In the dynamic landscape of modern business operations, ensuring economic security through efficient and intelligent fleet management is imperative. This necessitates a dual focus on safeguarding revenue streams and optimizing operational costs. The aim of this study centers on two main objectives: first, to identify driving behaviors that have a substantial impact on vehicle maintenance costs; second, to ensure the sustainability of the fleet is managed effectively. To achieve these objectives, the research employs unsupervised Machine Learning (ML) techniques for segmenting driving styles based on diverse parameters collected from Internet of Things (IoT) devices. Furthermore, the Long Short-Term Memory (LSTM) algorithm is used for forecasting fuel consumption, offering a predictive glance into future expenditures. The methodology is based on the analysis of data gathered from sensors installed on the vehicle's Controller Area Network (CAN), collected over a span of five months. The findings spotlight a subset of drivers whose aggressive driving significantly influences maintenance costs and highlight optimal indicators for drivers to monitor to minimize CO2 emissions. Additionally, the study identifies key performance indicators that drivers should monitor to reduce CO2 emissions, contributing to the environmental sustainability of the fleet. This investigation not only elucidates the financial and environmental implications of driving behaviors but also showcases the transformative potential of ML technologies in enhancing the strategic management of vehicle fleets. Through this exploration, the research advocates for the integration of advanced analytics and sustainable practices as foundational elements for businesses striving to achieve economic security and operational resilience.

In finance, creating a model that balances risk reduction with opportunity is essential. This investigation addresses the necessity for risk evaluation frameworks that combine efficiency with adaptability, thus preserving opportunities for transactions critical to some organizations. The present study identifies, within a factoring process involving two key players: the invoice seller and the debtor, the essential variables that determine the likelihood of the debtor defaulting on the invoice payment. The event of non-payment is most often associated with the debtor's inability to pay due to insolvency, making it crucial in this type of activity to emphasize an efficient credit scoring system capable of proactively highlighting a debtor company with a high risk of default. Nonetheless, some companies pass this filter and enter the factoring process, but end up being unable to pay. The study identifies them based on a set of real data and uses supervised machine learning techniques to select the optimal classification model, also highlighting the variables with a major impact on the target. The specialized literature is focused on identifying the models that perform best in the credit scoring activity or studies that identify the non-payment behavior of clients. What this work adds is the combination of these two dimensions, for example, it provides an additional filter to credit scoring, using parameters identified as essential in determining defaulters and using them as inputs for an unsupervised learning model, thus classifying the entire population of companies in Romania to identify clusters containing the highest proportion of non-payment companies.

The most impactful factors for the cost of fleet management are maintenance expenses and fuel consumption. Traditional ways of monitoring fleet performance fail to connect raw operational data with driving habits. The current study addresses this challenge by developing an architecture of frameworks, consisting of unsupervised and supervised machine learning algorithms, statistical testing, simulation and survival analysis to discover insights that lead to key behavioral predictors. The nucleus of this complex architecture is the decision-making grid (DMG), a two-dimensional matrix that groups vehicles based on their frequency of entering the service and the cost of their repairs. It is the first integration of DMG with ML for prescriptive fleet management. The objective of the study is twofold: firstly, to build a system that classifies vehicles according to their risk profile, and secondly, to offer clear directions for changing driver patterns that most affect vehicle costs or for keeping good practices. The framework proposed by this study not only drives the optimization of operational efficiency but also contributes to a methodology that links driver profiles to costs, offering a scalable methodology for similar business contexts.