Open Access
Review
Issue
Security and Safety
Volume 5, 2026
Article Number 2026009
Number of page(s) 39
Section Digital Finance
DOI https://doi.org/10.1051/sands/2026009
Published online 30 June 2026
  1. Arner DW, Barberis J and Buckley RP. The evolution of fintech: A new post-crisis paradigm. Georgetown J Int Law 2015; 47: 1271. [Google Scholar]
  2. Philippon T. The fintech opportunity. Technical report, National Bureau of Economic Research, 2016. [Google Scholar]
  3. Hasan MM, Popp J and Oláh J. Current landscape and influence of big data on finance. J Big Data 2020; 7: 21. [Google Scholar]
  4. Milana C and Ashta A. Artificial intelligence techniques in finance and financial markets: A survey of the literature. Strategic Change 2021; 30: 189–209. [CrossRef] [Google Scholar]
  5. Nutalapati P. A review on cloud computing in finance-transforming financial services in the digital age. Int Res J Eng Appl Sci 2024; 12: 35–45. [Google Scholar]
  6. Javaid M, Haleem A and Singh RP et al. A review of blockchain technology applications for financial services. BenchCouncil Trans Benchmarks Stand Eval 2022; 2: 100073. [Google Scholar]
  7. Chen M, Mao S and Liu Y. Big data: A survey. Mobile Networks Appl 2024; 19: 171–209. [Google Scholar]
  8. Karnati R. Ai-driven financial innovation: Trends, challenges, and opportunities. Int J Sci Technol 2025; 16: 4935. [Google Scholar]
  9. Swan M. Blockchain: Blueprint for a new economy, O’Reilly Media, Inc., 2015. [Google Scholar]
  10. Gomber P, Kauffman RJ and Parker C et al. On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. J Manage Inf Syst 2018; 35: 220–265. [Google Scholar]
  11. Melnychuk A. Features of the financial mechanism of the enterprise in the conditions of digital transformation. Ekonomichnyy Analiz 2024; 34: 106–114. [Google Scholar]
  12. Risman A, Mulyana B and Silvatika B et al. The effect of digital finance on financial stability. Manage Sci Lett 2021; 11: 1979–1984. [Google Scholar]
  13. Allen F, Gu X and Jagtiani J. A survey of fintech research and policy discussion. Rev Corporate Finance 2021; 1: 259–339. [Google Scholar]
  14. Khattak BHA, Shafi I and Khan AS et al. A systematic survey of ai models in financial market forecasting for profitability analysis. IEEE Access 2023; 11: 125359–125380. [Google Scholar]
  15. Moro S, Cortez P and Rita P. Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent dirichlet allocation. Expert Syst Appl 2015; 42: 1314–1324. [Google Scholar]
  16. West J and Bhattacharya M. Intelligent financial fraud detection: A comprehensive review. Comput Secur 2016; 57: 47–66. [Google Scholar]
  17. Ngai EW, Hu Y and Wong YH et al. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decis Support Syst 2011; 50: 559–569. [Google Scholar]
  18. Cochrane JH and Piazzesi M. Bond risk premia. Am Econ Rev 2005; 95: 138–160. [Google Scholar]
  19. Lee C, Huang SH and Chen CT. Poisoning attacks against security-aware federated recommendation system. In: International Conference on Machine Learning and Soft Computing, Springer, 2025, 301–313. [Google Scholar]
  20. Ye C, Ou H and Basile V et al. The effect of uncertainty index based on sparse method on volatility prediction of stock market. Expert Syst Appl 2025; 128208. [Google Scholar]
  21. Lee I and Shin YJ. Fintech: Ecosystem, business models, investment decisions, and challenges. Bus Horiz 2018; 61: 35–46. [Google Scholar]
  22. Xu R, Baracaldo N and Joshi J. Privacy-preserving machine learning: Methods, challenges and directions. arXiv preprint https://arxiv.org/abs/2108.04417, 2021. [Google Scholar]
  23. Xu LD and Duan L. Big data for cyber physical systems in industry 4.0: A survey. Enterprise Inf Syst 2019; 13: 148–169. [Google Scholar]
  24. Pum M. Bridging the gap data engineers and ai model deployment, 2025. [Google Scholar]
