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Annals of Data Science
Published in: Annals of Data Science 3/2020

29-06-2020

Deep Learning and Implementations in Banking

Authors: Hossein Hassani, Xu Huang, Emmanuel Silva, Mansi Ghodsi

Published in: Annals of Data Science | Issue 3/2020

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Abstract

Data-driven technologies have been changing every aspect of human life and the fast-developing banking sector with its data-rich nature has become the implementation field of these fast-evolving technologies. Deep learning, as one of the emerging technologies in recent years, has also been inevitably adopted for various improvements in banking. To the best of our knowledge, there is no comprehensive literature review, which focuses on specifically deep learning and its implementations in banking. Therefore, this paper investigates the deep learning technology in-depth and summarizes the relevant applications in banking so to contribute to the existing literature. Moreover, by providing a reliable and up-to-date review, it is also aimed to serve as the one-stop repository for banks and researchers who are interested in embracing deep learning, whilst bringing insights for the directions of future research and implementation.
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Literature
1.
2.
3.
Xu Z, Shi Y (2015) Exploring big data analysis: fundamental scientific problems. Ann Data Sci 2:363–372
4.
Shi Y, Shan Z, Li J, Jianping L, Fang Y (2017) How China deals with big data. Ann Data Sci 4:433–440
5.
Hassani H, Huang X, Silva ES, Ghodsi M (2016) A review of data mining applications in crime. Stat Anal Data Min ASA Data Sci J 9(3):139–154
6.
Hassani H, Huang X, Ghodsi M (2018) Big data and causality. Ann Data Sci 5(2):133–156
7.
Olson D, Shi Y (2007) Introduction to business data mining. McGraw-Hill/Irwin, New York
8.
Shi Y, Tian YJ, Kou G, Peng Y, Li JP (2011) Optimization based data mining: theory and applications. Springer, London
9.
Shi Y (2014) Big Data: history, current status, and challenges going forward. Bridge US Natl Acad Eng 44(4):6–11
10.
Hassani H, Silva E (2018) Big Data: a big opportunity for the petroleum and petrochemical industry. OPEC Energy Rev 42(1):74–89
11.
Hassani H, Huang X, Silva E (2019) Big Data and climate change. Big Data Cognit Comput 3(1):12
12.
Hassani H, Silva ES, Unger S, TajMazinani M, Mac Feely S (2020) Artificial Intelligence (AI) or Intelligence Augmentation (IA): what Is the Future? AI 1:143–155
13.
Hassani H, Huang X, Silva E (2018) Digitalisation and big data mining in banking. Big Data Cognit Comput 2(3):18
14.
Hassani H, Huang X, Silva E (2018) Banking with blockchain-ed big data. J Manag Anal 5(4):256–275
15.
Hormozi AM, Giles S (2004) Data mining: a competitive weapon for banking and retail industries. Inf Syst Manag 21(2):62–71
16.
Chitra K, Subashini B (2013) Data mining techniques and its applications in banking sector. Int J Emerg Technol Adv Eng 3(8):219–226
17.
Jayasree V, Balan RVS (2013) A review on data mining in banking sector. Am J Appl Sci 10(10):1160
18.
Chye KH, Gerry CKL (2002) Data mining and customer relationship marketing in the banking industry. Singap Manag Rev 24(2):1–28
19.
Aburrous M, Hossain MA, Dahal K, Thabtah F (2010) Intelligent phishing detection system for e-banking using fuzzy data mining. Expert Syst Appl 37(12):7913–7921
20.
Ince H, Aktan B (2009) A comparison of data mining techniques for credit scoring in banking: a managerial perspective. J Bus Econ Manag 10(3):233–240
21.
Sun N, Morris JG, Xu J, Zhu X, Xie M (2014) iCARE: a framework for big data-based banking customer analytics. IBM J Res Dev 58(5/6):4:1–4:9
22.
Srivastava U, Gopalkrishnan S (2015) Impact of big data analytics on banking sector: learning for Indian banks. Procedia Comput Sci 50:643–652
23.
Tsai CF, Chen ML (2010) Credit rating by hybrid machine learning techniques. Appl Soft Comput 10(2):374–380
24.
Khandani AE, Kim AJ, Lo AW (2010) Consumer credit-risk models via machine-learning algorithms. J Bank Finance 34(11):2767–2787
25.
Carr M, Ravi V, Reddy GS, Veranna D (2013) Machine learning techniques applied to profile mobile banking users in India. Int J Inf Syst Serv Sect 5(1):82–92
26.
Smeureanu I, Ruxanda G, Badea LM (2013) Customer segmentation in private banking sector using machine learning techniques. J Bus Econ Manag 14(5):923–939
27.
Hassani H, Huang X, Silva ES (2019) Fusing Big Data, blockchain, and cryptocurrency. In: Fusing Big Data, blockchain and cryptocurrency, Palgrave Pivot
28.
Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55(10):78–87
29.
30.
Alpaydin E (2020) Introduction to machine learning. MIT Press, Cambridge
31.
Shalev-Shwartz S, Ben-David S (2014) Understanding machine learning: from theory to algorithms. Cambridge University Press, Cambridge
32.
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
33.
Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48
34.
Zhu XX, Tuia D, Mou L, Xia GS, Zhang L, Xu F, Fraundorfer F (2017) Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci Remote Sens Mag 5(4):8–36
35.
Miotto R, Wang F, Wang S, Jiang X, Dudley JT (2018) Deep learning for healthcare: review, opportunities and challenges. Brief Bioinf 19(6):1236–1246
36.
Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller PA (2019) Deep learning for time series classification: a review. Data Min Knowl Discov 33(4):917–963
37.
Fan C, Xiao F, Zhao Y (2017) A short-term building cooling load prediction method using deep learning algorithms. Appl Energy 195:222–233
38.
Socher R, Lin CC, Manning C, Ng AY (2011) Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 129–136
39.
Mikolov T, Karafiát M, Burget L, Černockỳ J, Khudanpur S (2010) Recurrent neural network based language model. In: Eleventh annual conference of the international speech communication association
40.
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
41.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9
42.
43.
Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
44.
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
45.
Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2014) Decaf: a deep convolutional activation feature for generic visual recognition. In: International conference on machine learning, pp 647–655
46.
Salakhutdinov R, Hinton G (2009) Deep boltzmann machines. In: Artificial intelligence and statistics, pp 448–455
47.
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
48.
Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, Shyu ML, Chen SC, Iyengar SS (2018) A survey on deep learning: algorithms, techniques, and applications. ACM Comput Surv (CSUR) 51(5):1–36
49.
Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26
50.
Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Advances in neural information processing systems, pp 153–160
51.
Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on Machine learning, pp 1096–1103
52.
53.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680
54.
Bose I, Chen X (2009) Quantitative models for direct marketing: a review from systems perspective. Eur J Oper Res 195(1):1–16
55.
Sing’oei L, Wang J (2013) Data mining framework for direct marketing: a case study of bank marketing. Int J Comput Sci Issues 10((2 Part 2)):198
56.
Kim KH, Lee CS, Jo SM, Cho SB (2015) Predicting the success of bank telemarketing using deep convolutional neural network. In: 2015 7th international conference of soft computing and pattern recognition (SoCPaR), IEEE, pp 314–317
57.
Zakaryazad A, Duman E (2016) A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing. Neurocomputing 175:121–131
58.
Moro S, Cortez P, Rita P (2014) A data-driven approach to predict the success of bank telemarketing. Decis Support Syst 62:22–31
59.
60.
Ładyżyński P, Żbikowski K, Gawrysiak P (2019) Direct marketing campaigns in retail banking with the use of deep learning and random forests. Expert Syst Appl 134:28–35
61.
Ogwueleka FN, Misra S, Colomo-Palacios R, Fernandez L (2015) Neural network and classification approach in identifying customer behavior in the banking sector: a case study of an international bank. Hum Factors Ergon Manuf Serv Ind 25(1):28–42
62.
Davies F, Moutinho L, Curry B (1996) ATM user attitudes: a neural network analysis. Mark Intell Plan 14:26–32
63.
Hsieh NC (2004) An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Syst Appl 27(4):623–633
64.
Laukkanen T, Pasanen M (2008) Mobile banking innovators and early adopters: How they differ from other online users? J Financ Serv Mark 13(2):86–94
65.
Wang G, Bai Y, Sun Y (2018) Application of BP neural network algorithm in bank customer hierarchy system. In: 2017 3rd international forum on energy, environment science and materials (IFEESM 2017), Atlantis Press
66.
Zhou X, Bargshady G, Abdar M, Tao X, Gururajan R, Chan KC (2019) A case study of predicting banking customers behaviour by using data mining. In: 2019 6th international conference on behavioral, economic and socio-cultural computing (BESC), IEEE, pp 1–6
67.
Vieira A, Sehgal A (2018) How banks can better serve their customers through artificial techniques. In: Digital marketplaces unleashed, Springer, Berlin, pp 311–326
