Lens on AI in securities market

One of the first measures taken by an Indian regulator vis-à-vis artificial intelligence (AI), last month the Securities and Exchange Board of India (Sebi) issued a consultation paper seeking feedback on its proposals to regulate the use of AI/machine learning (ML) in the securities market.

- SANDEEP PAREKH

As defined in the consultation paper, AI refers to technologies that allow machines to "mimic human decisions to solve problems". ML is a subset of AI, and refers to the automatic learning of rules to perform a task by analysing relevant data.

Currently, Sebi requires market infrastructure intermediaries such as stock exchanges, clearing corporations, depositories, etc., and intermediaries such as mutual funds to report AI/ML systems employed by them, thereby giving the regulator an insight into its use cases.

Sebi has identified that AI/ML is being used for various purposes. For instance, stock exchanges are leveraging AI for sophisticated surveillance and pattern recognition, and brokers are deploying it for product recommendations and algorithmic order execution. AI is also used for customer support.

Based on who is creating an AI/ML system, they can be classified into two categories—built in-house or sourced from a third party. In this context, it is also important to remember that AI/ML systems can be integrated with each other as well as with present systems.

Further, the capabilities of AI are expanding rapidly, with models making near-accurate predictions in finance and generating model portfolios that could, not too long from now, give a fund manager a run for their money.

In a forward-looking approach, Sebi's consultation paper proposes guidelines to be framed with five core principles—a model governance framework, investor protection, testing mechanisms, fairness and bias, and data/cybersecurity.