Trends in Borrower Behavior Since COVID-19

Here is some longer term perspective on delinquencies and charge offs… Consumer-credit delinquencies and charge-offs are growing at a pace rarely seen outside of recessions, though given the current state of consumer balance sheets and debt-service ratios, the rise may be at least in part a delayed effect from the end of temporary Covid-19 assistance programs. Consumer-loan delinquency rates rose from record lows of 1.5% in 4Q21 to 2.74%, their highest level since 2012 but below their 1987-2008 3.4% average. Charge-offs, however, are up to 2.92%, above their 2.2% 1987-2008 average. Unlike the 1990, 2001 and 2008 recessions, charge-offs and delinquencies didn't rise in 2020. This suggests loans that would otherwise have defaulted from 2020-22, were it not for the pandemic-assistance programs, might be doing so as programs expire.

Consumer Debt Soars: Auto, CC, Installment and Student Loan Delinquencies Rates Soars I always root for the consumer, but with rising/high level of indebtedness, inflation rising, and the economy slowing, it is wise to take a more conservative underwriting approach to consumer loans. For the first time in history, consumer debt has risen above $18T according to the Federal Reserve (2nd chart below). - Auto Laon DQ rates are at its highest level since the GFC (2008/09) as shown in Exhibit 1. - Five million-plus student loan borrowers under the Federal Gov’t borrowing program must make payments beginning May 5th, which will be a struggle for many since they have not made a payment since the COVID payment holiday was granted 4 years ago. We believe the debt reinstalment will knock down FICO scores by 100+ points on this event. - Credit Card DQ rates for non-bank issuers (30-day+ past due) look very similar to auto loan DQ rates, rising across all income levels. Even with those current on their CC payments, they are only making the minimum payment required as their balance outstanding increases (11% of borrowers fit this criterion, so even the shadow number is deteriorating). - Consumer installment loans tell us the same story; however, lenders are disciplined, and the rejection rate has soared to 90%! Bottom Line: 1. Asset-Based Lending (ABL) fund managers are wise to position accordingly with the knowledge that this cycle will likely show above trend loss rates for consumer loans across multiple asset type. The exception is for home mortgage loans as $35T of home equity has been built up; home mortgage DQ rates are near all-time lows. 2. ABL fund managers who have the sourcing channels with strong origination/underwriting/asset management teams to execute a program whereby they are secured by a perfected interest in hard assets (physical assets such as plant, property, equipment, aviation assets, infra-assets) are positioned well for this cycle. ABL is everyone’s favored private credit asset class in 2025, mine included. ABL is an asset class for all-seasons (invest throughout the cycle). Despite my cautious stance to consumer cyclical businesses and consumer credit at the current juncture, hard asset lending has come of age and should perform strongly in this cycle.

Since Covid, there are two revolutions underway that are being driven by India’s youth. The first is a rapid rise in stock market participation, both directly and through mutual funds, and the second is a surge in credit-driven consumption. These intertwined trends are redefining both the investing and spending habits of a generation. Prior to the pandemic, investors under 30 comprised just 23% of the NSE’s registered investor base; but by end 2024, that share soared to an estimated 40%. This increased share needs to be seen in the context that the registered base of investors on NSE has grown more than 3x since Covid. According to an estimate, the under 30 investor accounts for more than half of new mutual fund investors since 2020, with many from smaller towns. The proportion of retail F&O traders under 30 is estimated at almost 45%. All the above data is not based on value, it must be said, but is still very significant. A huge trend in India, not seen before, is that in spite of consistent and considerable selling by FIIs, markets have held up because of strong domestic flows, in part driven by this trend. While earlier generations thought “save now, consume later”, this generation is more about “consume now, invest for later”. The under 30 segment dominates the personal loan business and the Buy-Now-Pay-Later (BNPL) sector. Whether it’s essentials or one time indulgences, everything is available on EMI; it is estimated that over half of BNPL volume emanates from Gen Z and millennials. The personal loan market too is driven by the same segment who are said to account for a significant part of the demand. Whether the personal loan is funding consumption or investments is an important question. What is driving these twin revolutions? One, possibly greater optimism about the future which then fuels risk appetite, leading to taking leveraged bets in equities or funding lifestyle choices with credit. Second, the growth of Digital Platforms which have made access easy and seamless for a new generation which is digitally native. Third, Social Media influence, with “finfluencers” advocating equity investing while lifestyle influencers promote aspirational consumption. Fourth, a solid performance in Indian equities since the pandemic which has possibly led this group to believe that this kind of return is expected. The worst performing month (Oct 24) since Covid saw a 6% fall in the Nifty. Compare that to the larger corrections seen say in 2001, 2008 or 2011. What are the risks? Household leverage is rising and coupled with higher equity exposure, Indian households are becoming more sensitive to market and interest rate cycles and more vulnerable to downturns. Savings – the lifeblood of our economy for long - is falling. Regulations and financial literacy need to help strike a balance between deepening and widening markets, while curbing reckless speculation and over-leverage.

