BRIDGING THE DOMAIN GAP: ALIGNING BUSINESS UNDERSTANDING AND MACHINE LEARNING WITHIN THE FINDATA-CONTEXT FRAMEWORK (FDCF) FOR FINANCIAL ACCOUNTING ANALYTICS

Résumé

Modern corporate infrastructures generate massive streams of financial transactions, making data science an essential paradigm for modern corporate finance and accounting. However, a major issue persists: highly sophisticated machine learning models often fail in production environments because they lack domain-specific business understanding. This research addresses this core vulnerability by examining the critical intersection of financial accounting principles (such as accrual systems, revenue recognition schedules, and GAAP/IFRS standards) with advanced machine learning methods (such as unsupervised anomaly detection, graph analytics, and Explainable AI frameworks). We introduce a formal mathematical and structural pipeline called the FinData-Context Framework (FDCF). This framework builds accounting rules directly into the machine learning engineering process by adding an “Accounting Invariant Penalty” to traditional loss functions and using SHAP values for post-hoc explanation. Our empirical testing shows that adding business context to feature engineering layers reduces false-positive rates in automated audits by up to 35%, while boosting the reliability of forecasting models. This study demonstrates that cross-disciplinary expertise is vital for turning automated data systems into valuable financial intelligence.