Data science and AI in FinTech: An overview
Longbing Cao, Qiang Yang, Philip S. Yu. International Journal of Data Science and Analytics, 2021. Access the paper at JDSA, arXiv or SSRN, 1-19, 2021.
Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas. Smart FinTech is the new-generation FinTech, largely inspired and empowered by data science and new-generation AI and (DSAI) techniques. Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and systems. The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and blockchain, and the DSAI techniques including complex system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep learning, federated learning, privacy-preserving processing, augmentation, optimization, and system intelligence enhancement. Here, we present a highly dense research overview of smart financial businesses and their challenges, the smart FinTech ecosystem, the DSAI techniques to enable smart FinTech, and some research directions of smart FinTech futures to the DSAI communities.
High-dimensional Cross-market Dependence Modeling and Portfolio Forecasting by Copula Variational LSTM
Jia Xu and Longbing Cao. SSRN, 1-44, 2021.
In the increasingly connected world, many systems are more or less coupled with each other in various ways. A typical example is the cross-market portfolio management, where the products of heterogeneous markets are selected and configured for investment. In such cross-market problems, one market is coupled with and influenced by others, and the financial variables of a market are coupled over time. This work makes the first attempt to model both the observations-based and latent dependence degrees and structures of highdimensional financial variables in cross-market portfolios by integrating variational recurrent neural networks. It integrates the distribution-based sequential modeling of multivariate time series and the regular vine copula-based dependence structures for modeling variable dependencies. Our method addresses the needs and gaps of modeling non-normal and long-range distributional interactions across market variables. We verify the model in terms of both technical significance and portfolio investment performance against benchmarks including linear models, stochastic volatility models, deep neural networks, and variational recurrent networks for portfolio forecasting.
AI in Finance: Challenges, Techniques and Opportunities
Longbing Cao. Access the paper at arXiv or SSRN, submitted to ACM Computing Surveys, 1-40, 2021.
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.
AI in Finance: A Review
Longbing Cao. Access the paper at SSRN, 1-36, 2020.
The recent booming of AI in FinTech further highlights the decades of significant developments and potentials
of AI for making smart economy, finance and society. AI-empowered finance and economy has been a sexy
and increasingly critical area in AI, data science, economics, finance, and other relevant research disciplines
and business domains. This long history of AI in finance has been further enhanced by the new-generation AI,
data science and machine learning, which are fundamentally and seamlessly transforming the vision, missions,
objectives, paradigms, theories, approaches, tools and social aspects of economics and finance and driving
smart FinTech. AI is empowering more personalized and advanced and better, safer and newer mainstream and
alternative economic-financial mechanisms, products, models, services, systems, and applications. This review
summarizes the lasting research on AI in finance and focuses on creating a comprehensive, multidimensional
and economic-financial problems-driven research landscape of the roles and research directions of both classic
and modern AI in finance.
AI in FinTech: A Research Agenda
Longbing Cao. Access the paper at arXiv, 1-10, 2020.
