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Erschienen in: International Journal of Data Science and Analytics 2/2021

05.08.2021 | Editorial

Data science and AI in FinTech: an overview

verfasst von: Longbing Cao, Qiang Yang, Philip S. Yu

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 2/2021

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Abstract

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 artificial intelligence (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.

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Literatur
1.
Zurück zum Zitat Andersen, T.G., Davis, R.A., Kreiß, J.-P., Mikosch, T.V.: Handbook of Financial Time Series. Springer, Berlin (2009)MATH Andersen, T.G., Davis, R.A., Kreiß, J.-P., Mikosch, T.V.: Handbook of Financial Time Series. Springer, Berlin (2009)MATH
2.
Zurück zum Zitat Arslanian, H., Fischer, F.: The Future of Finance: The Impact of FinTech, AI, and Crypto on Financial Services. Palgrave Macmillan, London (2019)CrossRef Arslanian, H., Fischer, F.: The Future of Finance: The Impact of FinTech, AI, and Crypto on Financial Services. Palgrave Macmillan, London (2019)CrossRef
3.
Zurück zum Zitat Baddeley, M.: Behavioural Economics and Finance. Routledge, London (2013)CrossRef Baddeley, M.: Behavioural Economics and Finance. Routledge, London (2013)CrossRef
4.
Zurück zum Zitat Brahme, A., Bhadade, U.: Effect of various visual speech units on language identification using visual speech recognition. Int. J. Image Graph. 20(4), 1–27 (2020)CrossRef Brahme, A., Bhadade, U.: Effect of various visual speech units on language identification using visual speech recognition. Int. J. Image Graph. 20(4), 1–27 (2020)CrossRef
5.
Zurück zum Zitat Broemeling, L.: Bayesian Analysis of Time Series. Chapman and Hall/CRC, Boca Raton (2019)MATHCrossRef Broemeling, L.: Bayesian Analysis of Time Series. Chapman and Hall/CRC, Boca Raton (2019)MATHCrossRef
6.
Zurück zum Zitat Buchanan, B.G.: Artificial intelligence in finance. The Allen Turing Institute (2019) Buchanan, B.G.: Artificial intelligence in finance. The Allen Turing Institute (2019)
7.
Zurück zum Zitat Cao, L.: Data science: challenges and directions. Commun. ACM 60(8), 59–68 (2017)CrossRef Cao, L.: Data science: challenges and directions. Commun. ACM 60(8), 59–68 (2017)CrossRef
10.
Zurück zum Zitat Chatterjee, K., Samuelson, W.: Game Theory and Business Applications. Springer, Berlin (2001)MATHCrossRef Chatterjee, K., Samuelson, W.: Game Theory and Business Applications. Springer, Berlin (2001)MATHCrossRef
11.
Zurück zum Zitat Cornuéjols, G., Peña, J., Tütüncü, R.: Optimization Methods in Finance, 2nd edn. Cambridge University Press, Cambridge (2018)MATHCrossRef Cornuéjols, G., Peña, J., Tütüncü, R.: Optimization Methods in Finance, 2nd edn. Cambridge University Press, Cambridge (2018)MATHCrossRef
12.
Zurück zum Zitat Craja, P., Kim, A., Lessmann, S.: Deep learning for detecting financial statement fraud. Decis. Support Syst. 139, 113421 (2020)CrossRef Craja, P., Kim, A., Lessmann, S.: Deep learning for detecting financial statement fraud. Decis. Support Syst. 139, 113421 (2020)CrossRef
13.
Zurück zum Zitat Dhar, V., Stein, R.M.: Fintech platforms and strategy. Commun. ACM 60(10), 32–35 (2017)CrossRef Dhar, V., Stein, R.M.: Fintech platforms and strategy. Commun. ACM 60(10), 32–35 (2017)CrossRef
14.
Zurück zum Zitat Doloc, C.: Applications of Computational Intelligence in Data-Driven Trading. Wiley, Hoboken (2020) Doloc, C.: Applications of Computational Intelligence in Data-Driven Trading. Wiley, Hoboken (2020)
15.
