Aloud ME, Alkhamees N, 2021. Intelligent algorithmic trading strategy using reinforcement learning and directional change. IEEE Access, 9:114659-114671. https://doi.org/10.1109/ACCESS.2021.3105259https://doi.org/10.1109/ACCESS.2021.3105259

Azzone M, Barucci E, Moncayo GG, et al., 2022. A machine learning model for lapse prediction in life insurance contracts. Expert Syst Appl, 191:116261. https://doi.org/10.1016/j.eswa.2021.116261https://doi.org/10.1016/j.eswa.2021.116261

Baesens B, Höppner S, Verdonck T, 2021. Data engineering for fraud detection. Decis Support Syst, 150:113492. https://doi.org/10.1016/j.dss.2021.113492https://doi.org/10.1016/j.dss.2021.113492

Bai MJ, Zheng Y, Shen Y, 2022. Gradient boosting survival tree with applications in credit scoring. J Oper Res Soc, 73(1):39-55. https://doi.org/10.1080/01605682.2021.1919035https://doi.org/10.1080/01605682.2021.1919035

Cao SS, Yang XX, Chen C, et al., 2019. TitAnt: online real-time transaction fraud detection in Ant Financial. Proc VLDB Endow, 12(12):2082-2093. https://doi.org/10.14778/3352063.3352126https://doi.org/10.14778/3352063.3352126

Carcillo F, Le Borgne YA, Caelen O, et al., 2021. Combining unsupervised and supervised learning in credit card fraud detection. Inform Sci, 557:317-331. https://doi.org/10.1016/j.ins.2019.05.042https://doi.org/10.1016/j.ins.2019.05.042

Chen C, Liang C, Lin JB, et al., 2019a. InfDetect: a large scale graph-based fraud detection system for E-commerce insurance. IEEE Int Conf on Big Data, p.1765-1773. https://doi.org/10.1109/BigData47090.2019.9006115https://doi.org/10.1109/BigData47090.2019.9006115

Chen C, Fu CL, Hu X, et al., 2019b. Reinforcement learning for user intent prediction in customer service bots. Proc 42nd Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.1265-1268. https://doi.org/10.1145/3331184.3331370https://doi.org/10.1145/3331184.3331370

Chen CC, Zhou J, Wang L, et al., 2021. When homomorphic encryption marries secret sharing: secure large-scale sparse logistic regression and applications in risk control. Proc 27th ACM SIGKDD Conf on Knowledge Discovery & Data Mining, p.2652-2662. https://doi.org/10.1145/3447548.3467210https://doi.org/10.1145/3447548.3467210

Chen ZY, Van Khoa LD, Teoh EN, et al., 2018. Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review. Knowl Inform Syst, 57(2):245-285. https://doi.org/10.1007/s10115-017-1144-zhttps://doi.org/10.1007/s10115-017-1144-z

Cheng DW, Xiang S, Shang CC, et al., 2020. Spatio-temporal attention-based neural network for credit card fraud detection. Proc AAAI Conf on Artif Intell, 34(1):362-369. https://doi.org/10.1609/aaai.v34i01.5371https://doi.org/10.1609/aaai.v34i01.5371

Cheng XQ, Liu SH, Sun XQ, et al., 2021. Combating emerging financial risks in the big data era: a perspective review. Fundam Res, 1(5):595-606. https://doi.org/10.1016/j.fmre.2021.08.017https://doi.org/10.1016/j.fmre.2021.08.017

Chou YC, Chen CT, Huang SH, 2022. Modeling behavior sequence for personalized fund recommendation with graphical deep collaborative filtering. Expert Syst Appl, 192:116311. https://doi.org/10.1016/j.eswa.2021.116311https://doi.org/10.1016/j.eswa.2021.116311

Corbett-Davies S, Pierson E, Feller A, et al., 2017. Algorithmic decision making and the cost of fairness. Proc 23rd ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining, p.797-806. https://doi.org/10.1145/3097983.3098095https://doi.org/10.1145/3097983.3098095

Cui LM, Seo H, Tabar M, et al., 2020. DETERRENT: knowledge guided graph attention network for detecting healthcare misinformation. Proc 26th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining, p.492-502. https://doi.org/10.1145/3394486.3403092https://doi.org/10.1145/3394486.3403092

Dash S, Günlük O, Wei D, 2018. Boolean decision rules via column generation. Proc 32nd Int Conf on Neural Information Processing Systems, p.4655-4665.

