Business Inferences and Risk Modeling with Machine Learning - The case of Aviation Incidents

Aug 3, 2026

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Machine learning delivers business value when decision-makers rely on it to optimize choices, which requires trust, interpretability, and accountability in analytics. Aviation incidents are rare and irregular, yet produce high-impact disruptions; experts review reports, but organizations also need holistic, scenario-based understanding. This study presents an interpretable framework to predict aircraft damage and uncover patterns in flight specifications via simulation, integrating predictive results with what-if analysis. Results achieve 85% accuracy and 84% in-class accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Problem background and decision support approach

Data source and dataset overview

Data preparation (CRISP-DM) and feature engineering

Modeling and simulation-based what-if analysis

Proceedings of the 56th Hawaii International Conference on System Sciences | 2023 Business Inferences and Risk Modeling with Machine Learning; The case of Aviation Incidents Burak Cankaya Embry-Riddle Aeronautical University Worldwide, College of Business [bcankaya@erau.edu](bcankaya@erau.edu) Kazim Topuz The University of Tulsa, Collins College of Business [kat0141@utulsa.edu](kat0141@utulsa.edu) Aaron Glassman Embry-Riddle Aeronautical University Worldwide, College of Business [glassf10@erau.edu](glassf10@erau.edu) Abstract Machine learning becomes truly valuable only when decision-makers begin to depend on it to optimize decisions. Instilling trust in machine learning is critical for businesses in their efforts to interpret and get insights into data, and to make their analytical choices accessible and subject to accountability. In the field of aviation, the innovative application of machine learning and analytics can facilitate an understanding of the risk of accidents and other incidents . These occur infrequently, generally in an irregular, unpredictable manner, and cause significant disruptions, and hence, they are classified as "highimpact, low-probability" (HILP) events . Aviation incident reports are inspected by experts, but it is also important to have a comprehensive overview of incidents and their holistic effects. This study providesan interpretable machine-learning framework for predicting aircraft damage. In addition, it describes patterns of flight specifications detected through the use of a simulation tool and illuminates the underlying reasons for specific aviation accidents. As a result, we can predict the aircraft damage with 85% accuracy and 84% in-class accuracy. Most important, we simulate a combination of possible flight-type, aircraft-type, and pilot-expertise combinations to arrive at insights, and we recommend actions that can be taken by aviation stakeholders, such as airport managers, airlines, flight training companies, and aviation policy makers . In short, we combine predictive results with simulations to interpret findings and prescribe actions . Keywords: Business Analytics, Machine Learning, Decision Support Systems, Big Data, Aviation Risk Modeling, Business Inferences with Machine Learning 1. Introduction It is difficult to learn from aviation incidents since it is difficult to find the same combination offactors – aircraft, flight type, and pilot capabilities – in various incidents. Every incident has distinct characteristics, which makes it challenging to generalize any lessons learned to a broader scope of business decisions. The solution to this business problem calls for a simulation decision support system (DSS) to create scenarios and predict the likelihood of damage. A DSS could provide business insights by evaluating the inferences between variables and providing a what-if analysis to interpret incidents. Aviation is one of the earliest specialized fields because of its high-risk nature. Every incident is documented with detailed reports, which include records from devices, specifications ofthe nature of an event (such as the weather conditions and airfield data), and expert notes and judgments. We found that the severity of aircraft damage and the chains of events that cause incidents can be predicted; consequently, this study offers a way to apprehend the patterns that cause aviation incidents, by classifying them by flight type. In addition, using a simulation tool, what-if scenarios were analyzed, and actionable insights are offered to aviation decision-makers. The findings of this can help stakeholders by providing business insights mined from aviation incident reports. For the data, we used incident reports from the Federal Aviation Authority’s (FAA’s) Aviation Safety Information Analysis and Sharing (ASIAS) Accident and Incident Data System (AIDS) from 2000 to 2020, which include all aviation incidents that happened in the U.S. during that pe

FAQs

Why is trust and interpretability important for machine learning in business decisions?

The document emphasizes that machine learning becomes valuable only when decision-makers depend on it for optimizing decisions. Trust and accountability require making analytical choices interpretable and accessible.

What is the main objective of this study in aviation incident analysis?

The study aims to build an interpretable machine-learning framework that predicts aircraft damage and explains patterns behind aviation accidents using simulation and what-if scenarios.

Which data source and time range are used for modeling?

Incident reports from the FAA’s ASIAS Accident and Incident Data System (AIDS) are used for the years 2000 to 2020, covering aviation incidents in the U.S.

Aug 3, 2026

Upload by Jiven