Leveraging Market Intelligence for Asset Management with GenAI

FinTech is one of the most invested AI verticals globally, and financial services is the leading industry for GenAI adoption. GenAI can significantly increase productivity gains across the value chain in the financial industry:

‍Personalization: provide customizable service (risk tolerance profiles, financial goals) and intelligent marketing (ad design)
‍Knowledge compounding: effective information sharing across asset classes and contents (data cleaning,  insights summary)
‍Research and investment: generate investment hypotheses, draft investment memos, and develop trading strategies
‍Back-office functions: democratization of coding, legal, compliance, and operational tasks

With all the hypes, the statistics show mixed opinions from investors and customers.  66% of investors were somewhat or very unlikely to use an AI financial advisor. 45% of them don't trust AI to advise them on their finances at all. On the other hand, 53% customers report trust in generative AI-assisted financial planning. Younger age customers like Gen Z have higher trust (55%) as opposed to baby boomers (46.9%) and Gen X (48.8%). Higher level of trust (73%) is observed in general content written by GenAI. Everything comes with risks. Besides the general risks inherited from GenAI technology such as information robustness, data privacy, embedded bias, and cybersecurity,  three inevitable challenges arise in the financial industry:

‍Synthetic Data Usage:  It can better capture the complexity of real-world events, efficiently  generating data for backtesting. Such data, however, automatically inherits real-world biases.
‍Explainability: To boost trust in generated contents, it is necessary to understand and explain the reasoning behind the decisions and actions when providing financial recommendations.
‍Financial Stability: Reliance on GenAI could amplify the homogeneity in credit underwriting and financial advice, which might accelerate procyclical financial conditions and raise liquidity risks.

Productivity and Cost Savings
‍The most resource-consuming tasks in financial industry include generating market insights, summarizing financial statements/expert analysis, and finding the right information. GenAI can effectively and instantaneously provide the relevant resources to be utilized for specific job functions such as portfolio management, market research, and corporate strategy.

Both AlphaSense and Streetbeat have leveraged GenAI to reduce manual efforts, aiming to boost productivity (per capita). offers an integrated market intelligence platform for enterprises. Their customized subscription pricing model depends on enterprise size, working hours, and content needs. GenAI chatbots provide service 24/7 on answering business and finance-related questions.  offers an e-brokerage platform for retail (individual) investors to monitor financial insights, manage investment strategies, and evaluate performance, by replacing traditional investment advisors with GenAI.

This is an inevitable trend that GenAI is taking over some job responsibilities. The costs (time, salary, opportunities, etc.) saved and increased productivity are converted to the potential revenue for GenAI start-ups.

Human Responsibilities Shift
As GenAI take over repetitive tasks and data intensive works, human supervision and Expert knowledge are needed more than ever. It is attractive to try out the GenAI products at beginning. Maintaining customers is the next focus.

The companies have adopted different approaches. AlphaSense provides live customer service and expert call, ensuring technological issues and domain knowledge questions can be answered to unlock the full functionalities of GenAI and avoid hallucinated information generated. Streetbeat offers diversified, personalized, and up-to-date investments strategies by actively collecting portfolios from expert asset managers and accommodating customer preferences. They further perform backtesting to verify the effectiveness before recommending solutions.

At the current stage, humans still have the final call or can pivot the decisions when the generated contents are presented. Although GenAI takes over some responsibilities, it remains as a tool. Next thing we see is probably GenAI agents that are able to perceive, reason, plan, act, and react by continuously and iteratively prompting itself until reaching the goal. For example, an AI agent can automate high frequency trading, in which providing the goal and risk tolerance is “All You Need”.