Data science and AI in FinTech: an overview

Cao, Longbing; Yang, Qiang; Yu, Philip S.

doi:10.1007/s41060-021-00278-w

Cited by 88 publications

(44 citation statements)

References 47 publications

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“…Cao et al, 17 introduced the concept of smart currencies, smart cryptos, and smart blockchain in the context of smart FinTech enabled by data science and AI. Various opportunities are illustrated to enable smart blockchain, such as in pricing, trading strategy, portfolio management, bitcoin mining, risk analytics, fragility detection, governance, and regulation.…”

Section: Smart Blockchainmentioning

confidence: 99%

Decentralized AI: Edge Intelligence and Smart Blockchain, Metaverse, Web3, and DeSci

Cao

2022

IEEE Intell. Syst.

Self Cite

152

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Centralization has dominated classic scientific, social, and economic developments. Decentralization has also received increasing attention in management, decision, governance, and economics, despite its incomparability in AI. Going beyond centralized and distributed AI, this article reviews and delineates the conceptual map, research issues, and technical opportunities of decentralized AI and edge intelligence. The complementarity and metasynthesis between centralized and decentralized AI are also elaborated. We further assess where decentralized AI and edge intelligence can enable and promote smart blockchain, Web3, metaverse and decentralized science disciplinarily, technically, practically, and more broadly. Decentralization 1 complements and enhances centralization for systematic, all-round, and multifold objectives, functionality, and consequences. The paradigm shift from centralization to distribution has substantially mitigated methodology, knowledge, and capability gaps in social science, management science, decision science, economics, computing, complex systems, and AI. On the other hand, opensource and science have made paramount complementary achievements and contributions, and blockchain is transforming and inspiring finance to decentralized finance (DeFi), and the World Wide Web to Web3. In contrast, decentralizing AI is still an open area. By reviewing decentralized movements, systems, and technologies, including the recent movements on the blockchain, Web3, and decentralized science (DeSci), we paint a research picture of decentralized AI (DeAI), the research issues in edge intelligence, and the tasks of synthesizing centralized AI (CeAI) and DeAI. These further envisage and inspire the opportunities of DeAI-enabled smart blockchain, Web3, metaverse, and DeSci. DECENTRALIZATIONDecentralization is not a buzzword, hype or myth, although such concerns have been widely raised on decentralization-oriented initiatives, such as Web3 and the metaverse. Decentralization is also not a new or mysterious concept. 1 It has a 200-year history and is grounded in political science (such as subsidiarity, democracy, liberty, equality, and the decentralist movement), management and decision science (e.g., systems theory, self-organization, and self-determination), and economics (e.g., decentralized free markets and fiscal decentralization). Decentralization (with forms, such as devolution, deconcentration, delegation, etc.) is complementary or an alternative to centralization (and concentration), such as decentralized organizations, infrastructures, administration, operations, and services, corresponding to their centralized counterparts. In contrast to centralizing resourcing, manufacturing, and supply, globalization partitions, distributes, and decentralizes industrial, manufacturing, and supply chains to usually isolated and local economies and productivity. Open society, open government, and open science further promote the decentralization of administration, governance, governmentality, and scientific activiti...

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Section: Smart Blockchainmentioning

confidence: 99%

Decentralized AI: Edge Intelligence and Smart Blockchain, Metaverse, Web3, and DeSci

Cao

2022

IEEE Intell. Syst.

Self Cite

152

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“…The five major ethics principles for AI —beneficence, non‐maleficence, autonomy, justice, and explicability (Floridi & Cowls, 2019)—were addressed by 13% of our Scopus publications extract. Given the importance of cybersecurity, privacy, and preventing financial crime and money laundering (Cao et al, 2021), it is not surprising that 10% of publications relate to non‐maleficence and just 3% to the four other ethical principles. However, the reliance of machine and deep learning on a combination of historical input/output data instead of human experience has caused an ethics debate about those other principles (Burt, 2019, 2020; Ashta & Herrmann, 2021; Floridi et al, 2020; Klein, 2020; Shevlin, 2021).…”