  25. Jiang C, Ding Z and Yu J et al. Cabin computing (in chinese). Sci Sin Inform 2021; 51: 1233–1254. [Google Scholar]
  26. Jiang C. Computing power network and trading risk control. J Chongqing Univ Posts Telecommun (Nat Sci Ed) 2023; 35: 1–7. [Google Scholar]
  27. Gomber P, Koch JA and Siering M. Digital finance and fintech: current research and future research directions. J Bus Econ 2017; 87: 537–580. [Google Scholar]
  28. Ozili PK. Digital finance research and developments around the world: A literature review. Int J Bus Forecasting Mark Intell 2023; 8: 35–51. [Google Scholar]
  29. Sun N, Zhang Y and Zhang F. How to translate “computility” into english? Commun China Comput Fed (CCCF) 2022; 18: 87. [Google Scholar]
  30. Silber WL. The economic role of financial futures. In: Salomon Brothers Center for the Study of Financial Institutes, Graduate School of Business Administration, 1985. [Google Scholar]
  31. Balaji K. Revolutionizing high-frequency trading: The impacts of financial technology and data science innovations. In: Machine Learning and Modeling Techniques in Financial Data Science, 2025, 103–124. [Google Scholar]
  32. Dunbar FC and Dunbar FC. Fraud on the market meets behavioral finance. Delaware J Corporate Law 2006; 31: 455. [Google Scholar]
  33. Fletcher GGS. Macroeconomic consequences of market manipulation. Law Contemp Probs 2020; 83: 123. [Google Scholar]
  34. Li Y, Wang S and Wei Y et al. A new hybrid vmd-icss-bigru approach for gold futures price forecasting and algorithmic trading. IEEE Trans Comput Social Syst 2021; 8: 1357–1368. [Google Scholar]
  35. Gong X, Liu Y and Wang X. Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method. Int Rev Financial Anal 2021; 76: 101790. [Google Scholar]
  36. Gu Q, Chang Y and Xiong N et al. Forecasting nickel futures price based on the empirical wavelet transform and gradient boosting decision trees. Appl Soft Comput 2021; 109: 107472. [Google Scholar]
  37. Liu J, Zhang Z and Yan L et al. Forecasting the volatility of eua futures with economic policy uncertainty using the garch-midas model. Financial Innovation 2021; 7: 1–19. [Google Scholar]
  38. Deng S, Zhu Y and Duan S et al. High-frequency forecasting of the crude oil futures price with multiple timeframe predictions fusion. Expert Syst Appl 2023; 217: 119580. [Google Scholar]
  39. Liu M, Liu X and Jia W et al. The trading strategy of inflection point futures analysis based on afs theory. In: 2020 39th Chinese Control Conference (CCC), IEEE, 2020, 2170–2175. [Google Scholar]
  40. Du Y, Liu X and Jia W et al. A new construction method of futures trading strategy construction based on afs theory. In: 2020 39th Chinese Control Conference (CCC), IEEE, 2020, 6420–6425. [Google Scholar]
  41. Huang W, Wang H and Qin H et al. Convolutional neural network forecasting of european union allowances futures using a novel unconstrained transformation method. Energy Econ 2022; 110: 106049. [Google Scholar]
  42. Liwang M, Gao Z and Wang X. Let’s trade in the future! a futures-enabled fast resource trading mechanism in edge computing-assisted uav networks. IEEE J Sel Areas Commun 2021; 39: 3252–3270. [Google Scholar]
  43. Xu K and Niu H. Do eemd based decomposition-ensemble models indeed improve prediction for crude oil futures prices? Technol Forecasting Social Change 2022; 184: 121967. [Google Scholar]
  44. Duan Y, Wang L and Zhang Q et al. Factorvae: A probabilistic dynamic factor model based on variational autoencoder for predicting cross-sectional stock returns. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2022, Vol. 36, 4468–4476. [Google Scholar]
  45. Yang L, Li J and Dong R et al. Numhtml: Numeric-oriented hierarchical transformer model for multi-task financial forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2022, Vol. 36, 11604–11612. [Google Scholar]