Zurück zum Zitat Dunis, C.L., Middleton, P.W., Karathanasopolous, A., Theofilatos, K.: Artificial Intelligence in Financial Markets. Springer, Berlin (2019)MATH Dunis, C.L., Middleton, P.W., Karathanasopolous, A., Theofilatos, K.: Artificial Intelligence in Financial Markets. Springer, Berlin (2019)MATH
16.
Zurück zum Zitat Ehrentreich, N.: Agent-Based Modeling. Springer, Berlin (2008)MATH Ehrentreich, N.: Agent-Based Modeling. Springer, Berlin (2008)MATH
17.
Zurück zum Zitat Firdaus, M., Thakur, N., Ekbal, A.: MultiDM-GCN: aspect-guided response generation in multi-domain multi-modal dialogue system using graph convolution network. In: EMNLP’2020, pp. 2318–2328 (2020) Firdaus, M., Thakur, N., Ekbal, A.: MultiDM-GCN: aspect-guided response generation in multi-domain multi-modal dialogue system using graph convolution network. In: EMNLP’2020, pp. 2318–2328 (2020)
18.
Zurück zum Zitat Fischer, T.: News reaction in financial markets within a behavioral finance model with heterogeneous agents. Algorithm. Financ. 1(2), 123–139 (2011)MathSciNetMATHCrossRef Fischer, T.: News reaction in financial markets within a behavioral finance model with heterogeneous agents. Algorithm. Financ. 1(2), 123–139 (2011)MathSciNetMATHCrossRef
19.
Zurück zum Zitat Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: a survey of recent developments. ACM Comput. Surv. 42(4), 14:1–14:53 (2010)CrossRef Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: a survey of recent developments. ACM Comput. Surv. 42(4), 14:1–14:53 (2010)CrossRef
20.
Zurück zum Zitat Gentle, J.E., Hardle, W.K., Mori, Y.: Handbook of Computational Finance. Springer, Berlin (2012)MATH Gentle, J.E., Hardle, W.K., Mori, Y.: Handbook of Computational Finance. Springer, Berlin (2012)MATH
21.
Zurück zum Zitat Gilli, M., Maringer, D., Schumann, E.: Numerical Methods and Optimization in Finance. Academic Press, Cambridge (2019)MATH Gilli, M., Maringer, D., Schumann, E.: Numerical Methods and Optimization in Finance. Academic Press, Cambridge (2019)MATH
22.
Zurück zum Zitat Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)MATH Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)MATH
23.
Zurück zum Zitat Hadi, I.: Intelligent authentication for identity and access management: a review paper. Iraqi J. Comput. Inform. 45(1), 6–10 (2019)CrossRef Hadi, I.: Intelligent authentication for identity and access management: a review paper. Iraqi J. Comput. Inform. 45(1), 6–10 (2019)CrossRef
24.
Zurück zum Zitat Hamill, L., Gilbert, N.: Agent-Based Modelling in Economics. Wiley, Hoboken (2016) Hamill, L., Gilbert, N.: Agent-Based Modelling in Economics. Wiley, Hoboken (2016)
25.
Zurück zum Zitat He, X., Zhao, K., Chu, X.: AutoML: a survey of the state-of-the-art. Knowl. Based Syst. 212, 106622 (2021)CrossRef He, X., Zhao, K., Chu, X.: AutoML: a survey of the state-of-the-art. Knowl. Based Syst. 212, 106622 (2021)CrossRef
26.
Zurück zum Zitat Heaton, J.B., Polson, N.G., Witte, J.H.: Deep learning for finance: deep portfolios. Appl. Stoch. Model. Bus. Ind. 33, 3–12 (2017)MathSciNetMATHCrossRef Heaton, J.B., Polson, N.G., Witte, J.H.: Deep learning for finance: deep portfolios. Appl. Stoch. Model. Bus. Ind. 33, 3–12 (2017)MathSciNetMATHCrossRef
27.
