Conversational AI chatbots are transforming industries by streamlining customer service, automating transactions, and enhancing user engagement. However, evaluating these systems remains a challenge, particularly in financial services, where compliance, user trust, and operational efficiency are critical. This paper introduces a novel evaluation framework that systematically assesses chatbots across four dimensions: cognitive and conversational intelligence, user experience, operational efficiency, and ethical and regulatory compliance. By integrating advanced AI methodologies with financial regulations, the framework bridges theoretical foundations and real-world deployment challenges. Additionally, we outline future research directions, emphasizing improvements in conversational coherence, real-time adaptability, and fairness.

With the development of the financial industry, credit default prediction, as an important task in financial risk management, has received increasing attention. Traditional credit default prediction methods mostly rely on machine learning models, such as decision trees and random forests, but these methods have certain limitations in processing complex data and capturing potential risk patterns. To this end, this paper proposes a deep learning model based on the combination of convolutional neural networks (CNN) and Transformer for credit user default prediction. The model combines the advantages of CNN in local feature extraction with the ability of Transformer in global dependency modeling, effectively improving the accuracy and robustness of credit default prediction. Through experiments on public credit default datasets, the results show that the CNN+Transformer model outperforms traditional machine learning models, such as random forests and XGBoost, in multiple evaluation indicators such as accuracy, AUC, and KS value, demonstrating its powerful ability in complex financial data modeling. Further experimental analysis shows that appropriate optimizer selection and learning rate adjustment play a vital role in improving model performance. In addition, the ablation experiment of the model verifies the advantages of the combination of CNN and Transformer and proves the complementarity of the two in credit default prediction. This study provides a new idea for credit default prediction and provides strong support for risk assessment and intelligent decision-making in the financial field. Future research can further improve the prediction effect and generalization ability by introducing more unstructured data and improving the model architecture.

Cloud computing (cloud computing) is a kind of distributed computing, referring to the network "cloud" will be a huge data calculation and processing program into countless small programs, and then, through the system composed of multiple servers to process and analyze these small programs to get the results and return to the user. This report explores the intersection of cloud computing and financial information processing, identifying risks and challenges faced by financial institutions in adopting cloud technology. It discusses the need for intelligent solutions to enhance data processing efficiency and accuracy while addressing security and privacy concerns. Drawing on regulatory frameworks, the report proposes policy recommendations to mitigate concentration risks associated with cloud computing in the financial industry. By combining intelligent forecasting and evaluation technologies with cloud computing models, the study aims to provide effective solutions for financial data processing and management, facilitating the industry's transition towards digital transformation.

In the age of the Internet, people's lives are increasingly dependent on today's network technology. Maintaining network integrity and protecting the legitimate interests of users is at the heart of network construction. Threat detection is an important part of a complete and effective defense system. How to effectively detect unknown threats is one of the concerns of network protection. Currently, network threat detection is usually based on rules and traditional machine learning methods, which create artificial rules or extract common spatiotemporal features, which cannot be applied to large-scale data applications, and the emergence of unknown risks causes the detection accuracy of the original model to decline. With this in mind, this paper uses deep learning for advanced threat detection to improve protective measures in the financial industry. Many network researchers have shifted their focus to exception-based intrusion detection techniques. The detection technology mainly uses statistical machine learning methods - collecting normal program and network behavior data, extracting multidimensional features, and training decision machine learning models on this basis (commonly used include naive Bayes, decision trees, support vector machines, random forests, etc.).

Fintech is one of the industries that is skyrocketing due to the growing number of internet users. To increase the speed, security, and scalability of the financial industry, several technologies function in the background. One of the technologies that have significantly changed the financial industry in 2023 and beyond is artificial intelligence (AI). Financial organizations are focused on leveraging AI, which would be introduced in areas such as mobile banking, customer experience, cyber security, social banking, payments, branch automation, and operational efficiency. Due to its remarkable advantages, such as more effective business operations, superior financial analysis, and more consumer engagement, artificial intelligence (AI) and machine learning (ML) are increasingly being used in the finance industry. Artificial intelligence is not going out of trend anytime soon. But, what are the best use cases of AI in the fintech industry, how does it change the finance industry, and how can you profit from this new technology? This blog will address the technical aspects of bringing AI/ML to the finance industry and outline every aspect of AI in the finance industry. But before proceeding further, please go through the interesting stats.

If you just think about the last 5 years alone, how your conversations have changed between your family and friends. Some of you may not speak about technology at all, but we can admit that it's hard not to consider it is around us. The recent release of ChatGPT and now GoogleBard are taking the world by storm with their amazing capabilities. You start to look at these tools and figure out how they can improve your work life, the company's process, your personal life, and more. Artificial Intelligence is automating tasks that were once only capable of being done by humans.

SlateStone Wealth chief market strategist Kenny Polcari discusses when the Fed could cut rates and if it's too early to invest in A.I. on'Varney & Co.' Bloomberg, a leading financial data services provider, this week unveiled a new artificial intelligence (AI) model that aims to revolutionize the finance industry in the same way programs like OpenAI's ChatGPT are set to radically transform written communications. A research paper released by the company Thursday details the development of BloombergGPT, a new large language model (LLM) that has been trained on a massive amount of financial data to assist with a variety of natural language processing (NLP) tasks within the financial industry. In plain English, Blooomberg GPT is an advanced machine learning software that can rapidly analyze financial data to assist with making risk assessments, judge financial sentiment, and potentially even automate accounting and auditing tasks and more. The complexity and unique terminology of the financial industry requires an AI that is specifically trained with financial datasets, Bloomberg said in a release. BloombergGPT will have access to the vast quantity of data available on the Bloomberg Terminal -- a computer software system used by investors and financial professionals to access real-time market data, breaking news, financial research and powerful analytics.

NEW YORK – Bloomberg today released a research paper detailing the development of BloombergGPTTM, a new large-scale generative artificial intelligence (AI) model. This large language model (LLM) has been specifically trained on a wide range of financial data to support a diverse set of natural language processing (NLP) tasks within the financial industry. Recent advances in Artificial Intelligence (AI) based on LLMs have already demonstrated exciting new applications for many domains. BloombergGPT represents the first step in the development and application of this new technology for the financial industry. This model will assist Bloomberg in improving existing financial NLP tasks, such as sentiment analysis, named entity recognition, news classification, and question answering, among others.

Artificial intelligence in the FinTech industry is a topic that has created a big layer of curiosity around itself. The progress it has made over the past few years has left everyone either talking the whole day about it or speechless with amazement. Today, let's talk about how the FinTech industry has obtained a whole new outlook throughout the world with the help of artificial intelligence and machine learning. Are you ready to be enlightened? The AI in FinTech market size is projected to grow to $31.71 billion in 2027 at a CAGR of 28.6%.

Do you have a digital wealth management application for your investment portfolio that recommends investing in specific funds? You are likely using artificial intelligence (AI) to manage your money. From automating and optimizing processes to using conversational AI for enhanced customer engagement and fraud detection, AI and machine learning (ML) are leaving an indelible mark on banks and financial institution performance, completely disrupting the financial industry. In fact, the global market for AI in banking is expected to reach $64.03 billion by 2030. Today, 80% of banks are very aware of the potential benefits of implementing AI, and a majority are looking to deploy AI-enabled solutions.