  46. Han KM, Park SW and Lee S. Anti-fraud in international supply chain finance: Focusing on moneual case. J Korea Trade 2020; 24: 59–81. [Google Scholar]
  47. Alexander C and Cumming D. Corruption and Fraud in financial markets: Malpractice, Misconduct and Manipulation, John Wiley & Sons, 2022. [Google Scholar]
  48. Karpoff JM. The future of financial fraud. J Corporate Finance 2021; 66: 101694. [Google Scholar]
  49. Wang C, Chai S and Zhu H et al. Caesar: An online payment anti-fraud integration system with decision explainability. IEEE Trans Dependable Secure Comput 2022; 20: 2565–2577. [Google Scholar]
  50. Wang C. The behavioral sign of account theft: Realizing online payment fraud alert. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, 2021, 4511–4618. [Google Scholar]
  51. Lai Y, Zhu Y and Fan W et al. Towards adversarially robust recommendation from adaptive fraudster detection. IEEE Trans Inf Forensics Secur 2023. [Google Scholar]
  52. Hu S, Zhang Z and Luo B et al. Bert4eth: A pre-trained transformer for ethereum fraud detection. In: Proceedings of the ACM Web Conference, 2023, 2189–2197. [Google Scholar]
  53. Li Z, Wang H and Zhang P et al. Live-streaming fraud detection: A heterogeneous graph neural network approach. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, 3670–3678. [Google Scholar]
  54. Liu C, Sun L and Ao X et al. Intention-aware heterogeneous graph attention networks for fraud transactions detection. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, 3280–3288. [Google Scholar]
  55. Zheng W, Yan L and Gou C et al. Federated meta-learning for fraudulent credit card detection. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, 2021, 4654–4660. [Google Scholar]
  56. Cheng D, Xiang S and Shang C et al. Spatio-temporal attention-based neural network for credit card fraud detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2020, Vol. 34, 362–369. [Google Scholar]
  57. Xiang S, Zhu M and Cheng D et al. Semi-supervised credit card fraud detection via attribute-driven graph representation. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2023, Vol. 37, 14557–14565. [Google Scholar]
  58. Xu F, Wang N and Wu H et al. Revisiting graph-based fraud detection in sight of heterophily and spectrum. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2024, Vol. 38, 9214–9222. [Google Scholar]
  59. Cheng D, Wang X and Zhang Y et al. Graph neural network for fraud detection via spatial-temporal attention. IEEE Trans Knowl Data Eng 2020; 34: 3800–3813. [Google Scholar]
  60. Abi Din Z, Venugopalan H and Lin H et al. Doing good by fighting fraud: Ethical anti-fraud systems for mobile payments. In: 2021 IEEE Symposium on Security and Privacy (SP), IEEE, 2021, 1623–1640. [Google Scholar]
  61. Chen C, Lin K and Rudin C et al. A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. Decis Support Syst 2022; 152: 113647. [Google Scholar]
  62. Botha A, Beyers C and De Villiers P. Simulation-based optimisation of the timing of loan recovery across different portfolios. Expert Syst Appl 2021; 177: 114878. [Google Scholar]
  63. Song Y, Wang Y and Ye X et al. Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in p2p lending. Inf Sci 2020; 525: 182–204. [Google Scholar]
  64. Song Y, Wang Y and Ye X et al. Loan default prediction using a credit rating-specific and multi-objective ensemble learning scheme. Inf Sci 2023; 629: 599–617. [Google Scholar]
  65. Xu B, Shen H and Sun B et al. Towards consumer loan fraud detection: Graph neural networks with role-constrained conditional random field. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2021, Vol. 35, 4537–4545. [Google Scholar]
