A Review of Finance and Wealth Management in India Using Artificial Intelligence

The review concludes that Artificial Intelligence has made a measurable and statistically supported contribution to finance and wealth management in India, but that the contribution is narrower and more conditional than the prevailing commentary suggests. Wherever outcomes are quantifiable and feedback is quick, AI is already an everyday utility: fraud is detected, credit is priced, queries are answered, documents are read. Where outcomes are slow to reveal themselves and judgement is inextricable, not least in comprehensive personal financial planning, it remains a promise. The force preventing the distance from being closed is not modelling capability. The techniques are available, the data is in abundance, and the costs of computation are falling. What limits adoption is the constellation of problems the reviewed literature most insistently identifies: a model whose reasoning cannot be explained cannot be vindicated before a regulator or a customer; data that cannot be governed cannot be safely used; and an investor who does not understand a recommendation will not act upon it, however compelling the recommendation may be. These are institutional issues, and India's regulators have perceived them to be such. The FREE-AI framework and the SEBI amendments of 2025 should be read as the conditions under which innovation at scale can take off. For India specifically, the most crucial finding concerns distribution. The households likely to benefit most from algorithmic advisory are those currently sporting consolidated, poorly diversified and behaviourally driven portfolios, and these are households of small means in smaller metros and towns. Serving them is technologically simple and commercially unattractive. Whether AI in Indian wealth management will be a financial inclusion tool or an efficient channel for serving the already wealthy will be decided by product design, distribution economics and regulatory incentives, not by model quality. That, rather than the search for incremental predictive accuracy, is where the next decade's effort in the field should be directed.

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