How Banks Can Better Serve Their Customers Through Artificial Techniques
Thanks to the big data revolution and advanced computational capabilities, companies have never had such a deep access to customer data. This is allowing organizations to interpret, understand, and forecast customer behaviors as never before. Artificial Intelligence (AI) algorithms will play a pivotal role in transforming business intelligence into a fully predictive probabilistic framework. AI will be able to radically transform or automate numerous functions within companies, from pricing, budget allocation, fraud detection and security. This chapter will present some approaches on advanced analytics and provide some examples from the financial sector on how AI is helping institutions refine small business credit scoring, understand online behavior and improve customer service. Further, we will also explore how integration with traditional business processes can work and how organizations can then take advantage of the data driven approach.
- 1.Y. N. Harari, Homos Deus, Harvill Secker, 2016.Google Scholar
- 4.I. Goodfellow, Y. Bengio und A. Courville, “Deep Learning,” MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org.
- 5.A. Vieira, Deep Neural Networks: review and business applications, Apress (in preperation).Google Scholar
- 6.A. Krizhevsky, I. Sutskever und G. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, Bd. 25, Curran Associates, 2012, pp. 1106–1114.Google Scholar
- 7.The Guardian, “RBS to cut 550 jobs as part of plan to automate investment advice,” 03 2016. [Online]. Available: https://www.theguardian.com/business/2016/mar/13/rbs-royal-bank-scotland-cut-550-jobs-automating-investment-advice.
- 9.S. Agarwal, S. Chomsisengphet, C. Liu und N. Souleles, “Benefits of relationship banking: Evidence from consumer credit markets,” J. Social Sci. Res. Netw., Rochester, NY, USA, 05 2009. [Online]. Available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1647019.
- 10.Convolutional LSTM Network, “A Machine Learning Approach for Precipitation Nowcasting,” 2015. [Online]. Available: http://arxiv.org/pdf/1506.04214v2.pdf.
- 11.Finextra, “Deutsche Bank crowdstorms AI ideas,” 05 2016. [Online]. Available: https://www.finextra.com/newsarticle/28855/deutsche-bank-crowdstorms-ai-ideas.
- 12.Finextra, “Absa to trial AI-driven chatbots to answer customer queries,” 04 2016. [Online]. Available: https://www.finextra.com/newsarticle/28794/absa-to-trial-ai-driven-chatbots-to-answer-customer-queries.
- 13.McKinsey Global Inst., Big Data, The Next Frontier for Innovation, Competition, Productivity, New York, NY, USA, 2011.Google Scholar
- 18.Kasisto.com, “ai-driven-virtual-assistant-from-kasisto-powers-indias-first-mobile-only-bank,” 04 2016. [Online]. Available: http://www.kasisto.com.
- 19.Indian mobile-only bank handles customer service with chatbots, 2016. http://www.forwardlook.com/indian-mobile-only-bank-handles-customer-service-with-chatbots/
- 20.techncrunch.com, penny-is-a-chat-based-personal-finance-coach, 2015.Google Scholar
- 21.do-your-banking-with-a-chatbot, Massachusetts: MIT Technology Review, 2016.Google Scholar
- 22.A. Radford, L. Metz und S. Chintala, “Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,” corr/RadfordMC15, 2015. [Online]. Available: https://arxiv.org/abs/1511.06434.
- 23.nature.com, 2016. [Online]. Available: http://www.nature.com/news/google-ai-algorithm-masters-ancient-game-of-go-1.19234.