How SEBI Can Use Advanced Analytics to Curb Insider Trading

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There have been several reports of recent crackdowns by India's capital markets regulator, Securities and Exchange Board of India (SEBI) on companies and individuals that were found to be involved in insider trading, front running, and trading anomalies such as the fat finger error. This demonstrates how the regulator is looking at trading compliance as a serious area of focus. It is not known if these cases were the result of a whistle-blower complaint or were discovered as part of a proactive review.

Insider Trading incidents in the Indian market have had their fair share of media attention, mostly for all the wrong reasons. However, as is often the case when something like this happens, it also brought to light some very real issues that need to be addressed. The regulator has implemented several new rules with regard to insider trading. In simple terms, insider trading occurs when someone with a privileged position in a company (usually executives or board members) buys or sells shares in that company based on confidential information that can only have been obtained from someone else with access to that information. The problem is not just limited to insiders selling their shares ahead of poor news becoming public but also extends to friends or family of those in management positions who may be privy to confidential information and trade on it at an advantage.

Ambiguous Definition of “Insiders”

The people who are considered insiders of a company are typically the executives or board members of the company. However, there are also times when other people may have access to confidential information and can be considered insiders. For example, lawyers or accountants who work with a company may have access to confidential information and could potentially trade on it. In India, the SEBI has taken a very broad view of what constitutes insider trading. In fact, the current definition of an insider under the SEBI (Insider Trading) Regulations, 1992 is so broad that it includes any person who is related to the company or is presumed to have access to unpublished price-sensitive information in respect of securities.

This definition raises a few important questions. Firstly, what is meant by connection with the company? Secondly, what type of information is price-sensitive? And finally, how does the SEBI determine if a person has access to unpublished price-sensitive information?

The SEBI has not provided any specific guidance on what is meant by connection with the company. However, it is safe to assume that this would include people who are employed by the company, people who have been associated with the company in the past, and people who are presumed to have access to unpublished price-sensitive information.

Price-sensitive information is defined as any information that is not generally available and which, if made available, may affect the price of securities. This would include things like financial results, new products or services, mergers and acquisitions, and changes in company strategy.

The SEBI has said that it will presume that a person has access to unpublished price-sensitive information if that person is in a position to influence the decision-making of the company with regard to the disclosure of that information. This would include people such as senior management, board members, key employees, and external service providers.

Evolution of SEBI Using Analytics

The SEBI has put in place several rules and regulations to prevent insider trading. These include things like the SEBI (Prohibition of Insider Trading) Regulations, 2015, which lay down specific prohibitions on insider trading. The regulations also require companies to put in place a code of conduct for preventing insider trading. In addition, the SEBI has also set up a dedicated department to investigate cases of insider trading.

The SEBI has been very proactive in recent years in trying to curb insider trading. However, there are still several challenges that the regulator faces in this area. Firstly, it can be very difficult to prove that someone has traded on inside information as there is often no direct evidence to support this. Secondly, even if the SEBI can prove that someone has traded on inside information, it can be very difficult to prosecute them as the burden of proof is very high.

One way in which the SEBI can try to overcome these challenges is by using advanced analytics. Advanced analytics is a type of data analysis that uses sophisticated techniques, such as machine learning, to extract insights from data.

The SEBI can use advanced analytics to detect patterns in trading activity that may be indicative of insider trading. SEBI may look at conducting periodic look-back activity in collaboration with other agencies such as exchanges and other private and public entities to detect insider trading cases. One biggest loophole in the detection of insider trading is to establish individuals who can be potential violators and the trading accounts under which such illegal trades have happened. Most savvy violators will never use their own trading accounts for insider trading. Many also do not take delivery of shares to keep their Demat accounts clean but would take positions using Futures & Options.

The regulator needs to create a big-data framework that links individuals within the company, its external service providers who have access to price-sensitive information, and linking all relevant individuals with trading accounts of family members, extended family members and friends based on advanced link analysis. This link analysis will identify a network of trading accounts associated with one individual who may be managing these accounts directly or indirectly. The link-analysis will also use pattern matching techniques by using data points such as mobile numbers, landline numbers, addresses, email, IP addresses, bank account details, social media, geolocation and advanced techniques such as Artificial Neural Networks.

The framework will also have to consider the fact that some individuals may use multiple email accounts, mobile numbers, and bank accounts to avoid detection. The framework should be able to identify such individuals by using sophisticated techniques such as clustering and outlier detection.

The SEBI can also use advanced analytics to predict which stocks are likely to see unusual trading activity. This can be done by looking at historical data to identify stocks that have shown signs of insider trading in the past. The SEBI can then use this information to flag stocks that may be at risk of insider trading in the future.

The use of advanced analytics is just one way in which the SEBI can try to curb insider trading. However, it is important to note that this is not a silver bullet, and the SEBI will need to continue to use other means, such as surveillance and investigations, to detect and prosecute insider trading.

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