Dastile X, Celik T, Potsane M, 2020. Statistical and machine learning models in credit scoring: a systematic literature survey. Appl Soft Comput, 91:106263. https://doi.org/10.1016/j.asoc.2020.106263https://doi.org/10.1016/j.asoc.2020.106263

Djeundje VB, Crook J, Calabrese R, et al., 2021. Enhancing credit scoring with alternative data. Expert Syst Appl, 163:113766. https://doi.org/10.1016/j.eswa.2020.113766https://doi.org/10.1016/j.eswa.2020.113766

Doering J, Kizys R, Juan AA, et al., 2019. Metaheuristics for rich portfolio optimisation and risk management: current state and future trends. Oper Res Perspect, 6:100121. https://doi.org/10.1016/j.orp.2019.100121https://doi.org/10.1016/j.orp.2019.100121

Dumitrescu E, Hué S, Hurlin C, et al., 2022. Machine learning for credit scoring: improving logistic regression with non-linear decision-tree effects. Eur J Oper Res, 297(3):1178-1192. https://doi.org/10.1016/j.ejor.2021.06.053https://doi.org/10.1016/j.ejor.2021.06.053

Ehrentraud J, Ocampo DG, Garzoni L, et al., 2020. Policy Responses to Fintech: a Cross-Country Overview. FSI Insights on Policy Implementation, No. 23. Bank for International Settlements.

Etmann C, Lunz S, Maass P, et al., 2019. On the connection between adversarial robustness and saliency map interpretability. Proc 36th Int Conf on Machine Learning, p.1823-1832.

Fiore U, De Santis A, Perla F, et al., 2019. Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Inform Sci, 479:448-455. https://doi.org/10.1016/j.ins.2017.12.030https://doi.org/10.1016/j.ins.2017.12.030

Floridi L, 2019. Establishing the rules for building trustworthy AI. Nat Mach Intell, 1(6):261-262. https://doi.org/10.1038/s42256-019-0055-yhttps://doi.org/10.1038/s42256-019-0055-y

Forough J, Momtazi S, 2021. Ensemble of deep sequential models for credit card fraud detection. Appl Soft Comput, 99:106883. https://doi.org/10.1016/j.asoc.2020.106883https://doi.org/10.1016/j.asoc.2020.106883

G20, 2019. G20 Japan: AI Principles. https://www.g20-insights.org/wp-content/uploads/2019/07/G20-Japan-AI-Principles.pdfhttps://www.g20-insights.org/wp-content/uploads/2019/07/G20-Japan-AI-Principles.pdf [Accessed on Jan. 14, 2022].

Gao QJ, Xu DL, 2019. An empirical study on the application of the evidential reasoning rule to decision making in financial investment. Knowl-Based Syst, 164:226-234. https://doi.org/10.1016/j.knosys.2018.10.039https://doi.org/10.1016/j.knosys.2018.10.039

Ghosh Dastidar K, Jurgovsky J, Siblini W, et al., 2022. NAG: neural feature aggregation framework for credit card fraud detection. Knowl Inform Syst, 64(3):831-858. https://doi.org/10.1007/s10115-022-01653-0https://doi.org/10.1007/s10115-022-01653-0

Gianini G, Fossi LG, Mio C, et al., 2020. Managing a pool of rules for credit card fraud detection by a game theory based approach. Fut Gener Comput Syst, 102:549-561. https://doi.org/10.1016/j.future.2019.08.028https://doi.org/10.1016/j.future.2019.08.028

Gomes C, Jin Z, Yang HL, 2021. Insurance fraud detection with unsupervised deep learning. J Risk Insur, 88(3):591-624. https://doi.org/10.1111/jori.12359https://doi.org/10.1111/jori.12359

Hassani H, Huang X, Silva E, et al., 2020. Deep learning and implementations in banking. Ann Data Sci, 7(3):433-446. https://doi.org/10.1007/s40745-020-00300-1https://doi.org/10.1007/s40745-020-00300-1

Herland M, Bauder RA, Khoshgoftaar TM, 2019. The effects of class rarity on the evaluation of supervised healthcare fraud detection models. J Big Data, 6(1):21. https://doi.org/10.1186/s40537-019-0181-8https://doi.org/10.1186/s40537-019-0181-8

Hickman E, Petrin M, 2021. Trustworthy AI and corporate governance: the EU's ethics guidelines for trustworthy artificial intelligence from a company law perspective. Eur Bus Org Law Rev, 22(4):593-625. https://doi.org/10.1007/s40804-021-00224-0https://doi.org/10.1007/s40804-021-00224-0

Hu BB, Zhang ZQ, Shi C, et al., 2019. Cash-out user detection based on attributed heterogeneous information network with a hierarchical attention mechanism. Proc AAAI Conf on Artif Intell, 33(1):946-953. https://doi.org/10.1609/aaai.v33i01.3301946https://doi.org/10.1609/aaai.v33i01.3301946

IEEE, 2017. EAD: a Vision for Prioritizing Human Well-Being with Autonomous and Intelligent Systems. https://standards.ieee.org/news/2017/ead_v2https://standards.ieee.org/news/2017/ead_v2 [Accessed on Jan. 13, 2022].