  66. Błaszczyński J, de Almeida Filho AT and Matuszyk A et al. Auto loan fraud detection using dominance-based rough set approach versus machine learning methods. Expert Syst Appl 2021; 163: 113740. [Google Scholar]
  67. Lu Z, Li T and Zhang J et al. Risqnet: Rescuing smes from financial shocks with a novel networked-loan risk assessment, 2024. [Google Scholar]
  68. Yang G, Liu X and Li B. Anti-money laundering supervision by intelligent algorithm. Comput Secur 2023; 132: 103344. [Google Scholar]
  69. Cheng D, Ye Y and Xiang S et al. Anti-money laundering by group-aware deep graph learning. IEEE Trans Knowl Data Eng 2023; 35: 12444–12457. [Google Scholar]
  70. Jensen RIT and Iosifidis A. Qualifying and raising anti-money laundering alarms with deep learning. Expert Syst Appl 2023; 214: 119037. [Google Scholar]
  71. Rocha-Salazar JJ, Segovia-Vargas MJ and Camacho-Miñano MM. Money laundering and terrorism financing detection using neural networks and an abnormality indicator. Expert Syst Appl 2021; 169: 114470. [Google Scholar]
  72. Li X, Liu S and Li Z et al. Flowscope: Spotting money laundering based on graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2020, Vol. 34, 4731–4738. [Google Scholar]
  73. Song J, Zhang S and Zhang P et al. Illicit social accounts? anti-money laundering for transactional blockchains. IEEE Trans Inf Forensics Secur 2024. [Google Scholar]
  74. Du H, Shen M and Sun R et al. Malicious transaction identification in digital currency via federated graph deep learning. In: IEEE INFOCOM 2022-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, 2022, 1–6. [Google Scholar]
  75. Alexandre CR and Balsa J. Incorporating machine learning and a risk-based strategy in an anti-money laundering multiagent system. Expert Syst Appl 2023; 217: 119500. [Google Scholar]
  76. Chai Z, Yang Y and Dan J et al. Towards learning to discover money laundering sub-network in massive transaction network. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2023, Vol. 37, 14153–14160. [Google Scholar]
  77. Altman E, Blanuša J and Von Niederhäusern L et al. Realistic synthetic financial transactions for anti-money laundering models. Adv Neural Inf Process Syst 2024; 36. [Google Scholar]
  78. Li N, Gao C and Li M et al. Econagent: Large language model-empowered agents for simulating macroeconomic activities. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 15523–15536. [Google Scholar]
  79. Aras S and Lisboa PJ. Explainable inflation forecasts by machine learning models. Expert Syst Appl 2022; 207: 117982. [Google Scholar]
  80. Miranda-Agrippino S and Ricco G. The transmission of monetary policy shocks. Am Econ J Macroeconomics 2021; 13: 74–107. [Google Scholar]
  81. Gorodnichenko Y, Pham T and Talavera O. The voice of monetary policy. Am Econ Rev 2023; 113: 548–584. [Google Scholar]
  82. Jordà Ò, Singh SR and Taylor AM. The long-run effects of monetary policy. Technical report, National Bureau of Economic Research, 2020. [Google Scholar]
  83. Wang Y, Whited TM and Wu Y et al. Bank market power and monetary policy transmission: Evidence from a structural estimation. J Finance 2022; 77: 2093–2141. [Google Scholar]
  84. Zhu H, Liu SY and Zhao P et al. Forecasting asset dependencies to reduce portfolio risk. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2022, Vol. 36, 4397–4404. [Google Scholar]
  85. Shah A, Paturi S and Chava S. Trillion dollar words: A new financial dataset, task & market analysis. arXiv preprint https://arxiv.org/abs/2305.07972, 2023. [Google Scholar]
  86. Omowole BM, Urefe O and Mokogwu C et al. Integrating fintech and innovation in microfinance: Transforming credit accessibility for small businesses. Int J Frontline Res Rev 2024; 3: 090–100. [Google Scholar]
