarterly
- Integrate behavioral insights into credit policy and early collections
- Treat consent renewal as part of every reloan, top-up, or cross-sell strategy
Conclusion
One-time credit scores are no longer enough. Risk isn’t static. Monitoring 1,000 borrowers post-disbursal has shown us that behavioral signals emerge early, often, and with surprising accuracy. Lenders who watch closely can act early, recover better, and lend more confidently. The future of credit isn’t just about approving the right borrower—it’s about staying in sync with them long after disbursal.
FAQ
Why is post-disbursal borrower monitoring important for lenders?
Because borrower behavior can change over time, continuous monitoring helps detect early signs of stress or potential defaults.
What kind of data is used to monitor borrowers after loan disbursal?
Data includes bank statements, salary credits, EMI repayment history, spending patterns, and NACH activity—collected with consent.
What are the most important metrics monitored under post-disbursal monitoring?
Salary credit frequency, EMI behavior, account balance trends, discretionary spending patterns, and credit inflow consistency.
How does Deepvue pick up early warning signals in borrowers’ behavior?
Deepvue monitors salary trends, EMI timing, balance direction, and transaction buckets to uncover behavioral indicators such as salary delays, low buffers, or risk-inclined spending behaviors.
What type of alerts does Deepvue provide for lending teams?
Deepvue raises alarms like low balance before EMI, salary credit shortfall, credit blackout dates, and alteration of spending habits, which can be a sign of financial strain.
