“AI Is not a fortune teller”: Rahul Ghose on the limits of algorithmic investing

With AI revolutionizing financial services, the convergence of technology, trust, and investor sentiment is a major point of interest in India's fintech industry. Speaking to ET Digital, Rahul Ghose, MD and CEO of Octanom Tech and Hedged.in, detailed his company's strategy of using AI as a disciplined risk-management component to refine investor judgment, rather than to supersede it. Ghose offers his viewpoint on how AI-powered investment platforms can achieve a balance between innovation and transparency amidst volatile markets. His insights span retail involvement, behavioural tendencies, hedging approaches, accountability, and the evolving psychology of wealth generation. Edited excerpts.

The Economic Times (ET): AI is increasingly stepping into roles traditionally held by financial advisors. How is Hedged ensuring that algorithm-driven investment guidance builds trust rather than skepticism among Indian retail investors?
Rahul Ghose (RG): I don’t think Indian investors are skeptical of AI for the wrong reasons. In fact, I would be more concerned if they trusted it too quickly. Retail investors have already seen too many versions of “easy money” like tips, calls, Telegram groups, screenshots of profits, and influencer-led confidence. So, when a new technology claims it can help them invest better, skepticism is natural.


At Hedged, we are very clear that AI is not a fortune teller. We use it more as a discipline layer. It can analyse market data without emotion, identify changing risk conditions faster, and help investors avoid decisions driven purely by panic or excitement. However, trust will not come from simply saying something is “AI-powered.” Trust comes from showing the logic, explaining the risks, and being honest when the system does not have certainty.

ET: With only about 3.5% of Indians participating directly in equities, what are the biggest psychological and structural barriers and how does Hedged’s model specifically address them?
RG: The biggest barrier today is no longer access. Access has improved dramatically. The bigger challenge is confidence. Most Indians understand saving. They understand gold, real estate, and fixed deposits. Equities, however, still feel like a space where either very smart people make money or very lucky people make money, while everyone else gets punished for entering late.

That perception does not disappear simply because someone is given an app. The first barrier is fear of loss. The second is information overload. The third is language. Over time, investing has been made to sound far more complicated than it actually needs to be.

At Hedged, we address this through risk-managed participation. The objective is not to encourage first-time or growing investors to take larger bets. The idea is to help them participate with structure, where downside risk is visible and decisions are not driven by market noise. India does not need more excitement around markets; it needs greater confidence supported by proper guardrails.

ET: There’s a growing global narrative that traditional long-term retirement planning may evolve significantly due to AI and changing work patterns. Do you see Indian investors rethinking wealth creation timelines as well?
RG: Yes, but I believe the Indian version of this shift will evolve differently. In the West, people are already questioning the traditional retirement model because the nature of work itself has changed. Careers are less linear, incomes are less predictable, and many people are freelancing, consulting, building side incomes, or taking career breaks either by choice or necessity.

India is beginning to experience a similar shift, especially among younger professionals. Earlier, the journey was relatively straightforward-study, secure a stable job, save consistently, buy a house, invest gradually, and retire at 60. That model still exists, but it is no longer the only aspiration people have.

Younger Indians are now asking different questions: Can I build wealth faster? Can my capital work harder? Can I take career risks if my investments are structured well? Can I achieve financial independence earlier? These are important questions, but they also come with risks. Rethinking wealth timelines should not become impatience disguised as ambition.

AI can help investors model scenarios, understand risks better, and make more informed decisions. However, it cannot change the fundamental reality that wealth creation still requires time, discipline, and behaviour. The goal should be to accelerate intelligently, not rush blindly.

ET: Critics argue that AI in finance can create a false sense of certainty. How do you ensure users understand both the power and the limitations of algorithmic recommendations?
RG: This criticism is completely valid. AI becomes dangerous when it presents probability as certainty. Markets are not machines. They are living systems shaped by data, liquidity, policy decisions, earnings, fear, greed, and at times, complete irrationality. Therefore, the first principle has to be humility.

At Hedged, we do not want users to think, “The system has said this, so it must happen.” Instead, we want them to understand that the system is identifying a high-probability situation based on current evidence, while recognising that risk still exists. That distinction is critical.

The strength of AI lies in its ability to process large amounts of data without fatigue, emotion, ego, or panic. Its limitation is that markets can still behave in ways that historical data may not fully capture. That is why every recommendation must be framed with proper context- what the risk is, what the probabilities are, what could go wrong, and where the view becomes invalid.

A good financial AI system should not make investors overconfident. It should make them more aware of what they do not know.

ET: What differentiates Hedged’s AI-driven insights from the surge of generic chatbot-based financial advice tools entering the market?
RG: A chatbot may provide answers, but that does not mean it can manage market risk. There is a significant difference between financial information and investment intelligence. A generic chatbot can explain what a stock is, what diversification means, or how mutual funds work. That information is useful, but it is not the same as helping someone make decisions in live market conditions.

Hedged is built around market behaviour, hedging frameworks, risk signals, derivatives intelligence, and real-time decision systems. We are not trying to answer generic finance questions. We are focused on helping investors understand what actions to take under changing market conditions. The second differentiator is context.

A bullish market, a sideways market, and a panic-driven market each require very different behaviour. Generic advice may sound correct in theory, but it often fails precisely when investors need guidance the most.

Our technology is built on market experience, not just data science. That distinction matters because markets are not purely mathematical-they are deeply psychological. Any platform that does not understand investor behaviour will eventually misjudge investor risk.

