Optimization study of intelligent accounting manager system modules in adaptive behavioral pattern learning and...

Area of Science:

• Accounting Management
• Financial Risk Assessment
• Machine Learning in Finance

Background:

• Adaptive learning technologies are transforming accounting management education and risk assessment.
• Current methods for classifying accounting management health require enhancement for precision and efficacy.

Purpose of the Study:

• To introduce an advanced health classification schema for accounting management, termed A-CHMM-FD.
• To improve the precision and efficacy of financial risk detection in accounting management.

Main Methods:

• The A-CHMM-FD methodology integrates the Analytic Hierarchy Process (AHP) with the Coupled Hidden Markov Model (CHMM).
• AHP is used to quantify accounting metrics, which are then analyzed by CHMM for risk evaluation.
• Fuzzy delineations are employed to classify entities as healthy, at-risk, or high-risk.

Main Results:

• Empirical validation on financial risk datasets confirmed the framework's superior efficiency and precision.
• The A-CHMM-FD methodology demonstrated effectiveness in the health classification of accounting management.
• The proposed approach offers a novel technological trajectory for managing accounting risks.

Conclusions:

• The A-CHMM-FD methodology provides an efficacious approach to accounting management health classification.
• This framework enhances risk detection and offers new perspectives on accounting knowledge acquisition.
• The study highlights the potential of integrating AHP and CHMM for sophisticated financial risk management.