Machine Learning Algorithms for Real-Time Risk Assessment

View Sample PDF

Author(s): Mustafa Kayyali (Maaref University of Applied Sciences, Syria)
Copyright: 2026
Pages: 28
Source title: Enhancing Financial Decision-Making Through AI and Accounting Systems
Source Author(s)/Editor(s): Ionica Oncioiu (Titu Maiorescu University, Constanta, Romania)
DOI: 10.4018/979-8-3373-3626-8.ch001

Keywords: Accounting and Finance / Business & Management / Business Science Reference / Decision Support Systems

Purchase

View Machine Learning Algorithms for Real-Time Risk Assessment on the publisher's website for pricing and purchasing information.

Abstract

In an increasingly volatile financial ecosystem, static risk models are no longer enough. This chapter explores the transformative potential of machine learning algorithms in delivering real-time risk assessment to support more agile and data-driven financial decision-making. The authors examine how models such as ensemble learners, deep neural networks, and time-series forecasting architectures can dynamically interpret transactional and behavioral data to flag emerging threats, anomalies, and opportunities. The discussion extends beyond technical efficiency to confront deeper questions of algorithmic transparency, ethical safeguards, and systemic bias—areas often overlooked in the rush to automate. Drawing from real-world use cases and implementation blueprints, the chapter ultimately argues for a paradigm shift: from reactive risk reporting to proactive, adaptive risk intelligence that evolves with the financial landscape. In doing so, it offers a roadmap not just for technologists, but for financial leaders navigating uncertainty in the age of AI.

The IRMA Community

Research IRM

Machine Learning Algorithms for Real-Time Risk Assessment

Purchase

Abstract

Related Content

IRMA Sponsors