Flight delays represent a significant challenge for airlines, airports, and passengers, impacting operational costs and customer satisfaction. Traditional prediction methods often rely on complex statistical analysis and mathematical models that may not be easily implementable. This study proposes scorecards as an innovative and simplified approach to forecast flight delays. Historical flight data from the United States were used, incorporating variables such as departure and arrival times, flight routes, aircraft types, and other factors related to delay. Exploratory data analysis identified key variables influencing delays, and scorecards were constructed by assigning weights, normalizing, and scaling variables to improve interpretability. The model was validated using test datasets, and predictive performance was evaluated by comparing forecast delays with actual results. The results indicate that scorecards provide accurate and interpretable predictions of flight delays. This method facilitates the identification of critical factors that contribute to delays and allows for an estimation of their likelihood and duration. Scorecards offer a practical tool for airlines and airport operators, potentially enhancing decision-making processes, reducing delay-related costs, and improving service quality. Future research should explore the integration of scorecards into operational systems and the inclusion of additional variables to increase model robustness and generalizability.

Współcześnie, choć technologia rozwinęła się na niewyobrażalną skalę, wciąż istnieją czynniki, które mogą zakłócić bezpieczne i sprawne funkcjonowanie wielu obszarów codziennego życia. Jednym z nich są opóźnienia. Niewątpliwie są one elementem niepożądanym, a w niektórych przypadkach nawet niebezpiecznym. Szczególnym przypadkiem może być ruch lotniczy, który jest jednym z najbardziej zaawansowanych technologicznie obszarów. Jednak występujące dość często opóźnienia w ruchu lotniczym sprawiły, że pożądane stało się badanie tego obszaru w oparciu o modelowanie przepustowości lotnisk i metody uczenia maszynowego, z głównym naciskiem na algorytmy drzew decyzyjnych. W oparciu o te metody drzew decyzyjnych, wynikiem pozyskiwania i przetwarzania danych i zmiennych było stworzenie konkretnych modeli, które mogą wspierać zarządzanie ruchem lotniczym, a w konsekwencji niwelowanie powstałych opóźnień.

Purpose: This study seeks to develop and empirically validate a predictive model for assessing local innovativeness at the municipality level. It responds to the increasing demand among Local Administrative Units for evidence-based insights into the socio-economic and fiscal determinants of innovation and investigates the feasibility of forecasting innovation performance using routinely collected public data. Design/methodology/approach: The research adopts a quantitative methodological framework, integrating objective administrative data sourced from national institutions (e.g., Statistics Poland, the Ministry of Finance, and the Social Insurance Institution) with subjective data obtained via a CAPI survey conducted among 2,418 enterprises in 144 municipalities in the Kuyavian-Pomeranian Voivodeship. A classical risk scorecard modelling approach—widely employed in the financial sector—was adapted to identify determinants of local innovativeness and to construct a statistical model predicting the Predicted Innovation Rate. Findings: The study confirms that publicly accessible data can be effectively utilised to forecast local innovation potential. Six key predictors were identified, including tax base structure, average non-agricultural income, the sectoral composition of local enterprises (notably real estate and education), and VAT data related to service imports. Higher levels of innovativeness were associated with a greater presence of education-oriented businesses and higher service imports, whereas a concentration of real estate firms correlated negatively with innovation. The model demonstrated moderate predictive capability and enabled the stratification of municipalities by innovation potential. Research limitations/implications: As the model was developed using data from a single voivodeship, its generalizability may be constrained. Future research should explore model validation in other regional contexts and incorporate additional qualitative variables to enhance interpretability and predictive accuracy. Practical implications: The proposed model provides municipalities with a cost-efficient, data-driven tool for monitoring and fostering local innovation. It enables the formulation of targeted development strategies and evidence-based policy interventions. Social implications: The model has the potential to inform public policy aimed at enhancing regional innovation capacity, reducing territorial disparities, and supporting socio-economic cohesion through the promotion of knowledge-intensive activities. Originality/value: This paper presents one of the first predictive modelling approaches for evaluating innovativeness at the municipal level, offering a novel, scalable framework of practical relevance to policymakers, local authorities, and development agencies. Keywords: innovativeness, forecasting, municipality, predictive modelling, risk scorecard Models. Category of the paper: Research paper.

This paper aims to present a general idea of method comparison of Credit Scoring techniques. Any scorecard can be made in various methods based on variable transformations in the logistic regression model. To make a comparison and come up with the proof that one technique is better than another is a big challenge due to the limited availability of data. The same conclusion cannot be guaranteed when using other data from another source. The following research challenge can therefore be formulated: how should the comparison be managed in order to get general results that are not biased by particular data? The solution may be in the use of various random data generators. The data generator uses two approaches: transition matrix and scorings. Here are presented both: results of comparison methods and the methodology of these comparison techniques creating. Before building a new model the modeler can undertake a comparison exercise that aims at identifying the best method in the case of the particular data. Here are presented various measures of predictive model like: Gini, Delta Gini, VIF and Max p-value, emphasizing the multi-criteria problem of a "Good model". The idea that is being suggested is of particular use in the model building process where there are defined complex criteria trying to cover the important problems of model stability over a period of time, in order to avoid a crisis. Some arguments for choosing Logit or WOE approach as the best scorecard technique are presented.

The paper is aware of the importance of certain figures that are essential to an understanding of Credit Scoring models in credit acceptance process optimization, namely if the power of discrimination measured by Gini value is increased by 5% then the profit of the process can be increased monthly by about 1 500 kPLN (300 kGBP, 500 kUSD, 350 kEUR). Simple business models of credit loans are also presented: acquisition - installment loan (low price) and cross-sell - cash loans (high price). Scoring models are used to optimize process, to become profitable. Various acceptance strategies with different cutoffs are presented, some are profitable and some are not. Moreover, in a time of prosperity some are preferable whilst the inverse is true during a period of high risk or crisis. To optimize the process four models are employed: three risk models, to predict the probability of default and one typical propensity model to predict the probability of response. It is a simple but very important example of the Customer Lifetime Value (CLTV or CLV) model business, where risk and response models are working together to become a profitable process.