Hasil untuk "Competition"

Y. Kuzyakov, Xingliang Xu

Jess Cornaggia, Yifei Mao, X. Tian et al.

M. Tangermann, K. Müller, A. Aertsen et al.

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.

G. Kunstler, D. Falster, D. Coomes et al.

Stephen Coate, D. Feenberg, Kai-Uwe Kuhn et al.

S. Makridakis, M. Hibon

M. Feldman, D. Audretsch

A. Ades, R. Tella

Theoretically the effect of competition on corruption is ambiguous. Less competition means firms enjoy higher rents, so that bureaucrats with control rights over them, such as tax inspectors or regulators, have higher incentives to engage in malfeasant behavior. Examples of a positive connection between rents and corruption abound, however. The hypothesis that natural rents, as in the case of oil, and rents induced by lack of product market competition foster corruption, is examined. A model is set up connecting rents to corruption.

James F. Moore

S. Claessens, L. Laeven

T. Khanna, R. Gulati, N. Nohria

S. Fontaine, A. Mariotti, L. Abbadie

John D. Wilson

T. Beck, A. Demirguc-Kunt, R. Levine

C. d'Aspremont, J. Gabszewicz, Jacques-François Thisse

Y. Tan

Several rounds of banking reforms in China have aimed to increase the competitive condition and further enhance stability in the Chinese banking sector, while the joint effects of competition and risk-taking behaviour on the profitability in the banking sector have not been studied well enough so far in the literature. The current study contributes to the empirical literature by testing the impacts of risk and competition on profitability in the Chinese banking industry (state-owned, joint-stock and city commercial banks) over the period 2003–2011 under a one-step Generalized Method of Moments (GMM) system estimator. The results do not show any robust finding with regards to the impacts of competition and risk on bank profitability, while it is found that Chinese bank profitability is affected by taxation, overhead cost, labour productivity and inflation. The study provides policy implications to the Chinese banking industry and different ownership types of Chinese commercial banks.

G. Gutiérrez, Thomas Philippon, Thomas Philippon

R. Holt

B.S. Prashanth, Manoj Kumar, Ariful Hoque et al.

The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal.We utilize a supervised learning pipeline that incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39 %, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives.Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.

David Bailey