Behavioral Finance Concepts

Kya aapke dimaag mei bhi alag-alag money ke dabbe hai? 🧠💰 Issi ko Mental Accounting kehte hai Mental Accounting is when we treat money differently based on where it came from or what we plan to use it for. For instance: 💼 Salary ka paisa = "Responsible spending" 🎁 Gift ka paisa = "Fun money" 💰 Unexpected bonus = "Splurge!" Why does this happen? Our brain likes to organize things, including money, into different categories. How it affects us: ✅ Can help in budgeting (e.g., separate accounts for bills, savings, fun) ❌ But can also lead to irrational financial decisions Examples: Keeping money in a low-interest savings account while having credit card debt Why? "Savings" & "debt" are in different mental accounts Spending a ₹5000 tax refund on a luxury item Why? It feels like "free money" rather than part of your regular income Tricks to overcome it: 👉🏻 Recognize that all money is the same, regardless of its source 👉🏻 Make financial decisions based on overall financial health, not individual "accounts" 👉🏻 Regularly review your entire financial picture, not just individual parts Next time you get unexpected money, pause & think: "What's the best use for this in my overall financial plan?" Want to understand how mental accounting affects your finances? Let's connect over video call https://lnkd.in/dYMxvvk5 #MentalAccounting #MoneyPsychology #PersonalFinance #BrahmiKapasi

Can Stablecoins hold the answer to one of the biggest challenges – and at the same time opportunities – in #financialservices today? Let’s take a look. According to IFC 65 million firms, or 40% of formal micro, small and medium enterprises (MSMEs) in developing countries, have an unmet financing need of $5.2 trillion every year, which is equivalent to 1.4 times the current level of the global MSME lending! One of the main reasons that this huge #financing gap still exists, even though everyone acknowledges the untapped potential and opportunity has to do with the structure of the modern financial system: Funding is mainly funneled via banks and a very large portion of small businesses do not meet their risk criteria due to: 1. The inherent complexity of SMEs 2. A lack of understanding of their needs from formal lenders, which is a direct result of limited credit modeling sophistication (i.e. granular segmentation) that makes it very costly to service them 3. Unavailability of formal data points based on which banks build their credit models Supply chain financing (SCF) has long been heralded as a solution for covering part of the gap, however it has not managed to address suppliers across the entire value chain and particularly those at its lower end. An ambitious project called Dynamo has tried to turn the problem on its head by using #blockchain to address the main challenge of the traditional SFC flow: trust. Lack of trust is the main reason why all these intermediaries (basically banks) exist in the flow in the first place. They make sure that payment is made only when all conditions are met. Here’s is the new approach: —    Digital Trade Tokens (DTTs) - basically stablecoins minted on Ethereum public blockchain - become the means of payment. —    Funds behind the DTTs are ringfenced in the sense that they are only converted to cash when conditions are met (i.e. delivery of goods). —    Conditions are coded on smart contracts on blockchain and #payments are automatically executed once the pre-set conditions are fulfilled. —    The buyer sends the DTTs to the supplier, but until proof of delivery is there the supplier doesn’t have access to real money but has a number of options 1) keep DTTs and wait until proof of delivery 2) Use DTTs (i.e. sell them) as a means to get funding (factoring mechanism). —    The process works exactly the same if suppliers further down the value chain end up with the tokens. They have the same (sell or hold) options and DTTs are converted to cash once the set conditions are met. —    Given that each shipment is unique, DTTs are non-fungible, meaning that they are non-interchangeable and can't be replaced. Although the project involved several drawbacks (i.e. regulation, privacy, integration costs, fluctuating gas fees) that need to be addressed, the idea of connecting programmable payments with trade finance is very promising. Opinions: my own, Graphics and source: BIS Innovation Hub