Section: Into the Futurementioning

confidence: 99%

“…4–5). An ecosystem evolved for fintech players, who leverage AI towards cryptocurrencies and asset management (WealthTech), consumer banking for challenger banks (BankTech), lending (LendTech), payments (PayTech), smart contracts and trading (TradeTech), risk management (RiskTech), data security, cost reduction and operational efficiencies, smart marketing, customer experience (e.g., chatbots), insurance (InsurTech), accounting (AccountingTech), and “RegTech,” which is concerned with using algorithmic AI for regulatory monitoring, reporting, and compliance (Cao, 2020; Cao et al, 2021; Chao et al, 2019; Chinnasamy et al, 2021; Littman et al, 2021; Milana & Ashta, 2021; Milian et al, 2019; Nicoletti, 2017, pp. 31–79; Ryll et al, 2020; Sangwan et al, 2019; Sowmiyaa et al, 2020; Tepe et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review

Herrmann

Masawi

2022

Strategic Change

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The banking, financial services, and insurance (BFSI) sector is one of the earliest and most prominent adopters of artificial intelligence (AI). However, academic research substantially lags behind the adoption of AI in practice. At the beginning of this century, AI research has been centered on the sector's credit risk. In the 2010s decade, expert systems were increasingly replaced by data‐driven, “algorithmic” AI. Big data enjoyed much hype in that decade, which diminished later mostly due to unsuccessful implementations. Much published research on big data actually relates to machine and deep learning but not to big data per se. These terms are often found to be conflated in research and practice. The insurance sector is substantially underrepresented in published AI research, and current research is dominated by banking and investments. Governance frameworks for “responsible AI” (RAI) are yet to be incorporated into practice by fintech companies as well as incumbent organizations. RAI is a particular issue for decentralized finance (DeFi). The most successful implementations of AI in BFSI practice, as well as dominant academic research areas, are in investments, securities, market making, customer relationships, lending, risk management, and compliance.

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“…Cao et al designed a hybrid learning algorithm based on multi-intelligent body theory and social networks to analyze the knowledge sharing of virtual community users with the help of NetLogo 5.0 [ 9 ]. Pananond et al propose the demand for personalized services in information-sharing virtual spaces and the demand knowledge base construction based on multiagent technology and discuss the characteristics and implementation scheme of agent technology [ 10 ].…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing

Tian

Wang

2022

Computational Intelligence and Neuroscience

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In this paper, a neural network algorithm is used to conduct in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce. Both the expected returns and the social network structure are analyzed, and both have positive effects on knowledge sharing in the actual development process, but the degree of them also possesses certain variability. A model of the factors influencing the quality of knowledge shared by users is constructed to explore the relationship between the dimensions in social capital and how they affect the community users’ perceptions of knowledge quality. Exploring the strong influencing factors of product repurchase rate has key implications for promoting product sales and sales forecasting. The scale of this paper has undergone several minor revisions, and the content validity is very good. Criterion validity is generally reflected by Person correlation coefficient, and construct validity includes exploratory factor analysis and confirmatory factor analysis. First, the values were clustered, and the optimal variables were selected using stepwise regression and fitted with Poisson regression models to explore the relationship between the repurchase rate of different products and the factors that strongly influence the repurchase rate in the case. To predict the sales of goods, the advantages of the BP neural network, LSTM neural network, and Verhulst gray model are combined: BP neural network can predict sales by combining the current data corresponding to independent variables, LSTM neural network can explore the influence of historical data on sales, and Verhulst model can predict sales based on the growth trend of variables. A BP-LSTM-Verhulst nested neural network based on the AP algorithm is constructed to predict sales volume, and the accuracy of this method is proved by example. Finally, it is found that the proposed sales prediction method has higher accuracy than exponential regression and shallow neural networks. The deep learning prediction method combining unstructured data such as images proposed in the paper not only provides a more accurate sales prediction method for short life cycle products in e-commerce but also provides an effective deep learning method for management practices. The KMO value obtained after the test is less than 0.5, which is not suitable for factor analysis. Using SPSS22.0 to expand the KMO value and Bartlett’s sphericity test, the KMO value is significantly greater than the minimum requirement of 0.5, the KMO value of self-efficacy is 0.826, and the KMO value of expected return is 0.870.

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Data science and AI in FinTech: an overview

Cited by 88 publications

References 47 publications

Decentralized AI: Edge Intelligence and Smart Blockchain, Metaverse, Web3, and DeSci

Decentralized AI: Edge Intelligence and Smart Blockchain, Metaverse, Web3, and DeSci

Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review

A Dynamic Prediction Neural Network Model of Cross-Border e-Commerce Sales for Virtual Community Knowledge Sharing

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