Authors: Olufunke Oluyemi Sarumi; Bolanle Adefowoke Ojokoh; Oluwafemi Abimbola Sarumi; Olumide Sunday Adewale
Addresses: Department of Computer Science, Federal University of Technology, Akure, Nigeria ' Department of Information Systems, Federal University of Technology, Akure, Nigeria ' Department of Computer Science, Federal University of Technology, Akure, Nigeria ' Department of Computer Science, Federal University of Technology, Akure, Nigeria
Abstract: Account reconciliation has become a daunting task for many financial organisations due to the heterogeneity of data involved in the accounts' reconciliation process - coupled with the recent data deluge in many accounting firms. Many organisations are using a heuristic-based algorithm for their account reconciliation process while in some firms the process is completely manual. These methods are already inundated and were no longer efficient in light of the recent data explosion and are prone to lots of errors that could expose the organisations to several financial risks. In this regard, there is a need to develop a robust financial data analytic algorithm that can effectively handle the account reconciliation needs of financial organisations. In this paper, we propose a computational data analytic model that provides an efficient solution to the account reconciliation bottlenecks in financial organisations. Evaluation results show the effectiveness of our data analytic model for enhancing faster decision making in financial account reconciliation systems.
Keywords: accounts reconciliation; financial analytics; functions; fraud; big data.
International Journal of Business Intelligence and Data Mining, 2022 Vol.21 No.2, pp.171 - 189
Received: 08 Jul 2020
Accepted: 16 Feb 2021
Published online: 11 Aug 2022 *