Zurück zum Zitat Hilpisch, Y.: Artificial Intelligence in Finance. OReilly, Newton (2020) Hilpisch, Y.: Artificial Intelligence in Finance. OReilly, Newton (2020)
28.
Zurück zum Zitat International Telecommunication Union. Assessing the economic impact of artificial intelligence, Issue Paper No. 1 (2018) International Telecommunication Union. Assessing the economic impact of artificial intelligence, Issue Paper No. 1 (2018)
29.
Zurück zum Zitat Iwana, B.K., Uchida, S.: An empirical survey of data augmentation for time series classification with neural networks. arXiv preprint arXiv:2007.15951 (2020) Iwana, B.K., Uchida, S.: An empirical survey of data augmentation for time series classification with neural networks. arXiv preprint arXiv:​2007.​15951 (2020)
30.
Zurück zum Zitat Jeong, G., Kim, H.Y.: Improving financial trading decisions using deep q-learning: predicting the number of shares, action strategies, and transfer learning. Expert Syst. Appl. 117, 125–138 (2019)CrossRef Jeong, G., Kim, H.Y.: Improving financial trading decisions using deep q-learning: predicting the number of shares, action strategies, and transfer learning. Expert Syst. Appl. 117, 125–138 (2019)CrossRef
31.
Zurück zum Zitat Kearney, C., Liu, S.: Textual sentiment in finance: a survey of methods and models. Int. Rev. Financ. Anal. 33, 171–185 (2013)CrossRef Kearney, C., Liu, S.: Textual sentiment in finance: a survey of methods and models. Int. Rev. Financ. Anal. 33, 171–185 (2013)CrossRef
32.
Zurück zum Zitat Khashanah, K., Alsulaiman, T.: Network theory and behavioral finance in a heterogeneous market environment. Complexity 21(S2), 530–554 (2016)MathSciNetCrossRef Khashanah, K., Alsulaiman, T.: Network theory and behavioral finance in a heterogeneous market environment. Complexity 21(S2), 530–554 (2016)MathSciNetCrossRef
33.
Zurück zum Zitat Kovalerchuk, B., Vityaev, E.E.: Data Mining in Finance: Advances in Relational and Hybrid Methods. Kluwer Academic Publishers, Dordrecht (2000)MATH Kovalerchuk, B., Vityaev, E.E.: Data Mining in Finance: Advances in Relational and Hybrid Methods. Kluwer Academic Publishers, Dordrecht (2000)MATH
34.
Zurück zum Zitat Lei, K., Zhang, B., Li, Y., Yang, M., Shen, Y.: Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading. Expert Syst. Appl. 140, 112872 (2020)CrossRef Lei, K., Zhang, B., Li, Y., Yang, M., Shen, Y.: Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading. Expert Syst. Appl. 140, 112872 (2020)CrossRef
35.
Zurück zum Zitat Li, B., Hoi, S.C.H.: Online portfolio selection: a survey. ACM Comput. Surv. 46(3), 1–36 (2014)MATH Li, B., Hoi, S.C.H.: Online portfolio selection: a survey. ACM Comput. Surv. 46(3), 1–36 (2014)MATH
36.
Zurück zum Zitat Li, H., Huang, C., Gu, L.: Image pattern recognition in identification of financial bills risk management. Neural Comput. Appl. 33(3), 867–876 (2021)CrossRef Li, H., Huang, C., Gu, L.: Image pattern recognition in identification of financial bills risk management. Neural Comput. Appl. 33(3), 867–876 (2021)CrossRef
37.
Zurück zum Zitat Li, Y., Ni, P., Chang, V.: Application of deep reinforcement learning in stock trading strategies and stock forecasting. Computing 102(6), 1305–1322 (2020)MathSciNetCrossRef Li, Y., Ni, P., Chang, V.: Application of deep reinforcement learning in stock trading strategies and stock forecasting. Computing 102(6), 1305–1322 (2020)MathSciNetCrossRef
38.
