Published Paper Details:

The evolutionary progression of artificial intelligence (AI) in the global financial domain depicts a trajectory of transformative advancements accompanied by noteworthy discoveries and discernible implications. Specifically, the Asia Pacific region emerges as a pivotal hub for the burgeoning growth of AI, propelled by substantial investments and widespread adoption across diverse industries encompassing automotive, healthcare, retail, and food and beverages. Forecasts signal an exceptional surge in the worldwide AI market, catapulting from a valuation of USD 454.12 billion in 2022 to an astounding USD 2,575.16 billion by 2032, demonstrating a commendable compound annual growth rate (CAGR) of 19% spanning from 2023 to 2032. However, this upward force is coupled with significant economic considerations. The assimilation of AI initiates a shift in occupational roles, necessitating the augmentation of skill sets within the workforce while potentially precipitating job displacement. Comprehensive surveys conducted by esteemed entities, such as the World Economic Forum, indicate the potential replacement of 9% of financial services jobs with AI by 2030, concurrently envisaging a robust 19% workforce expansion in FinTech enterprises. This transition is expected to impact sectors like investment management, predicting a prospective reduction of 10% in employment numbers within five years and a substantial 24% decline over a decade. Concerns regarding algorithmic biases, transparency, and ethical implications necessitate meticulous navigation. Concurrently, a diverse array of global financial institutions showcase multifaceted applications of AI. From J.P. Morgan Chase and Goldman Sachs employing AI for fraud detection and risk assessment to forward-thinking entities like Trading Technologies and Sigmoidal leveraging AI for market analysis and asset allocation, respectively, these instances underscore AI's extensive utility in revolutionising conventional financial practices. However, the rapid proliferation of AI in finance also presents inherent risks. Limitations in programming codes pose a challenge in capturing the intricate dynamics of financial markets, potentially leading to inaccuracies. Furthermore, the finance sector confronts amplified virtual threats and data biases, necessitating stringent protocols against cybersecurity threats and biases perpetuated by AI's data interpretation. The intricate interplay between AI's transformative potential and associated challenges underscores the imperative need for a nuanced approach to harnessing AI's prowess within the financial landscape.