Legality of usage of Artificial Intelligence and Machine Learnings by Share Market Intermediary

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly utilized in share market services due to significant efficiencies and benefits for companies and investors across the globe. This has resulted in an alteration in the firm’s business models and has a potential impact on the effectiveness of the share market and could harm investors. Indian share market is also witnessing the usage of this technology by market intermediaries. The present regulatory framework of Securities Exchange Board of India (SEBI) on share market intermediaries is not dealing with the Fintech/ technology 2.0-based products and services offered in retail trading and investment advisor platforms in India. The research is primarily based on the normative method presenting a qualitative analysis of the usage of AI & ML in various business models by share market intermediaries. How various share market regulators are addressing and regulating this technology usage and their judicial exposition. The paper concludes that the Indian share market is no exception & SEBI require to look at this new transformation and address the challenges posed by it. SEBI needs to take a proactive step to promote, guide & regulate usages of AI & ML which is gradually seeking the attention of Indian share market intermediaries into their business models and get the maximum benefit out of these technologies. JEL

La legalidad del uso de la inteligencia artificial y el aprendizaje automático en el mercado de valores Resumen La inteligencia artificial (IA) y el aprendizaje automático (AA) se utilizan cada vez más en los servicios del mercado de valores debido a las eficiencias y beneficios significativos que suponen para las empresas y los inversores en todo el mundo. Ello ha provocado una alteración en los modelos de negocio de las empresas y podría influir en la efectividad del mercado de valores y perjudicar a los inversores. El mercado de valores indio también está asistiendo al uso de esta tecnología por parte de intermediarios del mercado. El marco regulatorio actual de la SEBI sobre los intermediarios del mercado de valores no aborda los productos y servicios basados en Fintech/tecnología 2.0 ofrecidos en plataformas de comercio minorista y asesoramiento de inversiones en la India. Este estudio sigue principalmente el método normativo para presentar un análisis cualitativo del uso de la IA y el AA en varios modelos de negocio de intermediarios del mercado de valores. Analiza la forma en que los diversos reguladores del mercado de valores abordan y regulan el uso de esta tecnología y su exposición judicial. El artículo concluye que el mercado de valores indio no es una excepción y que la SEBI debe considerar esta nueva transformación y abordar los desafíos que plantea. La SEBI debe tomar medidas proactivas para impulsar, guiar y regular el uso de la IA y el AA, que está captando gradualmente la atención de los intermediarios del mercado de valores de la India para sus modelos de negocio, y sacar el máximo partido de estas tecnologías.

Introduction
Technology 2.0 in the financial market across global is extensively used by financial intermediaries in their services and products on offers to clients and customers. This technology is also famously known as Fintech in the financial and service sector industry as it is having the potential to transform the concept of financial services. There are broadly eight categories such as payments, insurance, planning, lending, crowdfunding, blockchain, trading, and investments, data & analytics, and security having usage and impact of innovative fintech business models. The following table is an overview of these categories and products/ services on offer by companies.

Legal material and methods
The article strives to highlight that technology 2.0/ fintech is evolving in innovative ways.
It is transforming the traditional approaches and practices involved in products and services offered by various share market intermediates in the share market to the investors and clients overall. No share market or regulator is left unaffected by these technological developments.
This has raised very significant legal questions on overall governance, liability, risk-mitigating factors, investor protections, market resilience, streamlining and accommodating innovative

Result and discussion
Online trading and investment platforms have evolved significantly over the past four decades pressuring traditional ways of providing customers access to products and services through multiple channels of distribution. This pressure is resulting in an environment of cost competitiveness pushing companies for more robust use of automated technology, processes, and expansion of products and services. Let us understand the types of mainstreaming online trading and distribution platforms. Basically, there are three types of platforms i.e., online trading, online assets management, and exchange distribution platforms. In the first type of platform customers are enable to access and manage their accounts and related information, do research using online tools provided by the firm, and investment decisions in a broad range of products like exchange trade, mutual funds, and over-the-counter securities, placing of orders, professional advisor connections on request, etc. In the second type of platform assets management companies (AMC) are offering their funds, third-party funds to customers along with access to manage their information, research through online tools, and professional advice. The third type of platform is used by stock exchanges where various fund products of different companies are distributed with fund subscription and redemption in primary or secondary trading or in a combination of both. Some exchanges provide information to customers for making their own assessment prior to investment in those funds.