Jiang JX, Ni BY, Wang CP, 2021. Financial fraud detection on micro-credit loan scenario via Fuller location information embedding. Web Conf, p.238-246. https://doi.org/10.1145/3442442.3451372https://doi.org/10.1145/3442442.3451372

Kazemi HR, Khalili-Damghani K, Sadi-Nezhad S, 2021. Tuning structural parameters of neural networks using genetic algorithm: a credit scoring application. Expert Syst, 38(7):e12733. https://doi.org/10.1111/exsy.12733https://doi.org/10.1111/exsy.12733

Kim E, Lee J, Shin H, et al., 2019. Champion-challenger analysis for credit card fraud detection: hybrid ensemble and deep learning. Expert Syst Appl, 128:214-224. https://doi.org/10.1016/j.eswa.2019.03.042https://doi.org/10.1016/j.eswa.2019.03.042

Kriebel J, Stitz L, 2022. Credit default prediction from user-generated text in peer-to-peer lending using deep learning. Eur J Oper Res, 302(1):309-323. https://doi.org/10.1016/j.ejor.2021.12.024https://doi.org/10.1016/j.ejor.2021.12.024

Lappas PZ, Yannacopoulos AN, 2021. A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment. Appl Soft Comput, 107:107391. https://doi.org/10.1016/j.asoc.2021.107391https://doi.org/10.1016/j.asoc.2021.107391

Lee SI, Yoo SJ, 2020. Multimodal deep learning for finance: integrating and forecasting international stock markets. J Supercomput, 76(10):8294-8312. https://doi.org/10.1007/s11227-019-03101-3https://doi.org/10.1007/s11227-019-03101-3

Li Q, Tan JH, Wang J, et al., 2020. A multimodal event-driven LSTM model for stock prediction using online news. IEEE Trans Knowl Data Eng, 33(10):3323-3337. https://doi.org/10.1109/TKDE.2020.2968894https://doi.org/10.1109/TKDE.2020.2968894

Li Z, Hui PR, Zhang P, et al., 2021. What happens behind the scene? Towards fraud community detection in E-commerce from online to offline. Web Conf, p.105-113. https://doi.org/10.1145/3442442.3451147https://doi.org/10.1145/3442442.3451147

Li ZC, Huang M, Liu GJ, et al., 2021. A hybrid method with dynamic weighted entropy for handling the problem of class imbalance with overlap in credit card fraud detection. Expert Syst Appl, 175:114750. https://doi.org/10.1016/j.eswa.2021.114750https://doi.org/10.1016/j.eswa.2021.114750

Liang C, Liu ZQ, Liu B, et al., 2019. Uncovering insurance fraud conspiracy with network learning. Proc 42nd Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.1181-1184. https://doi.org/10.1145/3331184.3331372https://doi.org/10.1145/3331184.3331372

Lin WL, Sun L, Zhong QW, et al., 2021. Online credit payment fraud detection via structure-aware hierarchical recurrent neural network. Proc 30th Int Joint Conf on Artificial Intelligence, p.3670-3676. https://doi.org/10.24963/ijcai.2021/505https://doi.org/10.24963/ijcai.2021/505

Liu WW, Guo J, Sonboli N, et al., 2019. Personalized fairness-aware re-ranking for microlending. Proc 13th ACM Conf on Recommender Systems, p.467-471. https://doi.org/10.1145/3298689.3347016https://doi.org/10.1145/3298689.3347016

Liu ZQ, Chen CC, Yang XX, et al., 2018. Heterogeneous graph neural networks for malicious account detection. Proc 27th ACM Int Conf on Information and Knowledge Management, p.2077-2085. https://doi.org/10.1145/3269206.3272010https://doi.org/10.1145/3269206.3272010

Madiega TA, 2019. EU Guidelines on Ethics in Artificial Intelligence: Context and Implementation. https://www.europarl.europa.eu/RegData/etudes/BRIE/ 2019/640163/EPRS_BRI(2019)640163_EN.pdfhttps://www.europarl.europa.eu/RegData/etudes/BRIE/ 2019/640163/EPRS_BRI(2019)640163_EN.pdf [Accessed on Jan. 19, 2022].

