Abstract
This chapter provides a comprehensive analysis of the impact of machine learning on the specific domains of financial accounting and management accounting. By tracing the historical evolution, conceptually delineating key parameters, and systematizing various modalities of machine learning, the investigation highlights the notable advancements it engenders in financial management. The study underscores the central role of machine learning in automating processes, optimizing decision-making, and generating innovative analytical perspectives, while identifying ethical concerns inherent in its implementation, such as algorithmic transparency and data preservation. This research is based on a literature review approach using a descriptive analytical method. In conclusion, machine learning emerges as a significant driver of progress in the accounting domain, redefining professional standards and necessitating ethical management to fully capitalize on its benefits while minimizing potential risks.
TopMachine Learning: History And Definitions
Before studying any phenomenon, it's useful to know its origins. In the following, we will outline the history of machine learning, as well as the most useful definitions.
Key Terms in this Chapter
Unsupervised Learning: Unsupervised learning is a significant approach in machine learning. It involves training a model on unlabeled data, enabling the model to recognize intrinsic patterns without explicit guidance on desired outputs, unlike supervised learning.
Supervised Learning: Supervised learning is a fundamental approach in machine learning. It involves training a model on labeled data, where each input is associated with a known output. This allows the model to learn how to establish a precise relationship between the input data and the expected outputs.
Financial Accounting: Financial accounting is a specialized field that documents, synthesizes, and discloses a company's financial transactions. Its main goal is to provide reliable information about the entity's performance and financial position to external parties, such as investors, creditors, regulators, and the general public.
Semi-Supervised Learning: Semi-supervised learning is a machine learning approach that combines elements of both supervised and unsupervised methods. The model is trained on a dataset where some instances are labeled, while a larger portion remains unlabeled.
Auditing: Auditing is a systematic and independent process that involves reviewing financial data, operational procedures, and systems within an organization. Its primary goal is to ensure the accuracy and reliability of the audited information. Auditing is a crucial aspect of corporate governance and risk management, as it enhances the credibility of financial statements and ensures compliance with applicable laws and regulations.
Machine Learning: Machine learning, a subset of artificial intelligence, is a field devoted to the development of techniques that enable computer systems to learn from data. Unlike traditional programming, where explicit instructions are given to a computer to perform a task, machine learning involves the construction of statistical models that can be trained to recognize patterns and make decisions autonomously.
Management Accounting: Management accounting provides financial analysis and information for decision-making within an organization. It supports internal processes of planning, control, and strategy formulation, providing managers with the necessary tools for effective resource management and strategic orientation of the company.