ET: In volatile markets, human emotions often drive poor decisions. Can AI realistically override fear and greed, or does it simply repackage risk in a more sophisticated way?
RG: AI cannot eliminate fear and greed entirely-that would be unrealistic. If an investor wants to panic, no dashboard can physically stop them. Similarly, if someone wants to chase a stock after a sharp rally, no algorithm can intervene and prevent that decision.

What AI can do, however, is create a pause. And sometimes, that pause is enough. Most poor investment decisions happen within a very small emotional window. Markets fall and investors react impulsively. Markets rise and investors fear missing out. Markets move sideways and investors become impatient.

A good system can slow down those impulses. It can help investors assess whether risk conditions have genuinely changed, whether exposure levels are too high, and whether decisions are being driven by data or emotion.

So, I would not say AI overrides fear and greed. Rather, it acts as a mirror before a mistake is made. That is only useful if the mirror is honest. Otherwise, AI simply makes overconfidence appear more sophisticated.

ET: Hedging strategies are often seen as complex and accessible mainly to institutional investors. How are you simplifying this for retail users without diluting effectiveness?
RG: The issue with hedging is not that retail investors cannot understand it. The issue is that it has often been explained poorly. Most people hear terms like options, spreads, volatility, downside protection, theta, or delta, and immediately disengage. However, the basic idea behind hedging is very simple and very human.

It essentially means: “I want to participate, but I do not want to be severely impacted if I am wrong.” That is simply common sense. At Hedged, we aim to bring that common sense back into investing. The complexity can remain within the engine, but the user experience must stay simple and clear. At the same time, simplification cannot come at the cost of transparency. Hedging has a cost. Protection has a cost. Lower risk often comes with trade-offs. If those realities are not explained clearly, then it is not simplification, it is misrepresentation.

Our role is to simplify the decision-making process without diluting the strategy itself. Investors should understand what the strategy is trying to achieve, when it may work, when it may underperform, and what trade-offs are involved. Retail investors do not need jargon; they need clarity.

ET: Tools like the Nifty Crash Meter suggest predictive capabilities. How should investors interpret such signals without over-relying on them?
RG: The Nifty Crash Meter should be viewed as a risk-awareness tool, not as a crystal ball. Markets rarely provide perfect warnings. Instead, they indicate changes in conditions like volatility shifts, weakening breadth, slowing momentum, and changes in liquidity. Certain patterns begin to emerge over time.

A tool like the Crash Meter helps investors interpret these conditions more effectively. However, no investor should look at such signals and conclude, “The market will definitely crash tomorrow.” That would be the wrong way to use the tool. The correct interpretation is: “Risk levels appear to be changing. Should I reduce exposure? Should I hedge? Should I avoid taking fresh aggressive positions? Should I wait for stronger confirmation?”

That is how professional investors think. They do not constantly ask whether the market will crash. Instead, they ask whether they are being adequately compensated for the level of risk they are taking. The purpose of such tools is not to create fear, but to build awareness before fear takes control.

ET: With automation and AI expected to reshape jobs and income stability, do you think younger investors in India should rethink traditional savings vs. active investing strategies?
RG: Younger investors should reconsider the traditional equation, but they should do so carefully. Earlier, income patterns were more predictable for many people. Individuals joined a company, stayed for several years, received steady increments, saved regularly, and invested gradually. That model still works for some, but it is no longer universally applicable.

Today, careers are more fluid. People change jobs more frequently. Many have side incomes, freelance work, or periods of uncertainty. Automation and AI are likely to increase this unpredictability further.

So yes, younger investors need to become more active in how they think about money. However, being active does not mean constantly trading. It means understanding risk, asset allocation, liquidity, emergency funds, taxation, and changing income patterns. Capital should not remain idle, but it should also not be deployed recklessly.

Before taking market risk, individuals should build survival capital, maintain emergency funds, secure adequate insurance, and understand their monthly financial commitments. Once that foundation is in place, investing can be approached in a more structured manner. AI can support better decision-making, but it cannot compensate for poor financial discipline.

ET: With brokerage platforms, fintech apps, and now AI tools all competing for investor attention, where does Hedged position itself in this crowded ecosystem?
RG: We are not trying to become another brokerage platform, another content platform, or another chatbot offering generic financial explanations. Hedged operates in the decision layer. Brokerages help users execute trades. Content platforms help users learn. Fintech platforms improve access to financial products. But between learning and execution lies a crucial question: “What should I actually do, given the current market conditions and my risk profile?”

That is where Hedged positions itself. Our focus is on risk-managed investing and trading. We want to help users participate in markets without being completely exposed to market shocks or emotion-driven decisions.

The market does not need more noise; it needs better filters. Our goal is to help Indian investors transition from excitement-led investing to structure-led investing. Access has already improved significantly. The next challenge to solve is decision quality.

ET: How do you address accountability? If an AI-driven recommendation leads to losses, where does responsibility lie?
RG: Accountability in financial advice cannot be vague. First and foremost, every investor must understand that markets inherently involve risk. No recommendation, whether human-led or algorithm-driven, can eliminate that risk entirely. Losses are a natural part of investing.

However, that does not mean platforms have no responsibility.

The platform is responsible for maintaining the quality of its processes, ensuring clarity in disclosures, and communicating risks honestly. If a system presents a high-risk idea as completely safe, hides assumptions, or creates a false sense of certainty, that becomes a serious issue.

At the same time, investors also carry responsibility. A recommendation should not become a license to overexpose capital, ignore suitability, or treat markets as a guaranteed source of income. Accountability therefore exists on both sides, although platforms must hold themselves to a higher standard because of the influence they have on investor behaviour.

For us, the principles are straightforward: regulatory compliance, process integrity, and transparent communication of risk. If any of these are compromised, technology can become dangerous.