68.
Krishnan K (2020) Chapter 7—Banking industry applications and usage. Building Big Data Applications, Academic Press, pp 127–144
69.
70.
Quah JT, Chua YW (2019) Chatbot Assisted Marketing in Financial Service Industry. In: International conference on services computing, Springer, pp 107–114
71.
Przegalinska A, Ciechanowski L, Stroz A, Gloor P, Mazurek G (2019) In bot we trust: a new methodology of chatbot performance measures. Bus Horiz 62(6):785–797
72.
Spanoudes P, Nguyen T (2017) Deep learning in customer churn prediction: unsupervised feature learning on abstract company independent feature vectors. arXiv preprint arXiv:​1703.​03869
73.
Mirashk H, Albadvi A, Kargari M, Javide M, Eshghi A, Shahidi G (2019) Using RNN to predict customer behavior in high volume transactional data. In: International congress on high-performance computing and big data analysis, Springer, pp 394–405
74.
De Caigny A, Coussement K, De Bock KW, Lessmann S (2019) Incorporating textual information in customer churn prediction models based on a convolutional neural network. Int J Forecast (In Press)
75.
Leo M, Sharma S, Maddulety K (2019) Machine learning in banking risk management: a literature review. Risks 7(1):29
76.
77.
Lin WY, Hu YH, Tsai CF (2011) Machine learning in financial crisis prediction: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 42(4):421–436
78.
Sezer OB, Gudelek MU, Ozbayoglu AM (2020) Financial time series forecasting with deep learning: a systematic literature review: 2005–2019. Appl Soft Comput 90:106181
79.
Karisma H, Widyantoro DH (2016) Comparison study of neural network and deep neural network on repricing GAP prediction in Indonesian conventional public bank. In: 2016 6th international conference on system engineering and technology (ICSET), IEEE, pp 116–122
80.
Culkin R, Das SR (2017) Machine learning in finance: the case of deep learning for option pricing. J Invest Manag 15(4):92–100
81.
Weigand A (2019) Machine learning in empirical asset pricing. Financ Mark Portf Manag 33(1):93–104
82.
Chen Y, Rabbani RM, Gupta A, Zaki MJ (2017) Comparative text analytics via topic modeling in banking. In: 2017 IEEE symposium series on computational intelligence (SSCI), IEEE, pp 1–8
83.
Rönnqvist S, Sarlin P (2017) Bank distress in the news: describing events through deep learning. Neurocomputing 264:57–70
84.
Mai F, Tian S, Lee C, Ma L (2019) Deep learning models for bankruptcy prediction using textual disclosures. Eur J Oper Res 274(2):743–758
85.
Qu Y, Quan P, Lei M, Shi Y (2019) Review of bankruptcy prediction using machine learning and deep learning techniques. Procedia Comput Sci 162:895–899
86.
Vo NN, He X, Liu S, Xu G (2019) Deep learning for decision making and the optimization of socially responsible investments and portfolio. Decis Support Syst 124:113097
87.
Luo C, Wu D, Wu D (2017) A deep learning approach for credit scoring using credit default swaps. Eng Appl Artif Intell 65:465–470
88.
89.
Addo PM, Guegan D, Hassani B (2018) Credit risk analysis using machine and deep learning models. Risks 6(2):38
90.
Kvamme H, Sellereite N, Aas K, Sjursen S (2018) Predicting mortgage default using convolutional neural networks. Expert Syst Appl 102:207–217
91.
Gomez JA, Arevalo J, Paredes R, Nin J (2018) End-to-end neural network architecture for fraud scoring in card payments. Pattern Recognit Lett 105:175–181
92.
Fu K, Cheng D, Tu Y, Zhang L (2016) Credit card fraud detection using convolutional neural networks. In: International conference on neural information processing, Springer, pp 483–490
93.
Zhang Z, Zhou X, Zhang X, Wang L, Wang P (2018) A model based on convolutional neural network for online transaction fraud detection. Secur Commun Netw 2:1–9
94.
Kazemi Z, Zarrabi H (2017) Using deep networks for fraud detection in the credit card transactions. In: 2017 IEEE 4th international conference on knowledge-based engineering and innovation (KBEI), IEEE, pp 0630–0633
95.
Zamini M, Montazer G (2018) Credit card fraud detection using autoencoder based clustering. In: 2018 9th international symposium on telecommunications (IST), IEEE, pp 486–491
96.
Pumsirirat A, Yan L (2018) Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine. Int J Adv Comput Sci Appl 9(1):18–25
Metadata
Title
Deep Learning and Implementations in Banking
Authors
Hossein Hassani
Xu Huang
Emmanuel Silva
Mansi Ghodsi
Publication date
29-06-2020
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 3/2020
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-020-00300-1

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