Supported by Fintech
Innovative technology usage in the platforms related to retail trading and investment has accelerated by customer demand which is changing nowadays. As Customers are increasingly becoming techno savvy with the development of technology. This is resulting in a changing online usage behavior pushing share market firms to adopt and increase usage

Challenges and Risk Associated with automated products and services based on AI & ML
Risk is analyzed in the context of investor protections and minimum standards required to be followed by share market intermediaries in offering products and services on their platforms. Below are the risks associated with different platforms.
Most of the brokers and sub-broker, dealers, and investment advisors are required to get permission from regulators to share market-related activities. This permission is given in terms of licensing, fees charges, and commissions for transactions in securities by clients/ investors. Issues arise when platforms are offering cross-border services on offer to foreign clients/ investors as they required a license and without this requirement, it may lead to violations in overseas markets and clients thereof. Sometimes the automated platform algorithms are programmed in a manner to direct clients to a preferred specific range of securities and investment options or other intermediaries' platforms. In doing so they get more commissions, fees, or other types of compensation. This may lead to a conflict of interest and a lack of transparency in terms of cost and fees. Sometimes license is issued to platforms for the execution of securities transactions only and there is a different requirement of licensing for investment advice platforms. Execution of securities weather, systematic investment plans to the clients. Sometimes this automated platform does offer professional referrals but still, the risk is the same as they lack proper client history and give appropriate advice. Since the products and services offered on the platform are based on AI & ML, clients may not be able to know in detail the scope, associated risks, and limits of such services and products offered. AI and ML algorithms are based on the automated environment they may be lacking appropriate data, high-quality decision charts, loops related to the feedback, and questions that are controlled to have an automated process. There is a chance that the qualitative decision-making process outcome for investors is not convenient/ beneficial due to the velocity of the algorithms being programmed. Sometimes robot-based investor advisor platforms have a risk of errors in the algorithms program itself. As it contains the client data which is observed which is processed by algorithms to give output through various financial advice to clients. There is a risk that algorithms can give results that are unintended due to design faults, mistakes in software programs that are not aligned with the methodology involved in algorithms, and firms' predefined approaches. This will lead to the non-systematic and mismatched sales of investment products/services which are not in the interest of investors (COOK, 2016) or different advice to the identical profiles of various clients (SCHACHT, 2015). This leads to the risk associated with errors with the complexity of algorithms that generate different advice based on client-specific profiles which is not easy to understand by investors. If the algorithms are too simplistic then also it will have errors in generating the services or products in the form of a plan that is managing clients' accounts. Therefore, algorithms must be robust to capture appropriate data consisting of the client's various information such as overall financial situations/ constraints, income flow, tax implications, expenditure patterns, other income sources, etc. This information is processed by algorithms through questioners and included in a plan i.e. generic investment strategy as a response to the client. These plans are sometimes not a better-suited strategy due to a predetermined set of alternatives used by algorithms to respond to specific clients' needs. If the algorithms do not gather sufficient client data over a period with different frequencies, then information becomes static and the advice given by the algorithm will be ill-equipped which may not be suitable to the

Objective Of the Regulation:
Share market participants specially registered intermediaries and Asset Management Companies who are engaged in the adoption and usage of AI & ML-based trading portals and offering services based on algorithms are governed under the said regulation.

