Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble

In the realm of consumer lending, accurate credit default prediction stands as a critical element in risk mitigation and lending decision optimization. Extensive research has sought continuous improvement in existing models to enhance customer experiences and ensure the sound economic functioning of lending institutions. This study responds to the evolving landscape of credit default prediction, challenging conventional models and introducing innovative approaches. By building upon foundational research and recent innovations, our work aims to redefine the standards of accuracy in credit default prediction, setting a new benchmark for the industry. To overcome these challenges, we present an Ensemble Methods framework comprising LightGBM, XGBoost, and LocalEnsemble modules, each making unique contributions to amplify diversity and improve generalization. By utilizing distinct feature sets, our methodology directly tackles limitations identified in previous studies, with the overarching goal of establishing a novel standard for credit default prediction accuracy. Our experimental findings validate the effectiveness of the ensemble model on the dataset, signifying substantial contributions to the field. This innovative approach not only addresses existing obstacles but also sets a precedent for advancing the accuracy and robustness of credit default prediction models

Merton model as predictor of failure probability of public banks in Indonesia

This research attempts to use Black-Schole-Merton (BSM) model based on market approach to predict default probability of publishing bank in Indonesia. This is done by using stock prices and financial report. In this effort, this study estimates the neutral risk and default probability for the publish bank. The result showed that option model can predict default status more with accurate event long before default information was published for public. It can be studied from the case of Bank Century that has been imposed as a failure bank, in which it is known as bailout bank by the Indonesian government. The model does not only provide the ordinal ranking for the bank sample but also the good early warning prediction for the public. The probability estimation based on the option model can be an innovative model to measure and manage credit risk on the future for predicting probability default in Indonesia

Modelling credit risk for innovative firms: the role of innovation measures

Financial constraints are particularly severe for R&D projects of SMEs, which cannot generally rely on equity markets and, in the EU, on a sufficiently developed VC industry. If innovative SMEs have to depend on banks to finance their R&D projects, it is particularly important to develop models able to estimate their probability of default (PD) in consideration of their peculiar features. Based on the signaling value of some innovative assets, the purpose of this paper is to show the importance to include them into models which have proved to be successful for SMEs. To this end, we take a logit model and test it on a unique dataset of innovative SMEs (based on PATSTAT database, EPO BULLETIN and AMADEUS) to estimate a two-year PD with default years 2006-2008. In the regression analysis the innovation-related variables are two in order to account for R&D productivity at the level of the firm and to consider the value of the inventive output. Our analyses first address measurement issues concerning innovation-related variable and then show that, while the accounting variables and the patent value are always significant with the expected sign, the patent number per se reduces the PD only in the presence of an appropriate equity level.innovative SMEs; default probability; patent value

SUBSTANTIATION OF THE PUBLIC DEBT SUSTAINABILITY USING KALMAN FILTER

Global economic conditions have pushed many countries into the delicate situation of contracting foreign loans, leading overnight at alarming volumes of public debt. The need for control and relevant analysis for the sustainability of a country\'s public debt has led us to use the Kalman filter in predicting future values of the key indicators of public debt. The development of a mathematical model of analysis for public services and the budget deficit was necessary to objectively assess the level of the public debt sustainability.Knowing future values of the public debt or the future evolutions of the revenues for the operational budget, offers the posibility of a better handling of the operational expenditures and finally a better balance for the public budget deficit.Using the mathematical mechanism of Kalman filters implemented in Matlab programming language, we generated the estimated future values of the proposed model proposed and key indicators, the results confirming the fears of a low public debt sustainability for Romania.We predicted the future values for the debt service, the public external debt and the operational public revenues,expenditures and deficit, and compared them, to obtain an image of the future evolution and position of the sustainability of the public debt. The work in this paper is an innovative one for the public science sector, and the results obtained are promising for future researches. The values estimated by the Kalman filter are an orientation for the future public policies, and indicate a rather stable but negative evolution for the public debt service. The sustainability of the public debt depends on the decisions taken for the correction of the estimated values, in changing the negative evolution of the budgetary indicators into a positive one.Taking all this into consideration we will conclude that the mathematical mecanism of the Kalman filters offers valuable informations for Government and future research should be oriented to develop it's returned results.Kalman filter, debt, sustainability, deficit, prediction

Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME

We present a heuristic based algorithm to induce \textit{nonmonotonic} logic programs that will explain the behavior of XGBoost trained classifiers. We use the technique based on the LIME approach to locally select the most important features contributing to the classification decision. Then, in order to explain the model's global behavior, we propose the LIME-FOLD algorithm ---a heuristic-based inductive logic programming (ILP) algorithm capable of learning non-monotonic logic programs---that we apply to a transformed dataset produced by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics. Meanwhile, the number of induced rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system