Mehrabi N, Morstatter F, Saxena N, et al., 2021. A survey on bias and fairness in machine learning. ACM Comput Surv, 54(6):115. https://doi.org/10.1145/3457607https://doi.org/10.1145/3457607

Mohassel P, Zhang YP, 2017. SecureML: a system for scalable privacy-preserving machine learning. IEEE Symp on Security and Privacy, p.19-38. https://doi.org/10.1109/SP.2017.12https://doi.org/10.1109/SP.2017.12

Ozbayoglu AM, Gudelek MU, Sezer OB, 2020. Deep learning for financial applications: a survey. Appl Soft Comput, 93:106384. https://doi.org/10.1016/j.asoc.2020.106384https://doi.org/10.1016/j.asoc.2020.106384

Proença HM, van Leeuwen M, 2020. Interpretable multiclass classification by MDL-based rule lists. Inform Sci, 512:1372-1393. https://doi.org/10.1016/j.ins.2019.10.050https://doi.org/10.1016/j.ins.2019.10.050

Qi Y, Xiao J, 2018. Fintech: AI powers financial services to improve people's lives. Commun ACM, 61(11):65-69. https://doi.org/10.1145/3239550https://doi.org/10.1145/3239550

Sawhney R, Mathur P, Mangal A, et al., 2020. Multimodal multi-task financial risk forecasting. Proc 28th ACM Int Conf on Multimedia, p.456-465. https://doi.org/10.1145/3394171.3413752https://doi.org/10.1145/3394171.3413752

Sobreira Leite G, Bessa Albuquerque A, Rogerio Pinheiro P, 2019. Application of technological solutions in the fight against money laundering—a systematic literature review. Appl Sci, 9(22):4800. https://doi.org/10.3390/app9224800https://doi.org/10.3390/app9224800

Soui M, Gasmi I, Smiti S, et al., 2019. Rule-based credit risk assessment model using multi-objective evolutionary algorithms. Expert Syst Appl, 126:144-157. https://doi.org/10.1016/j.eswa.2019.01.078https://doi.org/10.1016/j.eswa.2019.01.078

Sun SR, Wu B, Zhang ZX, et al., 2019. A hierarchical insurance recommendation framework using GraphOLAM approach. IEEE/ACM Int Conf on Advances in Social Networks Analysis and Mining, p.757-764. https://doi.org/10.1145/3341161.3345643https://doi.org/10.1145/3341161.3345643

Suzumura T, Zhou Y, Baracaldo N, et al., 2019. Towards federated graph learning for collaborative financial crimes detection. https://arxiv.org/abs/1909.12946https://arxiv.org/abs/1909.12946

Théate T, Ernst D, 2021. An application of deep reinforcement learning to algorithmic trading. Expert Syst Appl, 173:114632. https://doi.org/10.1016/j.eswa.2021.114632https://doi.org/10.1016/j.eswa.2021.114632

Wang DX, Lin JB, Cui P, et al., 2019. A semi-supervised graph attentive network for financial fraud detection. IEEE Int Conf on Data Mining, p.598-607. https://doi.org/10.1109/ICDM.2019.00070https://doi.org/10.1109/ICDM.2019.00070

Wang JY, Zhang Y, Tang K, et al., 2019. AlphaStock: a buying-winners-and-selling-losers investment strategy using interpretable deep reinforcement attention networks. Proc 25th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining, p.1900-1908. https://doi.org/10.1145/3292500.3330647https://doi.org/10.1145/3292500.3330647

Wang L, Li PP, Xiong K, et al., 2021. Modeling heterogeneous graph network on fraud detection: a community-based framework with attention mechanism. Proc 30th ACM Int Conf on Information & Knowledge Management, p.1959-1968. https://doi.org/10.1145/3459637.3482277https://doi.org/10.1145/3459637.3482277

Wang Z, Zhang W, Liu N, et al., 2021. Scalable rule-based representation learning for interpretable classification. Proc 35th Conf on Neural Information Processing Systems, p.30479-30491.

Wei D, Dash S, Gao T, et al., 2019. Generalized linear rule models. Proc 36th Int Conf on Machine Learning, p.6687-6696.