Zurück zum Zitat Li, Z., Kiseleva, J., de Rijke M.: Improving response quality with backward reasoning in open-domain dialogue systems. In: Diaz, F., Shah, C., Suel, T., Castells, P., Jones, R., Sakai, T. (eds.) SIGIR’2021, pp. 1940–1944 (2021) Li, Z., Kiseleva, J., de Rijke M.: Improving response quality with backward reasoning in open-domain dialogue systems. In: Diaz, F., Shah, C., Suel, T., Castells, P., Jones, R., Sakai, T. (eds.) SIGIR’2021, pp. 1940–1944 (2021)
39.
Zurück zum Zitat Lynn, T., Mooney, J.G., Rosati, P., Cummins, M.: Disrupting Finance: Fintech and Strategy in the 21st Century. Palgrave Pivot, London (2019)CrossRef Lynn, T., Mooney, J.G., Rosati, P., Cummins, M.: Disrupting Finance: Fintech and Strategy in the 21st Century. Palgrave Pivot, London (2019)CrossRef
40.
Zurück zum Zitat Meng, T.L., Khushi, M.: Reinforcement learning in financial markets. Data 4(3), 110:1–110:17 (2019)CrossRef Meng, T.L., Khushi, M.: Reinforcement learning in financial markets. Data 4(3), 110:1–110:17 (2019)CrossRef
41.
Zurück zum Zitat Mitra, G., Mitra, L.: The Handbook of News Analytics in Finance. Wiley, Hoboken (2012) Mitra, G., Mitra, L.: The Handbook of News Analytics in Finance. Wiley, Hoboken (2012)
42.
Zurück zum Zitat Modha, D.S., Ananthanarayanan, R., Esser, S.K., Ndirango, A., Sherbondy, A.J., Singh, R.: Cognitive computing. Commun. ACM 54(8), 62–71 (2011)CrossRef Modha, D.S., Ananthanarayanan, R., Esser, S.K., Ndirango, A., Sherbondy, A.J., Singh, R.: Cognitive computing. Commun. ACM 54(8), 62–71 (2011)CrossRef
43.
Zurück zum Zitat Nakagawa, K., Abe, M., Komiyama, J.: RIC-NN: a robust transferable deep learning framework for cross-sectional investment strategy. In: DSAA’2020, pp. 370–379. IEEE (2020) Nakagawa, K., Abe, M., Komiyama, J.: RIC-NN: a robust transferable deep learning framework for cross-sectional investment strategy. In: DSAA’2020, pp. 370–379. IEEE (2020)
46.
Zurück zum Zitat Özbayoglu, A.M., Gudelek, M.U., Sezer, O.B.: Deep learning for financial applications: a survey. CoRR abs/2002.05786 (2020) Özbayoglu, A.M., Gudelek, M.U., Sezer, O.B.: Deep learning for financial applications: a survey. CoRR abs/2002.05786 (2020)
47.
48.
Zurück zum Zitat Qi, Y., Xiao, J.: Fintech: AI powers financial services to improve people’s lives. Commun. ACM 61(11), 65–69 (2018)CrossRef Qi, Y., Xiao, J.: Fintech: AI powers financial services to improve people’s lives. Commun. ACM 61(11), 65–69 (2018)CrossRef
49.
Zurück zum Zitat Ryll, L., Barton, M.E., Zhang, B.Z., McWaters, R.J., Schizas, E., Hao, R., Bear, K., Preziuso, M., Seger, E., Wardrop, R., Rau, P.R., Debata, P., Rowan, P., Adams, N., Gray, M., Yerolemou, N.: Transforming paradigms: a global AI in financial services survey (2020) Ryll, L., Barton, M.E., Zhang, B.Z., McWaters, R.J., Schizas, E., Hao, R., Bear, K., Preziuso, M., Seger, E., Wardrop, R., Rau, P.R., Debata, P., Rowan, P., Adams, N., Gray, M., Yerolemou, N.: Transforming paradigms: a global AI in financial services survey (2020)
50.