𝗕𝗲𝗵𝗶𝗻𝗱 𝗘𝘃𝗲𝗿𝘆 𝗦𝘄𝗶𝗽𝗲 💳 — 𝗧𝗵𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗼𝗳 𝗖𝗿𝗲𝗱𝗶𝘁, 𝗗𝗲𝗯𝗶𝘁 & 𝗕𝗡𝗣𝗟 Every payment rail represents a different architecture of trust, how risk, liquidity, and data flow through the system. Let’s decode what really happens beneath the tap, click, or scan 👇 💳 𝗖𝗿𝗲𝗱𝗶𝘁 𝗖𝗮𝗿𝗱: 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗙𝘂𝘁𝘂𝗿𝗲 𝗟𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 Operates on a post-paid model — the issuer funds the transaction instantly, assuming repayment later. Involves authorization → clearing → settlement → reconciliation, often across multiple intermediaries (issuer, acquirer, network). Data intelligence: Credit bureaus, risk scores, and spending patterns train issuer models for real-time underwriting. Key trade-off: Rewards and flexibility come at the cost of interest exposure and long-term behavioral data tracking. 🏦 𝗗𝗲𝗯𝗶𝘁 𝗖𝗮𝗿𝗱: 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗣𝗿𝗲𝘀𝗲𝗻𝘁 𝗟𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 Executes on a real-time balance validation through core banking APIs. Each swipe directly debits funds, eliminating credit risk for issuers. Security depends on EMV tokens, PIN validation, and network authorization latency. Ideal for predictable, low-risk, day-to-day transactions. 🛒 𝗕𝗡𝗣𝗟 (𝗕𝘂𝘆 𝗡𝗼𝘄 𝗣𝗮𝘆 𝗟𝗮𝘁𝗲𝗿): 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 Functions as a micro-credit abstraction layer built on top of e-commerce checkouts. Uses AI-driven risk assessment (alternative data, device signals, behavioral analytics) for instant approvals. Merchants get paid immediately, while consumers repay in split installments — shifting credit risk from user to provider. Integrates with APIs like Plaid, Stripe, or Affirm SDKs to perform KYC, scoring, and settlement in milliseconds. #Fintech #Payments #BNPL #FinancialInfrastructure

Most people don't know how long they'll live in retirement. That uncertainty is normal. But what they believe about how long retirement lasts has real consequences. Our new report shows that workers' expectations about retirement duration have a powerful effect on how they save. Those who expect a longer retirement save more, save more consistently, and plan more carefully. Those who expect a short retirement? Far less so. Only about half of workers who expect fewer than 10 years in retirement save regularly. Among those who do, contributions are modest. Compare that to workers who anticipate 30 or more years in retirement: 71% save regularly, and at meaningfully higher rates. This matters because those expectations don't form in a vacuum. They are shaped, in large part, by how workers perceive general life expectancy. And on that question, many workers are simply wrong. Thirty-six percent underestimate how long 65-year-olds typically live. Another 18% admit they don't know. Workers who underestimate life expectancy tend to expect shorter retirements and, as a result, save less and plan less. If a long retirement does arrive, they may not be financially prepared for it. When workers don't have accurate information about how long people typically live past 65, their planning horizons are effectively too short. Better longevity literacy can shift expectations and, with them, behavior. Retirement security starts with understanding what retirement might actually look like. That means not only knowing how to save, but understanding why the time horizon matters so much. Here is the link to the report from the Global Financial Literacy Excellence Center (GFLEC) and the TIAA Institute, take a look: https://lnkd.in/gvnKMzwH

Thoughtful digital design can drive good investment behaviors, such as saving more than you spend, maxing out an IRA, opening a 529 college savings account, selecting the right cost basis lots to minimize your tax burden, getting out of cash and getting invested, and staying the course even when markets tumble. Some organizations incorporate "dark patterns" into their digital design to foster addiction, speculation, and gambling that erodes wealth for their clients while driving bad revenue for their firms. We do the opposite. Our digital experience is built to make all clients better investors. As one example, most people don't realize that roll-overs from 401(k) plans are often defaulted into cash in an IRA. Investors will unwittingly move from low-cost, diversified investments into cash, missing out on the long-term benefits of compounding. Vanguard research has shown that nearly one-third of investors rolling out of a 401(k) were still sitting in cash 7 years later, costing them over $170B per year in collective potential retirement wealth. We saw this problem and built a set of thoughtful nudges, digital interventions, and design choices that ultimately helped clients move over $6.2B out of cash and into diversified investments. This is just the beginning. We're building a personalized digital ecosystem to help all clients become better investors.