Rationality Of Scope and Application of The Regulation:
While considering the proposed regulation emphasis is on the rationality of scope and application which is a very important component of the present regulation. There must be a fine balance between risk emerging out of AL & ML vis-à-vis key safeguards that are required to be put in place by market intermediatory. The focus must be on the activity carried out based on algorithms, the potential risk emerging out of it, and the potential effect of such technology on clients and the overall integrity of the share market. Based on the market atmosphere if there is a need, said regulations can be applied in a phased manner. The scope of the said regulation will be applied based on activities carried out by firms, technology's substantial impact on participants in markets along with clients, and using AL & ML-based service tools. Therefore, the size of firms is immaterial in terms of big or small

Senior Management Responsibility and Overall Governance in The Firm
This regulatory clause is one of the most important clauses as it takes care of the overall accountability of the firms while using AI & ML-based technology in share market trading platforms. The senior management (SM) is a key decision maker and player having control over the overall functioning of the firm. Sometimes SM of a firm if not having appropriate knowledge, then they can take help from another senior person and designate him for the support within the firm. As AI & ML-related technology is at its nascent stage and helps the firm's top management to understand its usage, deployment, testing, and monitoring of algorithms, its intended output, and overall implications. Thus, if something goes wrong then Senior management will be accountable for it and responsible for overall supervisory functions of AI &ML including third-party outsourcing models. This supervisory responsibility includes clear-cut policies covering approved procedures for developing, deployments, periodical updates of trading algorithms, solving identified major problems in the process of monitoring such algorithms, and accountability of staff involved including third-party outsourcing models. All the above policies must be clearly documented with the compliance reports of AI & ML technology in line with the compliance of risk management and existing legal framework. These policies must contain the understanding of utilization and predefined outcomes in deploying AI & ML technology, appropriate implementation of controls (sericite breakers) and governance to oversee the challenges pose by outcomes of such technology; the detailed methodology to be adopted in compliance report of the use of such AI & ML of its complete life cycle must be periodically audited across its business; assessment of the application of technology is within the ethical manners of firms appetite risk management with the client tolerance risk.

Market Conditions & Regulatory Compliances.
Due to the underlying complexity and systematic risks associated with AL & ML technology, testing should be conducted independently from the live market environment, and if any material changes are detected the system must trigger further in-depth testing.
This testing must ensure that AI & ML is responding as expected in un/stressed market conditions, functions & deployment of the kill switch, and simultaneous operation is fulfilling the obligation imposed by regulation/tor. After deployment of the technology, real-time monitoring of performance and output must be observed and if the situation demand, the kill switch is automatically triggered with backup solutions. It must be kept in mind that this technology is assessed and tested as per the risk associated with it, the market is not being abused, and the privacy of the dataset and cyber threat/ security and working as intended.
The post-development outcome also needs to be continuously monitored. There must be other forms of tests and oversight arrangements in a standby mode to control the behavior of algorithms. Because when an Algorithm start processing more data, it may change its behavior in an unforeseen manner. In such a scenario other techniques are used in traditional algorithms that shall be used to continuously monitor to ensure that the AI & ML algorithms get adjustment and transformation.

Skills, Expertise, and Experience in Staff Compliances Policies and Leaving the Firm
The importance of this policy is to highlight that firms may be lacking adequate skills, expertise, and experience with internal staff in maintaining and oversight of AI & ML. This may further aggregate the situation resulting in difficulties in algorithms models updating and sometime dependability on third parties. Therefore, to overcome such difficulties it is essential that in-house staff must possess the requisites of the aforementioned knowledge, expertise, and experience for supervisory risk management and compliance with legal regulatory parameters. It is advised that firms must constitute multi-disciplinary teams of IT/ database administrators, risk & compliance management, data scientists, legal personnel, etc. to investigate the above-mentioned matters. There must a written documentation, backup copy for continuity of models, and defined processes in case staff involved in the above process has left the job in the firm for smooth functioning of AI & ML technologies. Also, while doing the due diligence on compliance with the model supplied by third-party providers.

Relationship of the Firm with Third Party Service Providers & Governance
Sometimes firm itself will not have the capability to develop and test AI & ML. In such a scenario firms will take the help of third-party service providers or simply use the technology developed by them. In such a scenario it becomes important for firms to understand operation resilience and manage their relationship with service providers.