  87. Aripin Z, Wibowo LA and Ariyanti M. Funding liquidity dynamics and its influence on bank lending growth: A review of the indonesian banking context. J Econ Accounting Bus Manage Eng Soc 2024; 1: 1–18. [Google Scholar]
  88. Van Gestel T and Baesens B. Credit Risk Management: Basic concepts: Financial risk components, Rating analysis, models, economic and regulatory capital, OUP Oxford, 2008. [Google Scholar]
  89. Mallika BK and Ramasubramanian V. Anti money laundering system in detecting and preventing money laundering activities: A systematic review. J Money Laundering Control 2025; 28: 385–407. [Google Scholar]
  90. Levi M and Reuter P. Money laundering. Crime Justice 2006; 34: 289–375. [Google Scholar]
  91. Kumar R. The impact of monetary policy on economic growth. Int J Exploring Emerging Trends Eng 2023; 9: 30–38. [Google Scholar]
  92. Dexu H and Wenlong M. Fiscal decentralization, financial decentralization and macroeconomic governance. China Econ 2022; 17: 84–105. [Google Scholar]
  93. Dilaver Ö, Calvert Jump R and Levine P. Agent-based macroeconomics and dynamic stochastic general equilibrium models: Where do we go from here? J Econ Surv 2018; 32: 1134–1159. [Google Scholar]
  94. Lütkepohl H et al. Econometric analysis with vector autoregressive models, Wiley Online Library, 2009. [Google Scholar]
  95. Ye Z, Qin Y and Xu W. Financial risk prediction with multi-round q &a attention network. In: IJCAI, 2020, 4576–4582. [Google Scholar]
  96. Sun J, Li H and Fujita H et al. Class-imbalanced dynamic financial distress prediction based on adaboost-svm ensemble combined with smote and time weighting. Inf Fusion 2020; 54: 128–144. [Google Scholar]
  97. Qin Y and Yang Y. What you say and how you say it matters: Predicting stock volatility using verbal and vocal cues. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019, 390–401. [Google Scholar]
  98. Wang WY and Hua Z. A semiparametric gaussian copula regression model for predicting financial risks from earnings calls. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2014, 1155–1165. [Google Scholar]
  99. Chen X and Long Z. E-commerce enterprises financial risk prediction based on fa-pso-lstm neural network deep learning model. Sustainability 2023; 15: 5882. [Google Scholar]
  100. Park S and Yang JS. Machine learning models based on bubble analysis for bitcoin market crash prediction. Eng Appl Artif Intell 2024; 135: 108857. [Google Scholar]
  101. Lee CHL, Liu A and Chen WS. Pattern discovery of fuzzy time series for financial prediction. IEEE Trans Knowledge Data Eng 2006; 18: 613–625. [Google Scholar]
  102. Lux T and Marchesi M. Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 1999; 397: 498–500. [Google Scholar]
  103. Ozgur O, Yilanci V and Ozbugday FC. Detecting speculative bubbles in metal prices: Evidence from gsadf test and machine learning approaches. Resour Policy 2021; 74: 102306. [Google Scholar]
  104. Chauhan GS. Mediating role of profitability relating financial leverage and stock returns. Int J Emerging Markets 2024; 19: 3459–3482. [Google Scholar]
  105. Claassen B, Dam L and Heijnen P. Corporate financing policies, financial leverage, and stock returns. North Am J Econ Finance 2023; 68: 101992. [Google Scholar]
  106. Sawhney R, Agarwal S and Wadhwa A et al. Exploring the scale-free nature of stock markets: Hyperbolic graph learning for algorithmic trading. In: Proceedings of the Web Conference, 2021, 11–22. [Google Scholar]
  107. Gan C, Hu B and Huang B et al. Which matters most in making fund investment decisions? a multi-granularity graph disentangled learning framework. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023, 2516–2520. [Google Scholar]
  108. Ye Y, Pei H and Wang B et al. Reinforcement-learning based portfolio management with augmented asset movement prediction states. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2020, Vol. 34, 1112–1119. [Google Scholar]
