FinBrain 2.0: when finance meets trustworthy AI

Jun ZHOU; Chaochao CHEN; Longfei LI; Zhiqiang ZHANG; Xiaolin ZHENG

doi:10.1631/FITEE.2200039

Your Location：

Home >

Browse articles >

FinBrain 2.0: when finance meets trustworthy AI

Regular Papers | Updated：2022-12-16

- FinBrain 2.0: when finance meets trustworthy AI
- 金融大脑2.0：当金融遇到可信人工智能
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 12, Pages: 1747-1764(2022)
- Affiliations：
  
  1.College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  2.Ant Group, Hangzhou 310027, China
- Author bio：
  
  E-mail: jun.zhoujun@antfin.com;
  zjuccc@zju.edu.cn;
  longyao.llf@antfin.com;
  lingyao.zzq@antfin.com;
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2200039
  CLC： TP183
- Received：30 January 2022，
  
  Accepted：03 August 2022，
  
  Published Online：30 September 2022，
  
  Published：0 December 2022
- Accepted：
Scan QR Code
Jun ZHOU, Chaochao CHEN, Longfei LI, et al. FinBrain 2.0: when finance meets trustworthy AI[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1747-1764.
DOI：

Jun ZHOU, Chaochao CHEN, Longfei LI, et al. FinBrain 2.0: when finance meets trustworthy AI[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1747-1764. DOI： 10.1631/FITEE.2200039.

摘要

人工智能通过从数据中识别隐藏模式以提高金融决策质量，从而加速金融服务的发展。然而，除了通常需要的属性，如模型准确性，金融服务还需要可信赖的人工智能，但其属性尚未充分实现。这些可信人工智能的属性是可解释性、公平性和包容性、稳健性和安全性，以及隐私保护。在本文中，我们回顾人工智能应用于金融服务各领域的最新进展和局限性，包括风险管理、欺诈检测、财富管理、个性化服务和监管技术。基于这些进展和局限性，介绍了金融大脑2.0，一个走向可信人工智能的研究框架。我们认为，在金融服务中，我们离真正可信人工智能还有很长的路要走，并呼吁人工智能和金融业的社区一同努力。

Abstract

Artificial intelligence (AI) has accelerated the advancement of financial services by identifying hidden patterns from data to improve the quality of financial decisions. However

in addition to commonly desired attributes

such as model accuracy

financial services demand trustworthy AI with properties that have not been adequately realized. These properties of trustworthy AI are interpretability

fairness and inclusiveness

robustness and security

and privacy protection. Here

we review the recent progress and limitations of applying AI to various areas of financial services

including risk management

fraud detection

wealth management

personalized services

and regulatory technology. Based on these progress and limitations

we introduce FinBrain 2.0

a research framework toward trustworthy AI. We argue that we are still a long way from having a truly trustworthy AI in financial services and call for the communities of AI and financial industry to join in this effort.

关键词

Keywords

references

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.2021.3105259 https://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.2021.116261 https://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.2021.113492 https://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/10.1080/01605682.2021.1919035 https://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/10.14778/3352063.3352126 https://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.2019.05.042 https://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/BigData47090.2019.9006115 https://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 42 nd Int ACM SIGIR Conf on Research and Development in Information Retrieval , p. 1265 - 1268 . https://doi.org/10.1145/3331184.3331370 https://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 27 th ACM SIGKDD Conf on Knowledge Discovery & Data Mining , p. 2652 - 2662 . https://doi.org/10.1145/3447548.3467210 https://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-z https://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.v34i01.5371 https://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.2021.08.017 https://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.2021.116311 https://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 23 rd ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining , p. 797 - 806 . https://doi.org/10.1145/3097983.3098095 https://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 26 th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining , p. 492 - 502 . https://doi.org/10.1145/3394486.3403092 https://doi.org/10.1145/3394486.3403092

