Abstract
Big data is a form of data with increased volume, difficult to analyze, process, and store using traditional database technologies. It has long been adopted in business and finance where a large number of bank transaction are executed daily. The emergence of big data in banking industry results to large proportion of technical improvements in the industry. However, its processing causes disruption in the banking industry. Big data analytics is the process that involves using algorithms and software tools to extract useful business information from the dataset. This study adopts big data analytics process to investigates the disruption due to big data processing in the banking industry. The study identifies, acquired, and extracted dataset of the banking industry which was analyzed using MapReduce based fraud committed due to processing of large amount of data. findings show that government employee commit more crime in comparison with the private sector employees. Finally, based on customers gender, the male employees commit most of the fraud in both government and private sector.
Highlights
Big data is a form of data with increased volume, difficult to analyze, process and store with traditional database technologies (Hashem, Yaqoob et al 2015)
The review conducted by this study shows inexistence of the studies from the literature that focus on investigating the disruptions in the banking industry due to big data processing
This research proceeds to fill the gap by conducting the big data analytics process on the dataset identified from the banking industry to examine disruptions due processing of large amount of data
Summary
Big data is a form of data with increased volume, difficult to analyze, process and store with traditional database technologies (Hashem, Yaqoob et al 2015). Big data analytics is the process that involves using algorithms and software tools to extract useful business information from the dataset(Kugel 2013). It helps the banking industry in effective decision-making, business performance and risk management. This research proceeds to fill the gap by conducting the big data analytics process on the dataset identified from the banking industry to examine disruptions due processing of large amount of data. The study finding would help the banking industry prevent fraud occurrence from the customers because of processing and analytics of big data. The authors engaged in discussion on the identified research domains and consensus was achieved by the authors to investigate the impact of big data in the banking industry. TOTAL_OUT 544662,41 790008,58 0 372,9 26332,59 460809,68 216334,55 491429,11 10455,56 12235,07 0 3597,54 114,82
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