Applications of Deep Learning in Financial Intermediation: A Systematic Literature Review
Abstract In finance, an infinite amount of datais generated daily, which is important for decision-making in the business world. Consequently, there is a need to create models that help to process and interpret this data. Deep learning has demonstrated important advances in the processing of large amounts of data, and for this reason, the objective of this systematic review of literature corresponds to the search for applications, deep learning model and techniques that were used to solve problems in the financial area. For this purpose, out of 346 articles found, 20 were selected that met the inclusion and exclusion criteria corresponding to the research questions. Among the most common applications, models, and techniques were: prediction in market actions, sales forecasting, detection of fraud risks and tax evasion; with respect to the models, convolutional neural networks CNN and recurrent neural networks RNN were among the most executed; the ReLu and Sigmoid techniques turned out to be the most used in these models.
Keywords: deep learning, finance, machine learning, Convolutional Neural Network CNN, Recurrent Neural Network RNN
 Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. ™436–444, 2015.
 S. Gasparini et al., “Information Theoretic-Based Interpretation of a Deep Neural Network Approach in Diagnosing Psychogenic Non-Epileptic Seizures,” Entropy, vol. 20, no. 2, p. 43, 2018.
 D. Romo-Bucheli, A. Janowczyk, H. Gilmore, E. Romero, and A. Madabhushi, “A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers,” Cytom. Part A, vol. 91, no. 6, pp. 566–573, 2017.
 Y. Cao et al., “Improving Tuberculosis Diagnostics Using Deep Learning and Mobile Health Technologies among Resource-Poor and Marginalized Communities,” 2016 IEEE First Int. Conf. Connect. Heal. Appl. Syst. Eng. Technol., no. 1, pp. 274–281, 2016.
 F. Li, J. Zhang, C. Shang, D. Huang, E. Oko, and M. Wang, “Modelling of a postcombustion CO2capture process using deep belief network,” Appl. Therm. Eng., vol. 130, pp. 997–1003, 2018.
 L. G. Pang, K. Zhou, N. Su, H. Petersen, H. Stöcker, and X. N. Wang, “An equationof-state meter of quantum chromodynamics transition from deep learning,” Nat. Commun., vol. 9, no. 1, 2018.
 W. E, J. Han, and A. Jentzen, “Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations,” Communications in Mathematics and Statistics, vol. 5, no. 4. pp. 349–380, 2017.
 B. Kitchenham, “Procedures for performing systematic reviews,” Br. J. Manag., vol. 14, no. 0, pp. 207–222, 2003.
 S. Rönnqvist and P. Sarlin, “Bank distress in the news: Describing events through
deep learning,” Neurocomputing, vol. 264, pp. 57–70, Nov. 2017.
 Y. Zhao, J. Li, and L. Yu, “A deep learning ensemble approach for crude oil price
forecasting,” Energy Econ., vol. 66, pp. 9–16, 2017.
 W. Bao, J. Yue, and Y. Rao, “A deep learning framework for financial time series
using stacked autoencoders and long-short term memory,” PLoS One, vol. 12, no.
7, p. e0180944, Jul. 2017.
 A. Dingli and K. S. Fournier, “Financial Time Series Forecasting – A Deep Learning Approach,” Int. J. Mach. Learn. Comput., vol. 7, no. 5, pp. 118–122, 2017
 R. Ferreira, M. Braga, and V. Alves, Forecast in the Pharmaceutical Area – StatisticModels vs Deep Learning, vol. 746. Springer International Publishing, 2018.
 J. Lee, D. Jang, and S. Park, “Deep learning-based corporate performance prediction model considering technical capability,” Sustain., vol. 9, no. 6, pp. 1–12, 2017.
 Q. Yu, K. Wang, J. O. Strandhagen, and Y. W ang, “Application of Long Short-Term Memory Neural Network to Sales Forecasting in Retail—A Case Study,” pp. 11–17, 2018.
 Y. Kaneko, “A Deep Learning Approach for the Prediction of Retail Store Sales,” Icdmw, 2016.
 R. Akita, A. Yoshihara, T. Matsubara, and K. Uehara, “Deep learning for stock prediction using numerical and textual information,” 2016 IEEE/ACIS 15th Int. Conf. Comput. Inf. Sci. ICIS 2016 - Proc., 2016.
 O. Chang, I. Naranjo, C. Guerron, D. Criollo, J. Guerron, and G. Mosquera, “A Deep Learning Algorithm to Forecast Sales of Pharmaceutical Products,” no. August, 2017.
 R. Singh and S. Srivastava, “Stock prediction using deep learning,” Multimed. Tools Appl., vol. 76, no. 18, pp. 18569–18584, Sep. 2017.
 S. Sohangir, D. Wang, A. Pomeranets, and T. M. Khoshgoftaar, “Big Data: Deep Learning for financial sentiment analysis,” J. Big Data, vol. 5, no. 1, 2018.
 J. Sirignano, A. Sadhwani, and K. Giesecke, “Deep Learning for Mortgage Risk,” Ssrn, pp. 1–83, 2016.
 X. Ding, Y. Zhang, T. Liu, and J. Duan, “Deep Learning for Event-Driven Stock Prediction,” no. Ijcai, pp. 2327–2333, 2015.
 E. Chong, C. Han, and F. C. Park, “Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies,” Expert Syst. Appl., vol. 83, pp. 187–205, 2017.
 N. D. Goumagias, D. Hristu-Varsakelis, and Y. M. Assael, “Using deep Q-learning to understand the tax evasion behavior of risk-averse firms,” Expert Syst. Appl., vol. 101, pp. 258–270, 2018.
 H. Gunduz, Y. Yaslan, and Z. Cataltepe, “Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations,” Knowledge-Based Syst., vol. 137, pp. 138–148, 2017.
 M. Kraus and S. Feuerriegel, “Decision support from financial disclosures with deep neural networks and transfer learning,” Decis. Support Syst., vol. 104, pp. 38–48, 2017.
 C. Krauss, X. A. Do, and N. Huck, “Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500,” Eur. J. Oper. Res., vol. 259, no. 2, pp. 689–702, 2017.
 Y. Wang and W. Xu, “Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud,” Decis. Support Syst., vol. 105, pp. 87–95, 2018.