Retirement Isn’t Just Financial — It’s Existential We plan retirement like we’re flying a jet: spreadsheets, savings targets, health care hurdles, destination retirement communities. But as the Wall Street Journal (https://on.wsj.com/4sWY2C6) recently highlighted, most of us never plan for how we will continue to matter once work ends — and that oversight can be more destabilizing than any market downturn. The article opens with retirees in Sarasota, Florida — professionals who expected that their decades of experience would easily translate into new roles as consultants, volunteers, or teachers. Instead, they found closed doors and unanswered emails. What they mourned wasn’t just opportunity lost; it was the loss of “mattering” — that sense that their presence, experience, and contributions were still needed. Economists and psychologists have long shown that retirement isn’t merely a financial state; it’s a psychological transition. The financial planning we obsess over prepares us for longevity, but almost no one prepares for the mattering span — the emotional and social reality of being seen, valued, and needed. Research shows that the strongest predictors of post-retirement well-being aren’t the size of your portfolio, but the presence of connection, contribution, and purpose. The article frames mattering around a simple concept: people thrive when they feel significant, appreciated, invested in, and depended on. Retirement often disrupts all four at once because work carried all of those signals daily. As we age, it’s not about being youthful. It’s about being useful.  I see this as a larger life lesson: purpose isn’t something you earn only through work; it’s something you carry forward into your next chapters. A function of life, not just an outcome of employment. If we change the central question from “Have I saved enough?” to “How will I continue to matter?”, retirement becomes not a sudden end but a deliberate transition — a space to build new forms of contribution, connection, and belonging. Or here’s another reframe. Let’s move from “How will I spend my retirement?” to “How will I invest my wisdom?”

Have you been watching the World Cup? Here’s an interesting angle: Goalkeepers are roughly twice as likely to save a penalty if they stand still than if they dive. Yet they almost never do. In one famous study of professional penalties, goalkeepers dived left or right around 97% of the time. They stayed in the middle just 3% of the time. Why? Because diving looks like effort. Standing still looks like they’ve given up. Behavioural economists call this “action bias”: our tendency to do something rather than do the right thing. Investors fall into exactly the same trap. Markets drop. The headlines become frightening. Your portfolio falls in value. Suddenly, doing nothing feels irresponsible. So people sell. They switch funds. They move to cash. They try to “protect” themselves. The irony? For long-term investors, the decision that feels the hardest is often the one that produces the best outcome. Just like the goalkeeper, investors - and fund managers- worry more about looking inactive than making the optimal decision. That’s why one of the most valuable jobs of a financial planner isn’t recommending investments. It’s preventing clients from making emotional decisions at exactly the wrong moment. Sometimes the best investment decision you’ll ever make isn’t buying or selling. It’s simply refusing to dive.

Behavioural Modelling: The Hidden Lever in Asset and Liability Management Behavioural modelling is often treated as a technical detail in ALM. Yet it can be one of the most powerful levers in understanding and managing a bank’s balance sheet risk. Done well, behavioural models improve the accuracy of cash flow projections, optimise hedging decisions, and support a more realistic view of structural interest rate risk. Done poorly—or worse, left static—they can distort IRRBB measurement, liquidity forecasting, and FTP alignment. There are three common misconceptions that limit the effectiveness of behavioural modelling: 1. Behavioural models are not just for NMDs. While non-maturity deposits tend to attract the most modelling focus, behavioural assumptions also impact early repayments on mortgages, revolving credit usage, and even drawdown behaviour on undrawn limits. Ignoring these leads to skewed duration estimates and misplaced hedges. 2. Behavioural assumptions must be forward-looking, not just historically anchored. A model built on the past five years of customer behaviour may not reflect today’s reality. Rising rates, changing customer incentives, and digital banking adoption all alter behaviour. A static model based on outdated data will mislead, not inform. 3. Behavioural models are not a compliance checkbox. They are strategic tools. Used properly, they help treasury teams build more conservative and realistic views of risk, test stress scenarios more accurately, and improve alignment between ALM and commercial strategy. So what does effective behavioural modelling look like? It involves periodic recalibration, close collaboration between treasury and data teams, and back-testing against actual outcomes. Models should incorporate both quantitative patterns and qualitative judgement—particularly when customer behaviour changes faster than historical data can capture. They should also be integrated across disciplines: ALM, FTP, liquidity, IRRBB, and even business line pricing. Silos reduce effectiveness. When done properly, behavioural models are not just technical artefacts. They are enablers of prudent growth. They ensure the bank’s measured risks reflect reality—not assumptions. And in an environment where margin is tight and liquidity is precious, that can make all the difference. To learn more about behavioural modelling, IRRBB, and ALM strategy, visit the Global Banking Hub for expert-led courses and practical resources.