Dash S , Günlük O , Wei D , 2018 . Boolean decision rules via column generation . Proc 32 nd 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.2020.106263 https://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.2020.113766 https://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.2019.100121 https://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.2021.06.053 https://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 36 th 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.2017.12.030 https://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-y https://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.2020.106883 https://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.pdf https://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.2018.10.039 https://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/s10115-022-01653-0 https://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.2019.08.028 https://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.12359 https://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/s40745-020-00300-1 https://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/s40537-019-0181-8 https://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/s40804-021-00224-0 https://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.v33i01.3301946 https://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_v2 https://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/10.1145/3442442.3451372 https://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.12733 https://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.2019.03.042 https://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.2021.12.024 https://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.2021.107391 https://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/s11227-019-03101-3 https://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.2020.2968894 https://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/10.1145/3442442.3451147 https://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.2021.114750 https://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 42 nd Int ACM SIGIR Conf on Research and Development in Information Retrieval , p. 1181 - 1184 . https://doi.org/10.1145/3331184.3331372 https://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 30 th Int Joint Conf on Artificial Intelligence , p. 3670 - 3676 . https://doi.org/10.24963/ijcai.2021/505 https://doi.org/10.24963/ijcai.2021/505

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

Liu ZQ , Chen CC , Yang XX , et al. , 2018 . Heterogeneous graph neural networks for malicious account detection . Proc 27 th ACM Int Conf on Information and Knowledge Management , p. 2077 - 2085 . https://doi.org/10.1145/3269206.3272010 https://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.pdf https://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/10.1145/3457607 https://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.2017.12 https://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.2020.106384 https://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.2019.10.050 https://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/10.1145/3239550 https://doi.org/10.1145/3239550

Sawhney R , Mathur P , Mangal A , et al. , 2020 . Multimodal multi-task financial risk forecasting . Proc 28 th ACM Int Conf on Multimedia , p. 456 - 465 . https://doi.org/10.1145/3394171.3413752 https://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/app9224800 https://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.2019.01.078 https://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/10.1145/3341161.3345643 https://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/1909.12946 https://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.2021.114632 https://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.2019.00070 https://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 25 th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining , p. 1900 - 1908 . https://doi.org/10.1145/3292500.3330647 https://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 30 th ACM Int Conf on Information & Knowledge Management , p. 1959 - 1968 . https://doi.org/10.1145/3459637.3482277 https://doi.org/10.1145/3459637.3482277

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

Wei D , Dash S , Gao T , et al. , 2019 . Generalized linear rule models . Proc 36 th 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.2019.8851831 https://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.2020.2978386 https://doi.org/10.1109/TNNLS.2020.2978386

Xing E , 2018 . SysML: on system and algorithm co-design for practical machine learning . Proc 24 th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining , p. 2880 . https://doi.org/10.1145/3219819.3219934 https://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 38 th 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 29 th ACM Int Conf on Information & Knowledge Management , p. 2853 - 2860 . https://doi.org/10.1145/3340531.3412683 https://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.2017.12.072 https://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.2021.107871 https://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 34 th 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 29 th Int Joint Conf on Artificial Intelligence , p. 4661 - 4667 . https://doi.org/10.24963/ijcai.2020/643 https://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.2019.05.023 https://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/10.1145/3342241 https://doi.org/10.1145/3342241

Zheng WB , Yan L , Gou C , et al. , 2021 . Federated meta-learning for fraudulent credit card detection . Proc 29 th Int Joint Conf on Artificial Intelligence , p. 4654 - 4660 . https://doi.org/10.24963/ijcai.2020/642 https://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.1700822 https://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/10.1145/3366423.3380159 https://doi.org/10.1145/3366423.3380159

Zhou ZH , Feng J , 2017 . Deep forest: towards an alternative to deep neural networks . Proc 26 th Int Joint Conf on Artificial Intelligence , p. 3553 - 3559 . https://doi.org/10.24963/ijcai.2017/497 https://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.2021.100176 https://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/10.1145/3366423.3380172 https://doi.org/10.1145/3366423.3380172

Views

543

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

No data

Related Author

Min Gao

Shutong Chen

Yangbo Gao

Zhenhua Zhang

Yu Chen

Yupeng Li

Qiongzan Ye

Xin Wang

Related Institution

School of Computer Science, Fudan University

Shanghai Key Lab of Inteilligent Information Processing, Fudan University

Meituan

Department of Interactive Media, Hong Kong Baptist University

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