METHODS AND MODELS FOR OPTIMIZING FINANCIAL AND ADMINISTRATIVE PROCESSES THROUGH ARTIFICIAL INTELLIGENCE

Abstract

The rapid development of artificial intelligence (AI), machine learning (ML), natural language processing (NLP), predictive analytics, and intelligent automation is transforming the organization of financial and administrative processes. Financial administration traditionally depends on large volumes of structured and unstructured data, repetitive transactions, forecasting, compliance controls, reporting, budgeting, auditing, and resource allocation. These characteristics make the domain particularly suitable for the application of AI-based optimization methods. This article examines methods and models for optimizing financial and administrative processes through artificial intelligence, with particular attention to predictive forecasting, anomaly detection, intelligent process automation, risk classification, natural language processing, decision-support systems, and prescriptive analytics. The study applies a comparative analytical methodology based on international institutional reports, empirical studies, and scientific literature. Evidence from the OECD, International Monetary Fund, Financial Stability Board, World Bank, and peer-reviewed research demonstrates that AI can improve the timeliness and analytical depth of financial decision-making, automate repetitive administrative activities, strengthen risk-based controls, and improve the targeting of limited administrative resources. Empirical research also indicates that machine-learning models can outperform conventional forecasting benchmarks in selected macroeconomic applications. However, AI optimization is not equivalent to unrestricted automation. Data quality, model explainability, cybersecurity, algorithmic bias, legacy information systems, legal requirements, accountability, and human oversight remain fundamental conditions for successful implementation. The article proposes an integrated AI-based optimization framework consisting of process diagnosis, data preparation, model selection, prediction, optimization, human validation, monitoring, and continuous improvement. The results indicate that the most sustainable approach is a human-centred and risk-based model in which AI augments rather than eliminates professional judgment.