As multinational enterprises operate across currencies, credit systems, commodity markets, and cross-border reporting environments, financial risk management increasingly depends on timely data rather than periodic review. In the 2026 paper “The Application of Data-Driven Financial Risk Management in Multinational Enterprises,” published in Economics and Management Innovation, Yuchen Liu examines how big data, statistical methods, machine learning, and visualization tools can be integrated into a framework for identifying, assessing, and responding to financial risks.
The paper begins from a practical problem: multinational enterprises face exposures that can change quickly across markets. It identifies exchange-rate volatility, credit risk, market price fluctuation, and financial fraud as major risk categories. Traditional methods, the paper notes, often rely on static data and experience-based judgment, which can make them less responsive to changes in a complex international business environment.
To address that limitation, the study describes a data-driven financial risk management framework built around collection, integration, prediction, assessment, visualization, reporting, and system collaboration. The framework treats risk management as a dynamic process in which internal and external data are gathered from enterprise systems, accounting software, external datasets, APIs, web-crawler sources, and real-time streams before being cleaned, normalized, and integrated for analysis. That sequence is presented as the data foundation for later modeling, warning, reporting, and feedback.
A central part of the research is its discussion of risk prediction and assessment. Liu’s paper describes the use of statistical methods, machine learning algorithms, deep learning techniques, Value at Risk models, stress testing, and credit scoring models to evaluate potential exposures. It explains how these tools can help enterprises identify potential losses, simulate extreme scenarios, estimate customer default probability, and adjust risk strategies as new data become available.
The paper also emphasizes visualization and reporting systems as tools for decision support. It discusses dashboards, interactive charts, and reports built with tools such as Tableau, Power BI, and Google Data Studio. By converting complex financial data into clearer visual forms, the framework is designed to help managers review risk indicators more directly and respond to changing conditions more quickly.
The applied sections connect the framework to specific multinational-enterprise risks. For exchange-rate risk, the paper describes real-time monitoring systems that use foreign-exchange market data, historical currency movements, and macroeconomic indicators. For transnational credit risk, it discusses credit ratings, default-rate estimation, dynamic monitoring, credit reports, and flexible credit limits. The paper also addresses commodity price risk and cross-border financial fraud through hedging, scenario planning, transaction monitoring, and anomaly detection. Together, those sections move the framework from general architecture to specific applied risk-control use cases.
That focus on financial data, predictive modeling, and practical decision support connects the research to Liu’s background in accounting and analytics. The article page lists him as affiliated with the College of Professional Studies at Northeastern University in Boston, where his resume materials identify him as a Master of Analytics graduate. His resume also lists a Bachelor of Business from Capital University of Economics and Business, current work as a staff accountant at ShipSaving, prior experience as an accounting and financial analyst intern at Top Speed Energy USA, and audit associate experience at PwC in Beijing. The same materials describe experience with full-cycle accounting, financial reporting, variance analysis, budgeting support, audit risk assessment, internal controls, and process optimization.
By linking big data collection, risk prediction, visualization, and cross-system collaboration, Liu’s research frames financial risk management as an information-driven process for multinational enterprises. The work points to a model in which companies can monitor risk more continuously, organize financial evidence more consistently, and support business decisions with clearer analytical foundations.