Deep Learning and Implementations in Banking

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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References

  1. Saini A, Sharma A (2019) Predicting the unpredictable: an application of machine learning algorithms in indian stock market. Ann Data Sci. https://doi.org/10.1007/s40745-019-00230-7ArticleGoogle Scholar
  2. Ahmed M, Najmul Islam AKM (2020) Deep learning: hope or hype. Ann Data Sci. https://doi.org/10.1007/s40745-019-00237-0ArticleGoogle Scholar
  3. Xu Z, Shi Y (2015) Exploring big data analysis: fundamental scientific problems. Ann Data Sci 2:363–372 Google Scholar
  4. Shi Y, Shan Z, Li J, Jianping L, Fang Y (2017) How China deals with big data. Ann Data Sci 4:433–440 Google Scholar
  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 Google Scholar
  6. Hassani H, Huang X, Ghodsi M (2018) Big data and causality. Ann Data Sci 5(2):133–156 Google Scholar
  7. Olson D, Shi Y (2007) Introduction to business data mining. McGraw-Hill/Irwin, New York Google Scholar
  8. Shi Y, Tian YJ, Kou G, Peng Y, Li JP (2011) Optimization based data mining: theory and applications. Springer, London Google Scholar
  9. Shi Y (2014) Big Data: history, current status, and challenges going forward. Bridge US Natl Acad Eng 44(4):6–11 Google Scholar
  10. Hassani H, Silva E (2018) Big Data: a big opportunity for the petroleum and petrochemical industry. OPEC Energy Rev 42(1):74–89 Google Scholar
  11. Hassani H, Huang X, Silva E (2019) Big Data and climate change. Big Data Cognit Comput 3(1):12 Google Scholar
  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 Google Scholar
  13. Hassani H, Huang X, Silva E (2018) Digitalisation and big data mining in banking. Big Data Cognit Comput 2(3):18 Google Scholar
  14. Hassani H, Huang X, Silva E (2018) Banking with blockchain-ed big data. J Manag Anal 5(4):256–275 Google Scholar
  15. Hormozi AM, Giles S (2004) Data mining: a competitive weapon for banking and retail industries. Inf Syst Manag 21(2):62–71 Google Scholar
  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 Google Scholar
  17. Jayasree V, Balan RVS (2013) A review on data mining in banking sector. Am J Appl Sci 10(10):1160 Google Scholar
  18. Chye KH, Gerry CKL (2002) Data mining and customer relationship marketing in the banking industry. Singap Manag Rev 24(2):1–28 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  22. Srivastava U, Gopalkrishnan S (2015) Impact of big data analytics on banking sector: learning for Indian banks. Procedia Comput Sci 50:643–652 Google Scholar
  23. Tsai CF, Chen ML (2010) Credit rating by hybrid machine learning techniques. Appl Soft Comput 10(2):374–380 Google Scholar
  24. Khandani AE, Kim AJ, Lo AW (2010) Consumer credit-risk models via machine-learning algorithms. J Bank Finance 34(11):2767–2787 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  29. Grossfeld B (2020) Deep learning vs machine learning: a simple way to understand the difference. Available online: https://www.zendesk.com/blog/machine-learning-and-deep-learning/, Accessed 20 Mar 2020
  30. Alpaydin E (2020) Introduction to machine learning. MIT Press, Cambridge Google Scholar
  31. Shalev-Shwartz S, Ben-David S (2014) Understanding machine learning: from theory to algorithms. Cambridge University Press, Cambridge Google Scholar
  32. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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. Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  59. Yan C (2018) Convolutional Neural Network on a structured bank customer data. Towards data science. Available online: https://towardsdatascience.com/convolutional-neural-network-on-a-structured-bank-customer-data-358e6b8aa759, Accessed on 25 Mar 2020
  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 Google Scholar
  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 Google Scholar
  62. Davies F, Moutinho L, Curry B (1996) ATM user attitudes: a neural network analysis. Mark Intell Plan 14:26–32 Google Scholar
  63. Hsieh NC (2004) An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Syst Appl 27(4):623–633 Google Scholar
  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 Google Scholar
  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. Marous J (2018) Meet 11 of the most interesting chatbots in banking. The Financial Brand. Available online: https://thefinancialbrand.com/71251/chatbots-banking-trends-ai-cx/, Accessed on 26 Mar 2020
  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 Google Scholar
  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 Google Scholar
  76. Petropoulos A, Siakoulis V, Stavroulakis E, Vlachogiannakis NE (2020) Predicting bank insolvencies using machine learning techniques. Int J Forecast. https://doi.org/10.1016/j.ijforecast.2019.11.005 In Press ArticleGoogle Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  81. Weigand A (2019) Machine learning in empirical asset pricing. Financ Mark Portf Manag 33(1):93–104 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar
  88. Sirignano J, Sadhwani A, Giesecke K (2018) Deep learning for mortgage risk. Available at: https://doi.org/10.2139/ssrn.2799443
  89. Addo PM, Guegan D, Hassani B (2018) Credit risk analysis using machine and deep learning models. Risks 6(2):38 Google Scholar
  90. Kvamme H, Sellereite N, Aas K, Sjursen S (2018) Predicting mortgage default using convolutional neural networks. Expert Syst Appl 102:207–217 Google Scholar
  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 Google Scholar
  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 Google Scholar
  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 Google Scholar

Author information

Authors and Affiliations

  1. Research Institute for Energy Management and Planning, University of Tehran, Tehran, Iran Hossein Hassani & Mansi Ghodsi
  2. Department of Strategic Management and Marketing, De Montfort University, Leicester, UK Xu Huang
  3. London College of Fashion, University of the Arts London, London, UK Emmanuel Silva
  1. Hossein Hassani