Journal of Airline Operations and Aviation Management

The airline sector is highly competitive and costly, with dynamic pricing, shifting fuel costs, and operational inefficiencies all having an impact on profitability. Traditional financial modelling methodologies frequently fail to reflect the complexities of these difficulties, necessitating the use of AI-driven financial modelling. This article investigates how AI improves airline profitability and cost savings through enhanced revenue management, predictive analytics, and risk assessments. AI technologies like machine learning, deep learning, and predictive modelling increase demand forecasting, optimize ticket pricing, and reduce operational costs like fuel economy and predictive maintenance. Additionally, AI helps with financial risk management by detecting fraud, optimizing investment strategies, and limiting external market risks.
Despite its advantages, AI adoption in airline financial management confronts a number of obstacles, including data privacy concerns, algorithmic biases, and legal constraints. The study reviews existing governance frameworks and makes strategic recommendations for ethical AI deployment, highlighting the importance of openness, stakeholder participation, and compliance with international legislation. A comparison with other industries, such as banking and logistics, reveals best practices that airlines can implement for AI-driven financial transformation.
Future research should concentrate on empirical confirmation of AI's financial impact, cross-industry benchmarking, and the incorporation of AI into sustainable aviation financing. As AI advances, its role in designing airline business strategies will grow, providing airlines with a competitive advantage in cost efficiency, revenue creation, and long-term financial viability.