Application of Machine Learning Techniques for Credit Risk Management: A Survey

Oualid, Adil; Hansali, Abderrahim; Balouki, Youssef; Moumoun, Lahcen

doi:10.1007/978-3-030-91738-8_18

Cited by 5 publications

(1 citation statement)

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“…Statistical methods are focused on probabilities and approximations which may lead to unsure conclusions and results. To cope with this issue, intelligent systems based on machine learning; which is a rapidly evolving field [8]; are hence needed. Authors in [9] focused on predicting flight arrival time at the block-off moment instead of airborne moment with linear regression and gradient boosting machines.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Aircraft Performance and Time of the Day on Flight Arrival Delays Prediction in the United States: a Machine Learning Classification

2022

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The excessive growth of air traffic, with the limited airspace and airports capacity, results in a flight demand-capacity imbalance leading to air traffic delays. This paper explores the factors associated with delay in both microscopic and macroscopic ways. The aim is to develop a model which analyzes and predicts the occurrence of flight arrival delays using US domestic flight data for the year 2018. It will provide passengers, airlines and airport managers with reliable flight arrival schedules, and consequently reduce economic losses and enhance passengers trust. Beside database features, the proposed model is to the best of our knowledge the first attempt to predict flight arrival delays using three new features which are contributive factors to delays: Departure Time and Arrival Time of the day in which the flight was performed (Early morning, late morning, noon, afternoon, evening or night) and model of aircraft. Four Machine Learning classifiers namely Random Forest, Decision Trees, K-Nearest Neighbors and Naive Bayes were used. In order to find the best parameters of each algorithm, we implemented Grid Search technique. The performance of each classifier was compared in terms of hyperparameters tuning, classification metrics and features description. The experimental results showed that the proposed system was able to predict flight arrival delays with the best Random Forest accuracy of 0.9356 and a higher number of correctly classified flights. To prove the importance of our findings, we compared our model to that of existing literature studies.

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Section: Literature Reviewmentioning

confidence: 99%