AI in Finance: Challenges, Techniques and Opportunities (2107.09051v1)

Abstract: AI in finance broadly refers to the applications of AI techniques in financial businesses. This area has been lasting for decades with both classic and modern AI techniques applied to increasingly broader areas of finance, economy and society. In contrast to either discussing the problems, aspects and opportunities of finance that have benefited from specific AI techniques and in particular some new-generation AI and data science (AIDS) areas or reviewing the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense roadmap of the overwhelming challenges, techniques and opportunities of AI research in finance over the past decades. The landscapes and challenges of financial businesses and data are firstly outlined, followed by a comprehensive categorization and a dense overview of the decades of AI research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. The comparison, criticism and discussion of classic vs. modern AI techniques for finance are followed. Lastly, open issues and opportunities address future AI-empowered finance and finance-motivated AI research.

• The paper provides a comprehensive taxonomy of AI techniques, from traditional statistical models to deep neural networks, that improve functions like risk management and portfolio optimization.
• The paper highlights the complexity of financial data, addressing challenges such as heterogeneity, scale, and dynamic market conditions that affect analytical outcomes.
• The paper discusses future opportunities for AI in finance, advocating for interdisciplinary approaches and advanced systems to adapt to evolving economic landscapes.

Overview of "AI in Finance: Challenges, Techniques and Opportunities"

The paper "AI in Finance: Challenges, Techniques and Opportunities" by Longbing Cao provides a comprehensive analysis of the intersection between AI and finance. It navigates through the historical and contemporary landscape of AI applications in financial markets, alongside the techniques developed over past decades. This work is notable for its structured and extensive exploration of the challenges inherent in integrating AI into financial systems, providing both a critical examination of existing methods and an eye towards future prospects within AI-empowered financial services.

The paper outlines the facilitatory role of AI in modernizing traditional financial sectors such as banking, trading, and insurance, by virtue of emerging technologies like smart FinTech. It delineates the integration of EcoFin disciplines—economics and finance—within AI's broad methodology, marking a transformation supported by data science advancements. The integration of these domains has stimulated innovations across various subfields, such as digital currencies and blockchain technology, with AI proving instrumental in driving smarter financial solutions.

Key Highlights

1. Economic-Financial Ecosystem and AI Techniques: The paper categorizes EcoFin businesses into multiple domains ranging from traditional banking to complex regulatory frameworks. It elucidates how various AI techniques—from classic statistical modeling to novel deep neural networks—are applied across these areas, aiding functions like risk management, anomaly detection, and portfolio optimization.
2. Data Challenges in Finance: A notable point of discussion is the challenges posed by financial data's complexity. Issues such as data heterogeneity, scale, and dynamic nature are accentuated, outlining the significant hurdles AI must overcome to deliver meaningful analyses and predictions in finance.
3. Classification of AI Techniques: The paper provides a valuable taxonomy of AI methodologies, dividing them broadly into six main families: mathematical and statistical modeling, complex system methods, classic learning methods, computational intelligence, modern analytics, and hybrid methods. Each is analyzed for its applications, strengths, and limitations in dealing with financial systems.
4. Emerging Trends and Future Directions: The review speculates on the future gelation between AI and finance, emphasizing the need for more advanced and specialized AI systems that can handle the inherent intricacies of financial data and market dynamics. Innovations in behavioral economics, strategic planning, and integrative approaches are identified as areas rich with potential for future breakthroughs.

Implications and Future Developments

The implications of AI advancement in finance are multidimensional. Practically, AI is expected to enhance decision-making processes, improve operational efficiencies, and provide robust predictive capabilities across various financial services. Theoretically, this integration can augment financial models' ability to incorporate vast datasets and dynamic variables, potentially redefining how financial systems are understood and managed.

Looking ahead, the paper suggests that the frontier of AI in finance lies in developing systems that can intelligently adapt to contextual changes in the economic landscape, including geopolitical influences and socio-economic shifts. The paper advocates for more interdisciplinary research that bridges the gap between technological innovation and economic theory to foster a genuinely intelligent financial ecosystem.

In closing, "AI in Finance: Challenges, Techniques and Opportunities" provides a dense yet insightful roadmap for researchers and practitioners aiming to navigate and contribute to the fast-evolving terrain of AI applications in the financial industry. It underscores both the achievements thus far and the pressing challenges that remain, setting a clear agenda for the continued exploration and innovation necessary to achieve smarter, more intuitive financial technologies in the future.