Financial advisors: CPI day is rarely a data day. It’s an emotion day. Because inflation is experienced (grocery aisle, pharmacy, gas station)… not observed (a quarterly statement), CPI headlines can trigger fast, visceral reactions. "Inflation is low? Where? Not for me!" Here’s a quick reference guide for the conversations this CPI cycle tends to trigger. What we learned from the January 2026 report: ✅ Prices: +2.4% YoY (lower than Jan 2020), +0.2% MoM ✅ Core Prices: +2.5% YoY (lowest since 2021) Notable categories (YoY): - Food +2.9% - Shelter +3.0% - Medical care services +3.9% - Energy -0.1% (gasoline -7.5%) Data wrinkle: Oct/Nov shutdown disruptions → messier trend lines Forward-looking signals (useful for context, not prediction): - Cleveland Fed nowcast has Feb CPI ~2.36% YoY - UMich: 1Y expected inflation 3.5% (down from 4.0%, lowest in a year) The 3 client conversations I’d expect (and the behavioral trap behind each): 1️⃣ Retiree: “I should take extra distributions.” Why it happens: present-bias + “visceral factors” (today’s bills crowd out tomorrow’s tradeoffs) Questions you may hear: - “What if inflation takes off again?” - “Can I pull $15–20k ‘just in case’?” Common mistake: a “temporary” higher withdrawal rate that quietly becomes permanent. Helpful reframe (without minimizing): “Headline CPI is one thing, but your personal inflation basket is another. Let’s map food/medical/shelter vs. what you actually spend.” 2️⃣ Pre-retiree: “Should I move everything to TIPS / I-Bonds?” Why: affect heuristic + narrow framing (optimize for one risk: inflation) Questions: - “Stocks and bonds both got crushed in 2022 when inflation climbed… why risk it?” - “Isn’t CPI-matching the safest plan?” Common mistake: over-correcting 5–10 years before retirement and underfunding long-run growth. Helpful context: “Expectations just dropped (UMich 1-year: 4.0% → 3.5%). Core is already at a cycle low (2.5%). Shifting everything now can be ‘buying insurance’ when the fire may be moving away.” 3️⃣ Business owner: “I can’t raise prices. I’ll lose customers.” Why: loss aversion + fairness concerns (people overestimate backlash) Questions: - “What if customers think I’m gouging?” - “Should I just absorb it until things settle?” Mistake: margin erosion that compounds quietly. Reframe & context: - "Bank of America surveyed and found 76% of business owners have raised prices this past year by 12% on average." - “Cost-based increases are perceived as more fair than demand-based ones, and your customers are already seeing price increases everywhere. They'll get it.” ▶️ Inflation is more emotional because it shows up daily. Daniel Kahneman's System 1/System 2 framework explains that rising prices are "affect-rich" stimuli processed intuitively and emotionally. Experiencing inflation dozens of times per week makes it feel more threatening than abstract portfolio risk. 👇 How are you responding to rising prices for your life, business, or clients?

I've seen brilliant founders fail and mediocre ones get funded. The difference wasn't talent. It was psychology. Research suggests several psychological patterns can influence investor decisions - often operating below the conscious level. Understanding these cognitive biases might play a bigger role in fundraising success than we often realize. Here are 5 cognitive biases that could affect your funding chances: 1. Similarity Bias Studies indicate that investors may gravitate toward founders with similar backgrounds, education, or thinking styles. This explains why some VCs appear to fund certain "types" of founders more frequently than others. → Sharing authentic common ground with investors could create meaningful connection points. 2. Loss Aversion Research in behavioral economics suggests people often feel losses more strongly than equivalent gains. This explains why some investors seem more concerned about missing the next big thing than finding it. → Framing opportunities in terms of potential missed opportunities might resonate differently than only highlighting potential gains. 3. Anchoring Effect First impressions may create reference points against which everything else gets measured. → The order in which information is presented matters more than we think. 4. Digital Presence Recent data suggests that some investors now spend an average of 37 minutes researching founders online before their first meetings. → Your digital footprint might be creating impressions before you even enter the room. 5. Optimism Gap There is a natural difference between how founders and investors view projections. → Backing ambitious forecasts with solid evidence can bridge this perception gap. Understanding these patterns has helped many of the founders I've worked with navigate the fundraising process more effectively. What's interesting is how rarely these psychological factors get discussed in standard fundraising advice. Has anyone noticed these patterns in their own fundraising experiences?