Financial accounts reconciliation system using enhanced mapping algorithm

作     者：Sarumi, Olufunke Oluyemi Ojokoh, Bolanle Adefowoke Sarumi, Oluwafemi Abimbola Adewale, Olumide Sunday 

作者机构：Department of Computer Science Federal University of Technology Akure Nigeria Department of Information Systems Federal University of Technology Akure Nigeria 

出 版 物：《International Journal of Business Intelligence and Data Mining》 (Int. J. Bus. Intell. Data Min.)

年 卷 期：2022年第21卷第2期

页      面：171-189页

核心收录：

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 020205[经济学-产业经济学] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Big data 

摘      要：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. Copyright © 2022 Inderscience Enterprises Ltd.