Vol: 13, Issue: 4 2023

Page: 131-137

Developing a Smart Integrated Enterprise Financial Information Management System (EFIMS) on Data Analysis Algorithm

Ilayda Tunc

Received Date: 2023-10-14

Accepted Date: 2023-12-19

Published Date: 2023-12-24

http://doi.org/10.37648/ijtbm.v13i04.011

Enterprises generate massive volumes of financial data across ERP, banking, invoicing, procurement, payroll, and expense platforms. Yet finance teams still struggle with late closes, inconsistent master data, weak cross-system traceability, and limited early warning signals for cash, compliance, and fraud risks. This paper proposes an Enterprise Financial Information Management System (EFIMS) that combines (i) a governed financial data foundation (integration, quality controls, lineage, and role-based access) with (ii) a modular analytics layer powered by data analysis algorithms for anomaly detection, forecasting, segmentation, and risk scoring. The design is informed by prior work on enterprise systems and real-time reporting challenges in accounting information systems [2], data warehouse foundations [5], audit-focused analytics architectures [11], and structured reporting initiatives such as XBRL [8–10]. A prototype evaluation on synthetic enterprise-ledger data illustrates feasibility: an Isolation Forest anomaly module achieved an ROC-AUC of 0.847 for detecting injected irregular entries under realistic categorical and monetary distributions, while a baseline ARIMA cash-flow forecaster achieved 8.66% MAPE on a simulated series with seasonality and shocks. The outcome is a practical, explainable architecture that supports daily finance operations (close, controls, reporting) while enabling continuous monitoring and data-driven decision support.

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