Detecting financial fraud to profile crimes and pinpoint system vulnerabilities is an essential issue in the financial industry. Because of interpretability requirements and the lack of mass transaction data due to privacy regulations, sophisticated handcrafted features have been adopted in much of the literature for fraud detection. In addition to established recency, frequency, monetary, and anomaly features, we propose behavior- and segmentation-type features based on statistical characteristics belonging solely to (non-)fraudulent accounts informed by financial expertise. Our proposed features are difficult for automatic feature generators to synthesize, and provide transparent cause-effect relationships and good prediction results. Features with time-inhomogeneous properties cause popular boosting classifiers such as XGBoost and LGBM to produce unstable detection results. We use the Kolmogorov–Smirnov test to detect and remove these features to improve XGBoost and LGBM detection performance and robustness. The resulting performance shown in our experiments is better than that of other classifiers, such as SVM and random forests. We examine the advantage of our technique by comparing it with several feature engineering works on fraud detection and automatic feature generation methods. On the other hand, we also find that generating training/testing sets with random sampling falsely eliminates such time inhomogeneity and results in misleading assessments of the robustness of machine learning models. These time-inhomogeneous phenomena also entail various modus operandi patterns, which influence the performance of different resampling methods for addressing data imbalance in fraud detection. Improper linear interpolation of SMOTE-related approaches leads to poor performance due to varying patterns of modi operandi. However, synthesizing fraudulent samples with simple oversampling and GANs mitigates this problem.

Background/Aim: The present study aimed at evaluating the contribution of IL-4 promoter T-1099G (rs2243248), C-589T (rs2243250), C-33T (rs2070874) genotypes to the risk of breast cancer in Taiwanese. Materials and Methods: A total of 1232 breast cancer patients and 1232 age-matched controls were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) methodology. Results: Genotypic frequencies of IL-4 rs2243248, rs2243250 and rs2070874 were not differentially distributed between case and control groups. Consistently, there was no difference in the distribution of allelic frequencies among patients and controls. Conclusion: IL-4 rs2243248, rs2243250 and rs2070874 do not confer breast cancer susceptibility in Taiwanese.

Options can be priced by the lattice model, the results of which converge to the theoretical option value as the lattice’s number of time steps approaches infinity. The time complexity of a common dynamic programming pricing approach on the lattice is slow (at least ), and a large is required to obtain accurate option values. Although -time combinatorial pricing algorithms have been developed for the classical binomial lattice, significantly oscillating convergence behavior makes them impractical. The flexibility of trinomial lattices can be leveraged to reduce the oscillation, but there are as yet no linear-time algorithms on trinomial lattices. We develop -time combinatorial pricing algorithms for polynomial options that cannot be analytically priced. The commonly traded plain vanilla and power options are degenerated cases of polynomial options. Barrier options that cannot be stably priced by the binomial lattice can be stably priced by our -time algorithm on a trinomial lattice. Numerical experiments demonstrate the efficiency and accuracy of our -time trinomial lattice algorithms.