While everyone’s focused on inventory and interest rates… I’m seeing deeper shifts in buyer behavior... ones that could define Bay Area real estate for years to come. 1. International buyers are quietly returning 🌏 During COVID, foreign investment dried up almost entirely. But now? I’m seeing strategic buyers from Asia and Europe returning and locking in long-term U.S. assets while headlines still talk “cooling.” These are not speculators but planners. Buying for kids, diversification, or future migration. 2. Empty nesters are upsizing, not downsizing 🏡 Traditional wisdom said: sell the big house, move into a condo. Today’s reality: they want more space for home offices, adult kids returning home, or even hobbies and wellness rooms. Hybrid work and multigenerational living are redefining retirement housing. 3. First-time buyers are outbidding investors on starter homes 👨👩👦 In the past, cash-heavy investors snapped up sub-$1M homes. Now, I’m seeing tech couples with strong financing and heartfelt letters win out. Investors are backing off or shifting to higher-end flips or long-term multi-units. The starter home market is becoming more personal again. 💡 So what does this all mean? → The Bay Area is still a global safe haven quietly drawing international capital → “Downsizing” is no longer the rule for affluent retirees → First-time buyers are gaining ground as investor activity shifts The media may say we’re in a slowdown but on the ground, the story is far more dynamic. 👀 What unexpected trends are you seeing in your market? #bayarea #realestate #housingmarket #property #realtor

Recent RBI data shows a very clear trend in Indian borrowing behavior. At first glance, this looks like a simple distribution of loans. But if you read it like a balance sheet, it reveals something deeper. 1.) India is still a “home-first” economy. Almost half of all loans go into housing. This reflects not just demand, but also cultural preference for owning assets over renting. 2.) Consumption is rising, but cautiously. Vehicle loans and credit card dues together show growing consumption but they’re still much smaller than housing. India is spending more, but not recklessly. 3.) Gold loans often rise when people need quick cash. This segment quietly reflects short-term financial stress in households. 4.) Education loans are surprisingly low. At just 2%, this raises questions about access, affordability, and reliance on self-funding for education. 5.) “Others” is a big black box (28%)- This likely includes personal loans and small business borrowing, a segment worth watching because it often grows fastest during economic expansion. India’s credit behavior reflects a balanced economic structure: • Asset-led borrowing dominates * Consumption is rising, but not excessive * Households still show financial prudence This is a positive signal for long-term financial stability, especially when compared to economies driven heavily by unsecured consumption credit.

Retail Lending - moving storyline "I pay my rent on time. My electricity bills. Even my mother's LIC premium. But when I applied for a loan, they said—Sir, CIBIL low hai. Can't offer you a good rate." He wasn't angry. Just disappointed. Like the system hadn't noticed how hard he was trying. I came across Rohit, a graphic designer from Pune. Freelancing since the pandemic. No salary slips. No Form-16. Just a dream to move into his own 1BHK. Traditional banks didn't care about his rent paid via UPI every month. Or his scooter EMI history. Or how he hadn't missed a broadband bill during COVID. They only saw an incomplete CIBIL score. That's when it struck me: We've spent decades rewarding the formally visible — and ignoring the financially responsible. But something revolutionary is happening in Indian lending. Enter Risk-Based Dynamic Pricing. Instead of "low CIBIL, high rate" — lenders are asking: Does he pay phone bills consistently for 24 months?  Has his location remained stable?  Are UPI transactions showing regular income?  Does he invest in SIPs monthly, even Rs 500? AI models now read these behavioral signals and say: "This person is responsible. Let's price based on actual risk, not just a 3-digit score." The result? Rohit got his loan from a new-age lender that read his digital footprint. Interest rate: 2% higher than prime customers, but 6% lower than traditional unsecured loans for his CIBIL range. Two months later: A photo of his flat. Caption: "Finally, my own space." This is happening at scale. Lenders like KreditBee, MoneyTap use machine learning to analyze: Spending patterns on e-commerce Device consistency (same phone 18+ months = stability) Bill payment history across utilities Location stability through telecom data Digital transaction behavior Early results: 30% better loan performance than CIBIL-only models 60% of "thin file" customers paying on time 40% reduction in processing time But are we moving fast enough? Millions build digital credit trails daily through UPI, bill payments, subscriptions. Every Zomato order paid on time, every Netflix subscription maintained, every electricity bill cleared is a signal. Yet most still face "no CIBIL, no loan" or punitive rates. To fellow lenders and fintech innovators: > Can we build systems that notice silent discipline of people not in formal credit systems? > Can we reward responsibility, not just paperwork? > Can we use data to recognize potential? Behind every "thin file" is often a thick story of grit and consistency. Maybe that's exactly who we should lend to. What behavioral signals do you think predict repayment best? Have you seen dynamic pricing in action? The future of credit is behavioral, not historical. That future is now. #RiskBasedPricing #DigitalLending #CreditInnovation

