Generative artificial intelligence (AI) has emerged as a powerful technology with numerous applications in various domains. There is a need to identify the requirements and evaluation metrics for generative AI models designed for specific tasks. The purpose of the research aims to investigate the fundamental aspects of generative AI systems, including their requirements, models, input–output formats, and evaluation metrics. The study addresses key research questions and presents comprehensive insights to guide researchers, developers, and practitioners in the field. Firstly, the requirements necessary for implementing generative AI systems are examined and categorized into three distinct categories: hardware, software, and user experience. Furthermore, the study explores the different types of generative AI models described in the literature by presenting a taxonomy based on architectural characteristics, such as variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion models, transformers, language models, normalizing flow models, and hybrid models. A comprehensive classification of input and output formats used in generative AI systems is also provided. Moreover, the research proposes a classification system based on output types and discusses commonly used evaluation metrics in generative AI. The findings contribute to advancements in the field, enabling researchers, developers, and practitioners to effectively implement and evaluate generative AI models for various applications. The significance of the research lies in understanding that generative AI system requirements are crucial for effective planning, design, and optimal performance. A taxonomy of models aids in selecting suitable options and driving advancements. Classifying input–output formats enables leveraging diverse formats for customized systems, while evaluation metrics establish standardized methods to assess model quality and performance. 

The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges

In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly. Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial enterprises. At present, traditional machine learning methods (such as SVM, random forest, Markov model, etc.) have been widely studied in credit card fraud detection, but these methods are often have difficulty in demonstrating their effectiveness when faced with unknown attack patterns. In this paper, a new Unsupervised Attentional Anomaly Detection Network-based Credit Card Fraud Detection framework (UAAD-FDNet) is proposed. Among them, fraudulent transactions are regarded as abnormal samples, and autoencoders with Feature Attention and GANs are used to effectively separate them from massive transaction data. Extensive experimental results on Kaggle Credit Card Fraud Detection Dataset and IEEE-CIS Fraud Detection Dataset demonstrate that the proposed method outperforms existing fraud detection methods.

https://www.mdpi.com/2079-8954/11/6/305/pdf?version=1686707821

Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network

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Large Language Models and Generative AI in Finance: An Analysis of ChatGPT, Bard, and Bing AI

Deep learning (DL) research has made remarkable progress in recent years. Natural language processing and image generation have made the leap from computer science journals to open-source communities and commercial services. Pre-trained DL models built on massive datasets, also known as foundation models, such as the GPT-3 and BERT, have led the way in democratizing artificial intelligence (AI). However, their potential use as research tools has been overshadowed by fears of how this technology can be misused. Some have argued that AI threatens scholarship, suggesting they should not replace human collaborators. Others have argued that AI creates opportunities, suggesting that AI-human collaborations could speed up research. Taking a constructive stance, this editorial outlines ways to use foundation models to advance science. We argue that DL tools can be used to create realistic experiments and make specific types of quantitative studies feasible or safer with synthetic rather than real data. All in all, we posit that the use of generative AI and foundation models as a tool in information systems research is in very early stages. Still, if we proceed cautiously and develop clear guidelines for using foundation models and generative AI, their benefits for science and scholarship far outweigh their risks. 

Augmenting research methods with foundation models and generative AI

Deep neural networks (DNNs) have attained boundless accomplishments in dealing with high-dimensional data, particularly images. However, generating naturalistic images for tasks such as classification, detection, segmentation, reconstruction, and so on remains a difficult task. As a result, researchers are compelled to propose new algorithms and methods to overcome these challenges. These learning algorithms, on the other hand, frequently need a large volume of training data to be efficient. Also, its associated real-world challenges have limited data accessibility, and gathering sufficient amounts of data may be expensive. Additionally struggling with the issues of class imbalance, data shortage, and label propagation has turned the attention toward generative approaches, i.e., using modern procedures for producing new sample data with similar features as real sample data to enhance the efficiency of learning algorithms. Generative adversarial networks (GANs), a class of DNNs offer a novel way to model and generate data in an unsupervised manner. The deep learning community is increasingly pursuing an interest in GANs. The paper gives a basic introduction to GANs and discusses their variants in the study. 

Generative Adversarial Networks: Overview

Modelling in finance is a challenging task: the data often has complex statistical properties and its inner workings are largely unknown. Deep learning algorithms are making progress in the field of data-driven modelling, but the lack of sufficient data to train these models is currently holding back several new applications. Generative Adversarial Networks (GANs) are a neural network architecture family that has achieved good results in image generation and is being successfully applied to generate time series and other types of financial data. The purpose of this study is to present an overview of how these GANs work, their capabilities and limitations in the current state of research with financial data, and present some practical applications in the industry. As a proof of concept, three known GAN architectures were tested on financial time series, and the generated data was evaluated on its statistical properties, yielding solid results. Finally, it was shown that GANs have made considerable progress in their finance applications and can be a solid additional tool for data scientists in this field.

Generative Adversarial Networks in Finance: An Overview

Generative Adversarial Networks in finance: an overview

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Generative Adversarial Networks in Finance: An Overview

Generative Adversarial Networks in finance: an overview

Generative Adversarial Networks in finance: an overview