Fairness in How Much Disclosure is Required to be Made by Firms
There can be two types of disclosures i.e. at the level of clients and customers and at the level of regulators what kind of oversight information/ governance of firms are having while using AI & ML. customers and clients must get meaningful information in a comprehensible language so investors will be able to know the nature of AI & ML technologybased algorithms, their impact & outcome, products characteristics & services on offer. All these things can be in detailed objective disclosures made by firms to the clients and customers. So, it provides transparency and an opportunity to evaluate benefits and associated risks with such a technology involved in selling products so that informed decisions are taken by clients.

Relied AI & ML Applications
AI & ML-based algorithms must get bias-free and qualitative data for the appropriate performance of algorithms. Otherwise, it will jeopardize firms and customers while using it because of the risk associated with it in and it may result in inadequate and discriminatory advice to the investors. Firms should ensure that as per the objectives sets in algorithms, and non-discriminatory of sex, age, and background of investors, etc. Analysis of algorithms output and discriminatory risk must be ensured by firms in the dataset collection. The firms must develop a proper process at the place to identify, control and remove any biases from data if it is still present to restrict any potential harm to investors. Therefore, it is recommended that there must be continuous training be given to staff and dataset scientists involved in raising awareness amongst themselves.

Regulators
It is borne in mind that this regulation should be conducive to the conduct of the market and must be good and competitive for the usage of AI & ML. The above-mentioned approach of the firm and regulator will not serve the basic purpose of the effectiveness of the said regulations. A key element of the effectiveness lies in ethics which includes due diligence, care, respect towards others, fairness, and honesty on which the whole foundation of regulation relies to build upon. In the firms, it is the top and senior management who manages the cultural behaviors and driving forces to create and practice such ethics. This helps firms with the appropriate behaviors of staff to reduce potential harm to investors, minimize risks, design models of algorithms, be ready to face challenges, accountable for controlling the environment of the said ethics code. Against this backdrop, the regulator's role also becomes important to promote and bring more transparency in culture with appropriate disclosure to all stakeholders and assist firms to follow robust adaptive cultures within themselves and in the share market overall.

AI & ML Operational Resilience
AI & ML operations may have a wide impact on investors, market intermediaries, the integrity of the Share market, and the firm's viability overall. The widespread usage of AI & ML will also have the resilience of operation effects of interconnectedness forms which are not earlier expected. This may also impact on the financial stability of markets and firms. As third-party service providers are providing ever-green technological solutions based on the cloud which are cheaper & more secure compared to in-house developed algorithms technology. Thus, regulators through firms must have a separate mechanism of oversight Passagens. Revista Internacional de História Política e Cultura Jurídica Rio de Janeiro: vol. 15, n o 2, maio-agosto, 2023, p. 319-339. 337 and due diligence over third-party AI & ML services to control and mitigate any problem posed by their technology.

Conclusion and suggestions
As globally technology 2.0 is making a substantial impact and opening new avenues so is the case of the Indian share market and intermediaries will be none to second to leverage such benefits. These new avenues are simultaneously bringing potential harms and associated risks to investors and the share market. SEBI must be vigilant on how this technology is being used, its benefits, and its drawbacks while allowing usage by intermediaries in the share market. Sooner or later AI and ML-based technology is going to transform the present scenario and business models used by intermediaries. Therefore, it is recommended that SEBI must identify recent developments and examine the best practices and guide properly the industry intermediaries about the development, testing, and risk associated with the deployment of AI & ML in a regulated manner. The proposed regulation is focusing on the oversight, control, and governance of algorithms, monitoring, and testing, bias less, and qualitative datasets with proper circuit breaks and liability of top management and people involved in the process. The algorithm's process and logic must be transparent and able to explain in detail, the use of outsourcing services, expectations and liability of outsourcing firms, and overall ethical concerns. The proposed regulation is covering only AI & ML-based retail trading and investment advice-related products and services only. Other areas of such technology 2.0 used by institutional trading platforms, alternate financing platforms, and distributed ledger technology are not being regulated under this regulation.
But this should not be a problem. As there is a further scope of expansion of the proposed regulation as and when it is required to cover such technologies usage by these participants of the share market.