What 10 Years of Lending Data Taught Me About Indian Borrowers? Indians are creditworthy. The system just can't see them. Here's what the data actually taught us. → The invisible borrower problem India's retail credit penetration: 11% of GDP. The US: 75%. China: 55%. That gap isn't defaults. It's that 730 million credit-eligible Indians have no formal credit footprint. No loan history. No credit card. No trail. Not risky. Invisible. Those are two completely different problems. → What we got wrong early We assumed income was the variable that mattered most. It isn't. A borrower earning ₹40,000/month salary on the 1st, EMI on the 3rd repays at dramatically higher rates than someone earning ₹80,000 with irregular income timing. → Cash flow rhythm beats income level. Every time. This is what PDF bank statements couldn't tell us. It's what Account Aggregator data live, verified, tamper-proof finally could. Not income. Rhythm. → The loop that broke the industry P2P NPAs: ₹14.7 crore in FY19. By FY24: ₹1,163 crore. Up 7,800% in five years. Here's the loop that caused it: Platforms chased growth, offered implicit return guarantees, attracted borrowers who weren't genuinely creditworthy - COVID hit - income collapsed - defaults cascaded, trust collapsed. The platforms that survived never confused marketing with underwriting. That's it. That's the entire lesson. → The signal hiding in plain sight RBI's Financial Stability Report, June 2025: Average borrower debt: ₹4.8 lakh. Up from ₹3.9 lakh in March 2023. 54.9% of all household debt is now non-housing consumption loans, personal loans, credit cards, consumer durables. People aren't borrowing to build assets. They're borrowing to fund monthly expenses. That's not a credit market. That's a stress signal. And it shows up in transaction data months before the first missed EMI. The lenders who see it early restructure. The ones who miss it become an NPA statistic. → The one rule that never broke RBI caps single-borrower P2P exposure at ₹50,000. That number exists because the data demands it. ₹5 lakhs across 25 borrowers performs entirely differently from ₹5 lakhs across 5 borrowers. Same capital. Same platform. Same rate. The only variable is concentration. A 5% default on a 25-borrower book is a bad month. A 5% default on a 5-borrower book is a catastrophic quarter. Diversification isn't a strategy. It's the product. → 10 years of lending data. One thing no model fully captures: Borrower intent at origination and borrower behaviour under stress are different datasets entirely. Everyone intends to repay. The question your underwriting should answer is simpler and harder: What happens to this person when something goes wrong? That answer is already in their data. You just have to know where to look.

Are Credit Card Payments Signaling Trouble for Consumers? #chartoftheweek The stimulus checks and lockdown-driven savings that initially boosted household balance sheets during COVID-19 have long since passed. The chart below highlights the share of credit card accounts making only the minimum required payment – which is often not enough to cover interest charges. While credit card spenders paying the minimum percentage saw a steep decline in 2020, it has picked up over the past few years as consumers see the effects of stimulus checks ending, elevated inflation, and increased interest rates. With the percentage of minimum payer accounts now well above the 12 year average, household balance sheets deserve a closer look today. A growing portion of consumers making only their minimum payments on their credit cards could suggest households are facing tighter budgets and have a more limited ability to pay down debts. If this minimum payment trend persists, we could see this translate to decreased discretionary income and consumer spending, which could imply headwinds for segments of the economy such as hospitality and consumer discretionary. We expect high-quality companies with durable cash flows to best positioned to weather a potential pullback in consumer spending. While there are certainly a number of metrics for evaluating consumer strength, we feel this is a useful and intriguing piece of the mosaic. How are you considering household balance sheets and consumer spending in today’s market? Source: Federal Reserve Bank of St. Louis, Federal Reserve Bank of Philadelphia, Large Bank Consumer Credit Card Balances: Share of Accounts Making the Minimum Payment. Data displayed from 6/30/2012 through 12/31/2024, showing the 4-quarter trailing average level. “Average” level calculated over the full period and “Pre-Covid Level” is for the quarter ending 12/31/2019. It is not possible to invest directly in an index.

This is one of my favorite releases provided by the Federal Reserve Bank of New York Yesterday, they released the Household Debt and Credit Report. It covers loan production, debt levels, and delinquency using consumer credit files and other data points. Delinquency transition rates increased for all product types. Over the last year, approximately 8.9% of credit card balances and 7.9% of auto loan balances transitioned into delinquency. Early delinquency transition rates for mortgages increased by 0.3 percentage point yet remain low by historic standards. “About 121,000 consumers had a bankruptcy notation added to their credit reports in 2024Q1, more than in the previous quarter” Chapter 7 consumer BKs remain ~50% below 2019 numbers, and Chapter 7 roughly 30% below 2019. Overall, delinquencies are generally in line or below pre-COVID levels aside from credit cards and auto loans. These data points unfortunately continue to move in the wrong direction. Important to note that in the charts below, many figures are based on a four quarter moving sum, which will cause some smoothing but also show a gross figure that could be more alarming than the actual figures measured on simple DQ percentage.