Wu J, Wang C, Xiong LD, et al., 2019. Quantitative trading on stock market based on deep reinforcement learning. Int Joint Conf on Neural Networks, p.1-8. https://doi.org/10.1109/IJCNN.2019.8851831https://doi.org/10.1109/IJCNN.2019.8851831

Wu ZH, Pan SR, Chen FW, et al., 2020. A comprehensive survey on graph neural networks. IEEE Trans Neur Netw Learn Syst, 32(1):4-24. https://doi.org/10.1109/TNNLS.2020.2978386https://doi.org/10.1109/TNNLS.2020.2978386

Xing E, 2018. SysML: on system and algorithm co-design for practical machine learning. Proc 24th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining, p.2880. https://doi.org/10.1145/3219819.3219934https://doi.org/10.1145/3219819.3219934

Xu BB, Shen HW, Sun BJ, et al., 2021. Towards consumer loan fraud detection: graph neural networks with role-constrained conditional random field. Proc AAAI Conf on Artif Intell, 35(5):4537-4545.

Xu H, Liu XR, Li YX, et al., 2021. To be robust or to be fair: towards fairness in adversarial training. Proc 38th Int Conf on Machine Learning, p.11492-11501.

Xu K, Fu CL, Zhang XL, et al., 2020. aDMSCN: a novel perspective for user intent prediction in customer service bots. Proc 29th ACM Int Conf on Information & Knowledge Management, p.2853-2860. https://doi.org/10.1145/3340531.3412683https://doi.org/10.1145/3340531.3412683

Yan C, Li YQ, Liu W, et al., 2020. An artificial bee colony-based kernel ridge regression for automobile insurance fraud identification. Neurocomputing, 393:115-125. https://doi.org/10.1016/j.neucom.2017.12.072https://doi.org/10.1016/j.neucom.2017.12.072

Yang F, Qiao YN, Huang C, et al., 2021a. An automatic credit scoring strategy (ACSS) using memetic evolutionary algorithm and neural architecture search. Appl Soft Comput, 113:107871. https://doi.org/10.1016/j.asoc.2021.107871https://doi.org/10.1016/j.asoc.2021.107871

Yang F, He K, Yang LX, et al., 2021b. Learning interpretable decision rule sets: a submodular optimization approach. Proc 34th Conf on Neural Information Processing Systems, p.27890-27902.

Yang S, Zhang ZQ, Zhou J, et al., 2021. Financial risk analysis for SMEs with graph-based supply chain mining. Proc 29th Int Joint Conf on Artificial Intelligence, p.4661-4667. https://doi.org/10.24963/ijcai.2020/643https://doi.org/10.24963/ijcai.2020/643

Zhang XW, Han YC, Xu W, et al., 2021. HOBA: a novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Inform Sci, 557:302-316. https://doi.org/10.1016/j.ins.2019.05.023https://doi.org/10.1016/j.ins.2019.05.023

Zhang YL, Zhou J, Zheng WH, et al., 2019. Distributed deep forest and its application to automatic detection of cash-out fraud. ACM Trans Intell Syst Technol, 10(5):55. https://doi.org/10.1145/3342241https://doi.org/10.1145/3342241

Zheng WB, Yan L, Gou C, et al., 2021. Federated meta-learning for fraudulent credit card detection. Proc 29th Int Joint Conf on Artificial Intelligence, p.4654-4660. https://doi.org/10.24963/ijcai.2020/642https://doi.org/10.24963/ijcai.2020/642

Zheng XL, Zhu MY, Li QB, et al., 2019. FinBrain: when finance meets AI 2.0. Front Inform Technol Electron Eng, 20(7):914-924. https://doi.org/10.1631/FITEE.1700822https://doi.org/10.1631/FITEE.1700822

Zhong QW, Liu Y, Ao X, et al., 2020. Financial defaulter detection on online credit payment via multi-view attributed heterogeneous information network. Web Conf, p.785-795. https://doi.org/10.1145/3366423.3380159https://doi.org/10.1145/3366423.3380159

Zhou ZH, Feng J, 2017. Deep forest: towards an alternative to deep neural networks. Proc 26th Int Joint Conf on Artificial Intelligence, p.3553-3559. https://doi.org/10.24963/ijcai.2017/497https://doi.org/10.24963/ijcai.2017/497

Zhu XQ, Ao X, Qin ZD, et al., 2021. Intelligent financial fraud detection practices in post-pandemic era. Innovation, 2(4):100176. https://doi.org/10.1016/j.xinn.2021.100176https://doi.org/10.1016/j.xinn.2021.100176

Zhu YC, Xi DB, Song BW, et al., 2020. Modeling users' behavior sequences with hierarchical explainable network for cross-domain fraud detection. Web Conf, p.928-938. https://doi.org/10.1145/3366423.3380172https://doi.org/10.1145/3366423.3380172