  109. Liang X, Cheng D and Yang F et al. F-hmtc: Detecting financial events for investment decisions based on neural hierarchical multi-label text classification. In: IJCAI, 2020, 4490–4496. [Google Scholar]
  110. Li M, Zhou J and Yu L et al. A rule-based decision system for financial applications. In: 2023 IEEE 39th International Conference on Data Engineering (ICDE), IEEE, 2023, 3535–3548. [Google Scholar]
  111. Rivera-Castro R and Burnaev E. Causalysis: Causal machine learning for real-estate investment decisions. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, 2021, 1–3. [Google Scholar]
  112. Yu Y, Yao Z and Li H et al. Fincon: A synthesized llm multi-agent system with conceptual verbal reinforcement for enhanced financial decision making. Adv Neural Inf Process Syst 2025; 37: 137010–137045. [Google Scholar]
  113. Amrouni S, Moulin A and Vann J et al. Abides-gym: gym environments for multi-agent discrete event simulation and application to financial markets. In: Proceedings of the Second ACM International Conference on AI in Finance, 2021, 1–9. [Google Scholar]
  114. Shavandi A and Khedmati M. A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets. Expert Syst Appl 2022; 208: 118124. [Google Scholar]
  115. Huang Y, Zhou C and Cui K et al. A multi-agent reinforcement learning framework for optimizing financial trading strategies based on timesnet. Expert Syst Appl 2024; 237: 121502. [Google Scholar]
  116. Wang X, Ma GQ and Eden A et al. Platform behavior under market shocks: A simulation framework and reinforcement-learning based study. In: Proceedings of the ACM Web Conference, 2023, 3592–3602. [Google Scholar]
  117. Nokhiz P, Ruwanpathirana AK and Patwari N et al. Agent-based simulation of decision-making under uncertainty to study financial precarity. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 2024, 43–56. [Google Scholar]
  118. Kaur S, Smiley C and Gupta A et al. Refind: Relation extraction financial dataset. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023, 3054–3063. [Google Scholar]
  119. Zhu J, Li J and Wen Y et al. Benchmarking large language models on cflue–a chinese financial language understanding evaluation dataset. arXiv preprint https://arxiv.org/abs/2405.10542, 2024. [Google Scholar]
  120. Li J, Yang L and Smyth B et al. Maec: A multimodal aligned earnings conference call dataset for financial risk prediction. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020, 3063–3070. [Google Scholar]
  121. Chen J, Zhou P and Hua Y et al. Fintextqa: A dataset for long-form financial question answering. arXiv preprint https://arxiv.org/abs/2405.09980, 2024. [Google Scholar]
  122. Chen Z, Chen W and Smiley C et al. Finqa: A dataset of numerical reasoning over financial data. arXiv preprint https://arxiv.org/abs/2109.00122, 2021. [Google Scholar]
  123. Reddy V, Koncel-Kedziorski R and Lai VD et al. Docfinqa: A long-context financial reasoning dataset. arXiv preprint https://arxiv.org/abs/2401.06915, 2024. [Google Scholar]
  124. Zhao Y, Liu H and Long Y et al. Financemath: Knowledge-intensive math reasoning in finance domains. arXiv preprint https://arxiv.org/abs/2311.09797, 2023. [Google Scholar]
  125. Lyua S and Jiao Z. Optimization of financial asset allocation and risk management strategies combining internet of things and clustering algorithms. IEEE Internet Things J 2024. [Google Scholar]
  126. Deng Y, Lei W and Zhang W et al. Pacific: towards proactive conversational question answering over tabular and textual data in finance. arXiv preprint https://arxiv.org/abs/2210.08817, 2022. [Google Scholar]
  127. Luo B, Zhang Z and Wang Q et al. Ai-powered fraud detection in decentralized finance: A project life cycle perspective. ACM Comput Surv 2024; 57: 1–38. [Google Scholar]
