In an interaction with Adlin Pertishya Jebaraj, correspondent of Finance Outlook Magazine, Satinder Aggarwal, Founder & CEO of EQBAC, delves into how intelligent investing platforms can facilitate or foster AI-based personalization, manage risks continually, and provide scalable advisory models. He noted that there is a need for automation to be balanced by human intervention for trust, transparency and regulatory compliance. 

With over 25 years in banking, wealth management, and private investment across India, UAE, and the Gulf, Satinder has built EQBAC with one simple but powerful principle: good finance should feel personal. Providing clients within the High Net Worth segment with comprehensive, individualized, long-term financial strategies that adapt to the changing needs of their investment portfolios. 

How do you see the current intelligent investing platforms reshaping global wealth creation compared to traditional wealth management models?

Intelligent investing platforms are setting a new standard for how wealth is built and managed worldwide. What makes them different is their ability to combine advanced analytics, real-time data processing, and adaptive portfolio design in a way that traditional wealth models were never built to handle.

For years, wealth management has relied heavily on advisor capacity, static asset allocations, and fragmented access to products across regions. Today’s intelligent platforms remove those constraints. They use AI to deliver deeply tailored portfolios that evolve with market movements, client behavior, and changing risk profiles. This level of precision used to be available only to ultra-wealthy clients with institutional resources; now it’s accessible at a wider scale.

The economics of the industry are also being rebalanced. Value is increasingly accruing to platforms and advisors who pair human judgment with AI-driven decision intelligence. This strengthens client confidence, improves retention, and helps smaller firms compete with global incumbents through superior technology rather than scale alone.

What we are ultimately witnessing is the formation of an entirely new wealth infrastructure that is more responsive, more inclusive, and far more efficient.

How do intelligent platforms balance automation with human oversight to maintain trust and regulatory compliance? 

Intelligent platforms manage the balance between automation and oversight by placing structure around how each layer functions. Automation takes charge of the high-volume, rules-based activity that needs accuracy every single time, like data consolidation, suitability checks, ongoing monitoring of portfolios, and the continuous risk scans that run in the background. 

Oversight comes in where judgment and accountability are essential. Advisors review the outputs the system generates, interpret exceptions, and step in where a client’s situation requires context that goes beyond what models can infer. Compliance teams follow a similar rhythm. The interpretation of a regulatory nuance or the approval of a sensitive transaction remains with experienced professionals.

Transparency has become a cornerstone of this model. Recommendations must be traceable. Risk assessments must be explainable. Every action the platform takes leaves a documented footprint that supports regulatory review and strengthens client confidence. Investors should be able to see the logic behind their portfolios, understand the decisions taken, and explore scenarios before committing to those decisions.  

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What financial models are most viable for intelligent investing platforms, AUM-based fees, subscription models, or transaction-based revenue?

AUM-based fees remain the most reliable model for intelligent investing platforms, especially when serving high-net-worth and ultra-high-net-worth clients. The structure supports long-term engagement and gives the platform room to deliver the level of oversight, customization, and risk management these clients expect. Most advanced advisory layers sit naturally on an AUM foundation because the work is continuous. 

Subscription pricing works well in segments where advice is standardized and delivered primarily through digital channels. Platforms focused on mass-affluent investors often use this model because it offers predictable income and gives clients a clear understanding of what they are paying for. It suits environments where the service is driven by tools, education modules, planning features, and periodic guidance rather than full-scale discretionary management.

Transaction-based revenue has become far less central in modern platforms. Regulations have tightened expectations around conflict-free advice, and volume-linked incentives do not sit comfortably within that framework. These fees now appear mostly around specific products or liquidity events in alternatives, where the work involved justifies a separate charge. 

Most platforms, therefore, take a combined approach. An AUM or subscription-based model creates stability, and additional performance-linked or transaction-specific fees apply only where the offering requires specialized effort.  

How do intelligent investing platforms differentiate themselves in an increasingly saturated fintech ecosystem? 

Differentiation in this space rarely comes from the technology itself. Most firms have access to similar tools. What sets the stronger platforms apart is the depth of judgment built into the experience and the ability to translate information into guidance that actually helps someone make a better financial decision. 

Another area where real separation occurs is the quality of access. Investors want opportunities that are filtered, relevant, and operationally clean. When a platform can deliver that kind of access with consistency and proper oversight, clients tend to stay for the long term.

A good platform also operates like a complete financial environment. Reporting ties together performance, risk, and liquidity in a way that helps clients make sense of their overall position.

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What innovations in tokenized assets, AI-driven financial planning, and hyper-personalized portfolios will redefine investor experience in the coming decade? 

Over the next decade, three developments will meaningfully influence how investors engage with wealth platforms: tokenized assets, advanced AI planning, and deep personalization. Each of these areas is maturing quickly, and together they create a far more fluid and responsive investment environment than the one we operate in today. 

Tokenization is laying the groundwork for broader participation in asset classes that were once difficult to access. Real estate, private credit, treasuries, and even infrastructure projects are moving toward fractional, instant-settled formats. This reduces friction and opens doors for investors who want global exposure without long settlement cycles or high entry thresholds. As regulation firms up, tokenized instruments will sit alongside traditional securities rather than outside them, which will accelerate adoption. 

AI-driven planning will bring real discipline into the way investors respond to uncertainty. The ability to model forward-looking scenarios, integrate tax and cash-flow considerations, and update recommendations in real time shifts financial planning from periodic reviews to continuous guidance. The more sophisticated systems also understand behavioral tendencies, which help investors avoid decisions that undermine long-term outcomes.

Hyper-personalized portfolios take this a step further. Direct indexing, granular risk mapping, and ongoing data inputs allow each portfolio to reflect the investor’s actual financial life, not a generalized risk category. Over time, this level of precision sets new expectations around what “advice” should deliver. 

These advances point toward an investor experience that is more informed, more accessible, and far more aligned with individual goals.