Accounting Predictive Analytics and Corporate Financial Distress: A Study of Listed Manufacturing Firms in Nigeria

Damilola Peace BABALOLA; Damilola Gabriel FAGBORO; Imoleayo Foyeke OBIGBEMI

Damilola Peace BABALOLA Department of Accounting, University of Lagos, Lagos, Nigeria
Damilola Gabriel FAGBORO Department of Accounting, University of Lagos, Lagos, Nigeria
Imoleayo Foyeke OBIGBEMI Department of Accounting, University of Lagos, Lagos, Nigeria

Keywords: Forensic Accounting, Predictive Analytics, Financial Link Analysis, Financial Forensics Data Mining, Financial Metrics, Non-financial Metrics, Financial Distress

Abstract

The current research study centres on predictive analytics of financial distress in listed manufacturing firms in Nigeria. Using a six-year period, from 2018 to 2023 financial statements of selected firms; the study adopted the ex-post facto research design. The data source for the study was secondary in nature. Logistic regression distress prediction models, correlation analysis and descriptive statistical tools were enlisted to analyse the data. Empirical results show that application of forensic accounting predictive analytics is a potent means of early detection of financial distress, making it easier to work on the improved financial stability. This further presents new knowledge of how financial forensics data mining and analytics, and link analysis can be useful tools in detection of corporate distress. The study concludes that financial metrics and link analysis play a significant role in identifying financial instability of firms. Non-financial analysis and financial net worth analysis might not be reliable techniques but should not be however ignored as they can serve as red flag signals.