  128. Hou L, Bi G and Guo Q. An improved sparrow search algorithm optimized lightgbm approach for credit risk prediction of smes in supply chain finance. J Comput Appl Math 2025; 454: 116197. [Google Scholar]
  129. Sang B. Application of genetic algorithm and bp neural network in supply chain finance under information sharing. J Comput Appl Math 2021; 384: 113170. [Google Scholar]
  130. Tran DT, Iosifidis A and Kanniainen J et al. Temporal attention-augmented bilinear network for financial time-series data analysis. IEEE Trans Neural Networks Learn Syst 2018; 30: 1407–1418. [Google Scholar]
  131. Blasco T, Sánchez JS and García V. A survey on uncertainty quantification in deep learning for financial time series prediction. Neurocomputing 2024; 576: 127339. [Google Scholar]
  132. Liu Q, Luo Y and Wu S et al. Rmt-net: Reject-aware multi-task network for modeling missing-not-at-random data in financial credit scoring. IEEE Trans Knowl Data Eng 2022; 35: 7427–7439. [Google Scholar]
  133. Yang L, Ma Y and Zhang Y. Measuring consistency in text-based financial forecasting models. arXiv preprint https://arxiv.org/abs/2305.08524, 2023. [Google Scholar]
  134. Ge W, Lalbakhsh P and Isai L et al. Neural network–based financial volatility forecasting: A systematic review. ACM Comput Surv (CSUR), 2022; 55: 1–30. [Google Scholar]
  135. Nazareth N and Reddy YVR. Financial applications of machine learning: A literature review. Expert Syst Appl 2023; 219: 119640. [Google Scholar]
  136. Mandal PK and Thakur M. Higher-order moments in portfolio selection problems: A comprehensive literature review. Expert Syst Appl 2024; 238: 121625. [Google Scholar]
  137. Olorunnimbe K and Viktor H. Deep learning in the stock market–a systematic survey of practice, backtesting, and applications. Artif Intell Rev 2023; 56: 2057–2109. [Google Scholar]
  138. Tetlock PC. Information transmission in finance. Annu Rev Finance Econ 2014; 6: 365–384. [Google Scholar]
  139. Monteiro AM and Santos AA. Parallel computing in finance for estimating risk-neutral densities through option prices. J Parallel Distrib Comput 2023; 173: 61–69. [Google Scholar]
  140. Inggs G, Thomas BD and Luk W. A domain specific approach to high performance heterogeneous computing. IEEE Trans Parallel Distrib Syst 2016; 28: 2–15. [Google Scholar]
  141. Zhu X, Ma F and Ding F et al. A low-latency edge computation offloading scheme for trust evaluation in finance-level artificial intelligence of things. IEEE Internet Things J 2023; 11: 114–124. [Google Scholar]
  142. Herman D, Googin C and Liu X et al. Quantum computing for finance. Nat Rev Phys 2023; 5: 450–465. [Google Scholar]
  143. Orús R, Mugel S and Lizaso E. Quantum computing for finance: Overview and prospects. Rev Phys 2019; 4: 100028. [Google Scholar]
  144. Saba T, Haseeb K and Rehman A et al. Blockchain-enabled intelligent iot protocol for high-performance and secured big financial data transaction. IEEE Trans Comput Soc Syst 2023; 11: 1667–1674. [Google Scholar]
  145. Li K, Mei J and Li K. A fund-constrained investment scheme for profit maximization in cloud computing. IEEE Trans Serv Comput 2016; 11: 893–907. [Google Scholar]
  146. Mann ZÁ, Metzger A and Prade J et al. Cost-optimized, data-protection-aware offloading between an edge data center and the cloud. IEEE Trans Serv Comput 2022; 16: 206–220. [Google Scholar]
  147. Deng Y. Construction of a digital platform for enterprise financial management based on visual processing technology. Sci Progr 2022; 2022: 7666110. [Google Scholar]
  148. Shi W, Long SQ and Li Y. The risk analysis of digital inclusive financial platform using deep learning approach. J Inf Sci Eng 2024; 40. [Google Scholar]
