The significant losses that banks and other financial organizations suffered due to new bank account (NBA) fraud are alarming as the number of online banking service users increases. The inherent skewness and rarity of NBA fraud instances have been a major challenge to the machine learning (ML) models and happen when non-fraud instances outweigh the fraud instances, which leads the ML models to overlook and erroneously consider fraud as non-fraud instances. Such errors can erode the confidence and trust of customers. Existing studies consider fraud patterns instead of potential losses of NBA fraud risk features while addressing the skewness of fraud datasets. The detection of NBA fraud is proposed in this research within the context of value-at-risk as a risk measure that considers fraud instances as a worst-case scenario. Value-at-risk uses historical simulation to estimate potential losses of risk features and model them as a skewed tail distribution. The risk-return features obtained from value-at-risk were classified using ML on the bank account fraud (BAF) Dataset. The value-at-risk handles the fraud skewness using an adjustable threshold probability range to attach weight to the skewed NBA fraud instances. A novel detection rate (DT) metric that considers risk fraud features was used to measure the performance of the fraud detection model. An improved fraud detection model is achieved using a K-nearest neighbor with a true positive (TP) rate of 0.95 and a DT rate of 0.9406. Under an acceptable loss tolerance in the banking sector, value-at-risk presents an intelligent approach for establishing data-driven criteria for fraud risk management.

Three major principles for the selection of indicator data normalization methods in multi-attribute evaluation are presented in this paper. Principle 1: The relative gap between the data for the same indicator should remain constant; Principle 2: The relative gap between different indicators should remain variable ; and Principle 3: The maximum values after normalization should be equal. According to these three major principles, a normalization method for positive indicators is screened out from several alternatives, and a new normalization method for negative indicators is proposed. These two methods are very good for the comparison among panel data. The requirement for data normalization methods is different when the evaluation goals are different, ranking-order-based evaluation is insensitive to data normalization methods.

Human intrinsic motivation is of great importance in human behavior. However, although researchers have focused on this topic for decades, its neural basis was still unclear. The current study employed event-related potentials to investigate the neural disparity between an interesting stop-watch (SW) task and a boring watch-stop task (WS) to understand the neural mechanisms of intrinsic motivation. Our data showed that, in the cue priming stage, the cue of the SW task elicited smaller N2 amplitude than that of the WS task. Furthermore, in the outcome feedback stage, the outcome of the SW task induced smaller FRN amplitude and larger P300 amplitude than that of the WS task. These results suggested that human intrinsic motivation did exist and that it can be detected at the neural level. Furthermore, intrinsic motivation could be quantitatively indexed by the amplitude of ERP components, such as N2, FRN, and P300, in the cue priming stage or feedback stage. Quantitative measurements would also be convenient for intrinsic motivation to be added as a candidate social factor in the construction of a machine learning model.

Brand strategy is a critical problem in new product promotion. In relation to this, producers typically have two main options, namely, brand extension and new brand creation. The current study investigated the neural basis of evaluating these brand strategies at the brain level by using event-related potentials. The experiment used a word-pair paradigm, in which the first word was either a famous beverage brand name or a newly created brand, and the second word was a product name from one of the two product categories (beverage or household appliance). Therefore, four conditions existed as follows: a famous beverage brand paired with a beverage product (BB) or with a household appliance (BH) and a newly created brand paired with a beverage product (NB) or with a household appliance (NH). Behavioral results showed that brand extension obtained a higher acceptance rate than new brand creation under the beverage product category; however, a lower acceptance rate was observed under the household appliance category. Moreover, at the brain level, BB elicited lower N400 mean amplitude than the new brand product NB, whereas BH led to higher N400 amplitude than the new brand product NH. These results showed that the likelihood of accepting a product depended on the association between the brand name and product name, and that the N400 could serve as an index of brand strategy evaluation. In addition, this study also confirmed that brand extension is not always the best brand strategy; an inappropriate extension sometimes performed worse than the creation of a new brand.