Zurück zum Zitat Sewak, M.: Deep Reinforcement Learning: Frontiers of Artificial Intelligence. Springer, Berlin (2019)MATHCrossRef Sewak, M.: Deep Reinforcement Learning: Frontiers of Artificial Intelligence. Springer, Berlin (2019)MATHCrossRef
51.
Zurück zum Zitat Sezer, O.B., Gudelek, M.U., Özbayoglu, A.M.: Financial time series forecasting with deep learning: a systematic literature review, pp. 2005–2019. CoRR abs/1911.13288 (2019) Sezer, O.B., Gudelek, M.U., Özbayoglu, A.M.: Financial time series forecasting with deep learning: a systematic literature review, pp. 2005–2019. CoRR abs/1911.13288 (2019)
52.
Zurück zum Zitat Sirignano, J., Cont, R.: Universal features of price formation in financial markets: perspectives from deep learning. Quant. Financ. 19, 1449–1459 (2019)MathSciNetMATHCrossRef Sirignano, J., Cont, R.: Universal features of price formation in financial markets: perspectives from deep learning. Quant. Financ. 19, 1449–1459 (2019)MathSciNetMATHCrossRef
53.
Zurück zum Zitat Spanos, G., Angelis, L.: The impact of information security events to the stock market: a systematic literature review. Comput. Secur. 58, 216–229 (2016)CrossRef Spanos, G., Angelis, L.: The impact of information security events to the stock market: a systematic literature review. Comput. Secur. 58, 216–229 (2016)CrossRef
54.
Zurück zum Zitat Waldrop, M.M.: Complexity: The Emerging Science at the Edge of Order and Chaos. Simon & Schuster Paperbacks, New York (1992) Waldrop, M.M.: Complexity: The Emerging Science at the Edge of Order and Chaos. Simon & Schuster Paperbacks, New York (1992)
55.
Zurück zum Zitat Wei, Y., Chaudhary, V.: The directionality function defect of performance evaluation method in regression neural network for stock price prediction. In: DSAA’2020, pp. 769–770 (2020) Wei, Y., Chaudhary, V.: The directionality function defect of performance evaluation method in regression neural network for stock price prediction. In: DSAA’2020, pp. 769–770 (2020)
56.
Zurück zum Zitat Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 12:1–12:19 (2019)CrossRef Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 12:1–12:19 (2019)CrossRef
57.
Zurück zum Zitat Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., Yu, H.: Federated Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, San Rafael (2019) Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., Yu, H.: Federated Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, San Rafael (2019)
58.
Zurück zum Zitat Zhang, J., He, Q.: Dynamic cross-market volatility spillover based on MSV model: evidence from bitcoin, gold, crude oil, and stock markets. Complex 2021, 9912418:1–9912418:8 (2021) Zhang, J., He, Q.: Dynamic cross-market volatility spillover based on MSV model: evidence from bitcoin, gold, crude oil, and stock markets. Complex 2021, 9912418:1–9912418:8 (2021)
59.
Zurück zum Zitat Zhang, J., Zhuang, Y.: Cross-market infection research on stock herding behavior based on DGC-MSV models and Bayesian network. Complex 2021, 6645151:1–6645151:8 (2021) Zhang, J., Zhuang, Y.: Cross-market infection research on stock herding behavior based on DGC-MSV models and Bayesian network. Complex 2021, 6645151:1–6645151:8 (2021)
60.
Zurück zum Zitat Zhang, X., Li, Y., Wang, S., Fang, B., Yu, P.S.: Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data. Knowl. Inf. Syst. 61(2), 1071–1090 (2019)CrossRef Zhang, X., Li, Y., Wang, S., Fang, B., Yu, P.S.: Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data. Knowl. Inf. Syst. 61(2), 1071–1090 (2019)CrossRef
Metadaten
Titel
Data science and AI in FinTech: an overview
verfasst von
Longbing Cao
Qiang Yang
Philip S. Yu
Publikationsdatum
05.08.2021
Verlag
Springer International Publishing
Erschienen in
International Journal of Data Science and Analytics / Ausgabe 2/2021
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-021-00278-w

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