Automatic detection of relationships between banking operations using machine learning

In their daily business, bank branches should register their operations with several systems in order to share information with other branches and to have a central repository of records. In this way, information can be analysed and processed according to different requisites: fraud detection, accounting or legal requirements. Within this context, there is increasing use of big data and artificial intelligence techniques to improve customer experience. Our research focuses on detecting matches between bank operation records by means of applied intelligence techniques in a big data environment and business intelligence analytics. The business analytics function allows relationships to be established and comparisons to be made between variables from the bank's daily business. Finally, the results obtained show that the framework is able to detect relationships between banking operation records, starting from not homogeneous information and taking into account the large volume of data involved in the process. (C) 2019 Elsevier Inc. All rights reserved.This work was supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R)

Comparison of machine learning approaches for classification of invoices

Machine learning has become one of the leading sciences governing modern world. Various disciplines specifically neural networks have recently gained a lot of attention due to its widespread applications. With the recent advances in the technology the resulting big data has augmented the need of bigger means of storage, analysis and henceforth utilization. This not only implies the efficient use of available techniques but suggests surge in the development of new algorithms and techniques. In this project, three different machine learning approaches were implemented utilizing the open source library of keras on TensorFlow as a proof of concept for the task of intelligent invoice automation. The performance of these approaches for improved business on data of invoices has been analysed using the data of two customers with two target attributes per customer as a dataset. The behaviour of neural network hyper-parameters using matplotlib and TensorBoard was empirically calculated and investigated. As part of the first approach, the standard way of implementing predictive algorithm using neural network was followed. Moreover, the hyper-parameters search space was fine-tuned, and the resulting model was studied by grid search on those hyper-parameters. This strategy of hyper-parameters was followed in the next two approaches as well. In the second approach, not only further possible improvement in prediction accuracy is achieved but also the dependency between the two target attributes by using multi-task learning was determined. As per the third implemented approach, the use of continual learning on invoices for postings was analysed. This investigation, that involves the comparison of varied machine learning approaches has broad significance in approving the currently available algorithms for handling such data and suggests means for improvement as well. It holds great prospects, including but not limited to future implementation of such approaches in the domain of finance towards improved customer experience, fraud detection and ease in the assessments of assets etc

Examination of the Strategic Vision of Banks in Digitalization and the Effects of Innovation on Performance and Artificial Intelligence Perception

This study examines the effects of strategic vision and digital innovation on the performance and perceptions of digital natives regarding artificial intelligence (AI) in banks. This is due to the increased use of mobile banking applications with the proliferation of the internet. An online questionnaire was administered to 603 experts working in bank headquarters. The collected data were analyzed using the SmartPLS 3.3.3 software. Banks that adopt digital business strategies can gain a significant competitive advantage. To successfully transform and compete, banks need to address customer demands in the areas of digitalization, innovation, and mobile banking. This research is original in its evaluation of the strategic visions and digital innovations of banks, which play a crucial role in the service sector, with implications for finance, innovation, and mobile applications. The results demonstrate the positive effects of strategic vision and digital innovation on performance and digital natives' perceptions of AI. The hypotheses support the importance of digitalization and innovation for banks