In a time when insider trading continues to undermine the integrity and fairness of financial markets India and across the globe, researchers from the North Cap University, Gurugram, Haryana, published a paper titled, ‘Harnessing Artificial Intelligence For Enhanced Insider Trading Detection In India: Challenges And Regulatory Imperatives’. The study critically examines the potential of Artificial Intelligence (AI) to revolutionise insider trading detection within the Indian securities market. Traditional methods rely heavily on manual analysis and subjective judgment and often fall short in identifying sophisticated and covert trading patterns. 

AI technologies, particularly machine learning and blockchain, offer robust solutions through real-time monitoring, complex pattern recognition, and enhanced transparency. However, integrating AI into regulatory frameworks poses significant challenges, including data privacy concerns, algorithmic bias, and the need for comprehensive regulatory guidelines. 

India’s financial markets have experienced rapid growth and transformation, marked by increased participation from domestic and international investors. The rise of digital trading platforms, the proliferation of financial instruments, and the integration of global financial markets have contributed to this evolution.

However, with these advancements come heightened risks and challenges, including the sophisticated means by which insider trading can be conducted and concealed. Insider trading in India is not a new phenomenon. Historical instances have shown how individuals with access to privileged information have exploited it for personal gain, often at the expense of ordinary investors. 

Promise of AI

Historically, combating insider trading has been a formidable challenge for regulators. Traditional methods primarily involve manual analysis of trading data, whistleblower tips, and investigations based on market surveillance. While important, these approaches have significant limitations, such as the manual analysis of training data. Furthermore, traditional methods often rely on subjective interpretations of trading behaviour. 

In light of these challenges, there is a growing recognition of the need for more advanced, efficient, and accurate methods to detect and prevent insider trading. This is where Artificial Intelligence (AI) and technology come into play. With its ability to process and analyse vast amounts of data in real-time, AI offers a transformative approach to market surveillance and regulation.

Need for a regulatory framework

According to the study, the integration of AI and technology in surveillance necessitates a robust regulatory framework. Regulators must establish clear guidelines for using AI in financial markets, addressing issues such as data privacy, algorithmic fairness, and the explainability of AI systems. Collaboration between regulators, technology providers, and market participants is essential to develop and implement effective AI-based solutions.

In India, the Securities and Exchange Board of India (SEBI) has made significant strides in regulating insider trading through the Prohibition of Insider Trading Regulations (PIT) established in 2015. However, the rapidly evolving technological landscape calls for continuous adaptation of these regulations. Ensuring that the regulatory framework keeps pace with technological advancements is crucial for maintaining market integrity and protecting investors.

Impact of AI

The study recognises the role of AI in providing a glimmer of hope in revolutionising insider trading detection in India. At the forefront of this revolution is machine learning, a subset of AI that empowers algorithms to learn from data without explicit programming. Machine learning offers several distinct advantages over traditional methods. Its ability to process and analyse data in real-time enables regulators to stay ahead of the curve, detecting insider trading as it unfolds rather than after the fact.

One of the primary applications of machine learning in insider trading detection is anomaly detection. By establishing baselines of normal trading behaviour for specific stocks, sectors, or individual investors, machine learning algorithms can detect deviations indicative of insider trading. Pattern recognition is another powerful tool in the arsenal of AI for insider trading detection. Machine learning algorithms excel at identifying complex patterns within trading data that may be imperceptible to human analysts. 

In addition to anomaly detection and pattern recognition, machine learning can be leveraged for network analysis and relationship mapping. By analysing the network of relationships between investors, companies, and other market participants, AI algorithms can uncover potential connections between insiders and individuals profiting from leaked information. 

Source:

Image: Unsplash

Full Study