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International Journal of Transformations in Business Management

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Abstract

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

131-137 Vol: 13, Issue: 4, 2023
Receiving Date: 2023-10-14
Acceptance Date: 2023-12-19
Publication Date: 2023-12-24
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http://doi.org/10.37648/ijtbm.v13i04.011

Abstract

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.

Keywords: financial information management; enterprise systems; data warehouse; anomaly detection; cash-flow forecasting; audit analytics; XBRL; governance

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