‘AI can help MFDs reduce the manual workload and focus more on relationship building’

“AI will probably most likely lead to the end of the world, but in the meantime, there will be great companies”.

This statement by Sam Altman, CEO of OpenAI, seems right when one thinks about the use of artificial intelligence in all the business sectors including the financial intermediary industry.

Cafemutual talked to Ashish Ingle, Founder of Mulyankan GPT to understand how MFDs can leverage AI tools to grow business.

Why are research and analytics important for the distribution business?

The primary role of MFDs is to bring in capital. Their secondary role is to allocate that capital in a way that delivers maximum returns for clients with minimal risk. Previously, allocation decisions were largely based on client risk profiling. However, this approach overlooks the importance of profiling asset classes and categories for risk.

Asset class risk profiling involves analysing the risks and opportunities within a specific asset class, depending on the current market cycle. This includes factors like the value cycle, earnings cycle and interest rate cycle. Such analysis helps determine whether a given asset class is favourable or unfavourable under prevailing market conditions.

What are the changes that you think AI will bring to this business?

Traditionally, MFDs and financial advisors made investment decisions based on gut feeling and personal industry experience. This has led to a lack of structured decision-making across the distribution business. AI can change this by helping MFDs/advisors develop systematic and data-driven strategies.

AI tools will function for MFDs much like diagnostic tools do for doctors—analysing vast datasets that are too complex for manual processing. As the Indian financial market matures, it will inevitably grow in complexity, increasing the volume of data that advisors must interpret. AI will help reduce the manual workload, allowing MFDs to focus more on relationship building and business expansion.

Ashish, you are providing a research tool for the MFDs, but there are several SEBI-registered research analysts providing fund research and many MFDs rely on in-house research to shortlist funds. Why should they consider external platforms?

Currently, most research in the industry focuses on fund-level and AMC-level fundamentals. However, broader market dynamics—such as the suitability of SIP vs. lump sum investments or the favorability of a specific asset class—are often ignored. AI tools can bridge this gap by helping MFDs evaluate the right time to invest in specific asset classes or categories. They can also help identify optimal entry and exit points in the market.

In short, AI enables MFDs to move beyond the traditional ‘one size fits all’ model toward more personalized, data-driven decision-making, while still incorporating their own industry experience.

Can you briefly describe what Mulyankan GPT is and how it works for financial advisors?

It is an AI-powered platform designed specifically for financial distribution professionals. Unlike general AI models, Mulyankan GPT is trained on finance-related data sets relevant to Indian markets.

It is positioned as a tool to help with portfolio structuring and strategic investment decisions based on the combination of AI analysis and personal inputs from the professionals.

The tool does not offer direct fund recommendations. Instead, it presents data-backed trends and rationales for advisors to evaluate.

The intent of this platform is to provide transparency in the analytical process so users can validate or challenge the findings before making decisions.

What are the unique features of this platform and what are the things in which it can help the MFDs?
Flexibility to devise your own strategies and methods is one of the unique features. Users of the platform have the option to:

•             Use the AI-generated analysis while reviewing the rationale behind it, or

•             Create their own analysis frameworks by selecting metrics and assigning weightages according to their personal inputs

The users can use Mulyankan GPT to:

• Evaluate lumpsum vs. STP investments
• Identifying appropriate timing for initiating or stopping STPs
• Making asset class, category, and sub-category allocations
• Conducting investor profiling and assessing risk tolerance

What is the pricing structure and current adoption level?

The platform has over 900 users after 18 months of development. We have set a target to reach 1,500 users by the end of 2025.

Pricing:

• Rs.60,000 for a 3-year subscription

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