Stock-market Trading

AI-Powered Trading Opportunities

Head of Applied Machine Learning and Platform at, Shomron Jacob, looks into the stock market's future that will be intertwined with the advancements of AI, ushering in a new age of informed, efficient, and dynamic trading.

The financial world is in a continuous transformation state with the integration of Artificial Intelligence (AI) into trading practices, promising not only to change the landscape but also to make trading more accessible to a wider range of practitioners. Chief Financial Officers (CFOs) are now at the forefront of exploring the potential of AI, particularly in capital liquidity and adequacy planning stages. However, along with these unprecedented opportunities and changes, they grapple with unique challenges.

AI's prowess in processing and analysing vast amounts of data in real-time stands out as a game-changer in decision-making. The global big data market, expected to reach $103 billion by 2027, emphasises the growing significance of data analytics in financial decision-making.

The automation capabilities of AI, particularly in algorithmic trading platforms, offer a paradigm shift by eliminating manual intervention. This not only enhances efficiency but also opens-up trading to individuals, with varying levels of market knowledge.

Furthermore, AI's predictive capabilities in risk management offer sophisticated strategies, with a survey by Deloitte revealing that 83% of financial institutions reported improvements in risk management after implementing AI. The efficiency gains from AI-driven automation translate into tangible cost savings, as estimated by Juniper Research, which predicts that financial services will save $1.2 billion in 2022, due to the adoption of chatbots and virtual assistants.

However, with these opportunities come challenges that CFOs must grapple with in their pursuit of integrating AI into financial practices. One critical concern is the quality of data, as the accuracy and reliability of AI models hinge on the quality of the training data. Gartner's estimation that poor-quality data costs organisations $15 million annually, underscores the importance of ensuring data integrity.

Continuous refinement of AI models is imperative for their relevance, as highlighted by PwC's survey, indicating that 84% of CEOs express concerns about the quality of AI models, emphasising the need for ongoing resources dedicated to model refinement. Navigating regulatory compliance poses a challenge as regulatory bodies scrutinise the usage of AI in trading, with Accenture's report highlighting that 78% of financial services executives view navigating regulatory requirements as a top challenge in AI adoption.

Moreover, the demand for specialised skills in AI implementation poses a talent acquisition challenge for CFOs. LinkedIn reports a 32% increase in AI-related job postings in 2021, emphasising the competitive landscape for AI talent.

Shifting focus to Generative AI, a subset focused on creating new content, this promises significant applications in finance. Supported by compelling statistics, these applications include financial forecasting and predictive analytics, correlated with an average revenue increase of 32%, according to an IBM study. The natural language processing market in finance is projected to reach $1.3 billion by 2025, as per a report by MarketsandMarkets.

Shomron Jacob

Head of Applied Machine Learning and Platform,

Similarly, investment decision support leveraging AI is embraced by 56% of asset management firms, as found in a survey by EY. The global fraud detection and prevention market, incorporating AI-driven solutions, is expected to grow to $65.8 billion by 2025, according to Grand View Research. The RegTech market, encompassing AI solutions for regulatory compliance, is projected to reach $55.28 billion by 2025, as reported by MarketsandMarkets.

Cash flow management, a top priority for 50% of CFOs, benefits significantly from AI, as reported by Deloitte. A survey by McKinsey reveals that 61% of finance executives expect to increase spending on digital and analytics capabilities, indicating a growing emphasis on cost optimisation through technology. The document AI market is anticipated to reach $3.7 billion by 2025, driven by the adoption of AI in document review and analysis, according to MarketsandMarkets.

As CFOs navigate these diverse applications and statistics, the allocation of portfolios in AI trading becomes a critical decision-making process. Assessing the organisation's risk tolerance is fundamental, with a study by Aon finding that 42% of CFOs consider risk management as their top priority. Resource allocation, both financial and human, is critical for successful AI integration, with Deloitte recommending allocating at least 10-20% of the IT budget to AI initiatives for organisations planning adoption.

Robust mechanisms for monitoring the performance of AI algorithms are necessary for CFOs, ensuring adaptability to changing market conditions. Ethical considerations should not be overlooked, and CFOs must ensure that AI trading aligns with the organisation's values and ethical standards to maintain trust with stakeholders.

As AI continues to reshape the trading landscape, CFOs are tasked with harnessing its potential while navigating the associated challenges. The statistics underscore the growing influence of AI across various facets of finance, emphasising the need for informed decisions in an increasingly digital and data-driven financial ecosystem.

Charles Story

Director, Operations for Corporate Investigative Services, Rehmann

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