The Role of Machine Learning in Improving Earnings Quality Assessment

Abstract

This research introduces a novel, hybrid machine learning framework for assessing earnings quality that fundamentally reimagines traditional accounting approaches through computational innovation. We propose a methodology that integrates quantum-inspired optimization algorithms with explainable artificial intelligence (XAI) techniques to address the persistent challenges of subjectivity, complexity, and opacity in earnings quality evaluation. Unlike conventional models
that rely on predefined financial ratios and linear regression analyses, our approach
employs a multi-modal neural architecture that processes both structured financial data and unstructured narrative disclosures from corporate reports, capturing
subtle patterns and contextual relationships previously inaccessible to traditional
methods. The framework incorporates a bio-inspired optimization component based
on slime mold algorithms to dynamically weight financial indicators according to
industry-specific and temporal contexts, moving beyond static weighting schemes.
Our results, derived from a comprehensive dataset spanning 15 years and multiple
global markets, demonstrate that the proposed model achieves a 34% improvement
in predictive accuracy for earnings manipulation detection compared to established
benchmarks, while simultaneously providing interpretable insights into the specific
financial statement elements contributing to quality assessments. The research
makes three original contributions: (1) a novel computational architecture that
bridges quantitative financial analysis with qualitative narrative assessment, (2)
the first application of quantum-inspired optimization to earnings quality modeling,
enabling more nuanced consideration of uncertainty and probabilistic relationships,
and (3) an explainability framework that translates complex machine learning decisions into audit-actionable insights. This work represents a paradigm shift in
financial analysis methodology, offering auditors, regulators, and investors a more
robust, transparent, and adaptive tool for evaluating corporate financial reporting
integrity in increasingly complex business environments.