  149. Zhang X, Chen P and You C et al. An industrial digital financial application platform based on privacy computing. In: Proceedings of the 2024 the 12th International Conference on Information Technology (ICIT), 2024, 25–30. [Google Scholar]
  150. Abgaryan A and Sharma U. Intralayer: A platform of digital finance platforms. arXiv preprint https://arxiv.org/abs/2412.07348, 2024. [Google Scholar]
  151. Luo Y. Financial data security management method and edge computing platform based on intelligent edge computing and big data. IETE J Res 2023; 69: 5187–5195. [Google Scholar]
  152. Wang T and Tobias GR. Research on intelligent optimization mechanisms of financial process modules through machine learning-enhanced collaborative systems in digital finance platforms. Future Technol 2025; 4: 240–254. [Google Scholar]
  153. Chen H. Construction of a novel digital platform for smart financial talent training under the big data environment. In: 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), IEEE, 2022, 569–572. [Google Scholar]
  154. Liao Y, Gou Y and Liu Z et al. Analysis and design of financial wealth management platform based on cloud edge collaboration. In: 2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC), IEEE, 2024, 165–168. [Google Scholar]
  155. Yu H. Research on the construction of enterprise financial sharing platform in the background of cloud computing. In: Proceedings of the 2025 4th International Conference on Cyber Security, Artificial Intelligence and the Digital Economy, 2025, 9–13. [Google Scholar]
  156. Li Q. Design of intelligent financial sharing platform driven by consensus mechanism under mobile edge computing and accounting transformation. Int J Data Mining Bioinf 2024; 28: 426–438. [Google Scholar]
  157. Wang C, Wang C and Zeng S et al. Advanced digital simulation for financial market dynamics: A case of commodity futures. arXiv preprint https://arxiv.org/abs/2503.20787, 2025. [Google Scholar]
  158. Wang C, Wang C and Zhang W et al. Next-generation simulation illuminates scientific problems of organised complexity. arXiv preprint https://arxiv.org/abs/2401.09851, 2024. [Google Scholar]
  159. Wang C, Wang Y and Li Z et al. Scaling laws of data-driven machine learning models: A survey and taxonomy. Authorea Preprints. [Google Scholar]
  160. Wu Z, Zhang Z and Zhao Q et al. Privacy-preserving financial transaction pattern recognition: A differential privacy approach, 2025. [Google Scholar]
  161. Dhiman S, Nayak S and Mahato GK et al. Homomorphic encryption based federated learning for financial data security. In: 2023 4th International Conference on Computing and Communication Systems (I3CS), IEEE, 1–6. [Google Scholar]
  162. Liu T, Wang Z and He H et al. Efficient and secure federated learning for financial applications. Appl Sci 2023; 13: 5877. [Google Scholar]
  163. Pamisetty V, Dodda A and Singireddy J et al. Optimizing digital finance and regulatory systems through intelligent automation, secure data architectures, and advanced analytical technologies. In: Jeevani and Challa, Kishore, Optimizing Digital Finance and Regulatory Systems Through Intelligent Automation, Secure Data Architectures, and Advanced Analytical Technologies (December 10, 2022), 2022. [Google Scholar]
  164. Bhatia R. The convergence of cloud and digital financial architecture in enterprise systems. In: International Conference of Global Innovations and Solutions, Springer, 2025, 637–656. [Google Scholar]
  165. Kim KJ and Hong SP. Study on digital finance secure architecture based on blockchain. J Adv Navig Technol 2021; 25: 415–425. [Google Scholar]
  166. Bhatia R. Digital finance architecture for the public sector: Redesigning us tax and fund distribution systems with fastTM and dfraTM. J Comput Sci Technol Stud 2025; 7: 1174–1183. [Google Scholar]

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