Personalized financial advice requires consideration of user goals, constraints, risk tolerance, and jurisdiction. Prior LLM work has focused on support systems for investors and financial planners. Simultaneously, numerous recent studies examine broader personal finance tasks, including budgeting, debt management, retirement, and estate planning, through agentic pipelines that incur high maintenance costs, yielding less than 25% of their expected financial returns. In this study, we introduce a novel and reproducible framework that integrates relevant financial context with behavioral finance studies to construct supervision data for end-to-end advisors. Using this framework, we create a 19k sample reasoning dataset and conduct a comprehensive fine-tuning of the Qwen-3-8B model on the dataset. Through a held-out test split and a blind LLM-jury study, we demonstrate that through careful data curation and behavioral integration, our 8B model achieves performance comparable to significantly larger baselines (14-32B parameters) across factual accuracy, fluency, and personalization metrics while incurring 80% lower costs than the larger counterparts.

This study examines religious biases in AI-generated financial advice, focusing on ChatGPT's responses to financial queries. Using a prompt-based methodology and content analysis, we find that 50% of the financial emails generated by ChatGPT exhibit religious biases, with explicit biases present in both ingroup and outgroup interactions. While ingroup biases personalize responses based on religious alignment, outgroup biases introduce religious framing that may alienate clients or create ideological friction. These findings align with broader research on AI bias and suggest that ChatGPT is not merely reflecting societal biases but actively shaping financial discourse based on perceived religious identity. Using the Critical Algorithm Studies framework, we argue that ChatGPT functions as a mediator of financial narratives, selectively reinforcing religious perspectives. This study underscores the need for greater transparency, bias mitigation strategies, and regulatory oversight to ensure neutrality in AI-driven financial services.

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.

In recent years, Large Language Models (LLMs) have emerged as a transformative development in artificial intelligence (AI), drawing significant attention from industry and academia. Trained on vast datasets, these sophisticated AI systems exhibit impressive natural language processing and content generation capabilities. This paper explores the potential of LLMs to address key challenges in personal finance, focusing on the United States. We evaluate several leading LLMs, including OpenAI's ChatGPT, Google's Gemini, Anthropic's Claude, and Meta's Llama, to assess their effectiveness in providing accurate financial advice on topics such as mortgages, taxes, loans, and investments. Our findings show that while these models achieve an average accuracy rate of approximately 70%, they also display notable limitations in certain areas. Specifically, LLMs struggle to provide accurate responses for complex financial queries, with performance varying significantly across different topics. Despite these limitations, the analysis reveals notable improvements in newer versions of these models, highlighting their growing utility for individuals and financial advisors. As these AI systems continue to evolve, their potential for advancing AI-driven applications in personal finance becomes increasingly promising.

Purpose: Generative artificial intelligence (GenAI) has progressed in its ability and has seen explosive growth in adoption. However, the consumer's perspective on its use, particularly in specific scenarios such as financial advice, is unclear. This research develops a model of how to build trust in the advice given by GenAI when answering financial questions. Design/methodology/approach: The model is tested with survey data using structural equation modelling (SEM) and multi-group analysis (MGA). The MGA compares two scenarios, one where the consumer makes a specific question and one where a vague question is made. Findings: This research identifies that building trust for consumers is different when they ask a specific financial question in comparison to a vague one. Humanness has a different effect in the two scenarios. When a financial question is specific, human-like interaction does not strengthen trust, while (1) when a question is vague, humanness builds trust. The four ways to build trust in both scenarios are (2) human oversight and being in the loop, (3) transparency and control, (4) accuracy and usefulness and finally (5) ease of use and support. Originality/value: This research contributes to a better understanding of the consumer's perspective when using GenAI for financial questions and highlights the importance of understanding GenAI in specific contexts from specific stakeholders.

With a major focus on its history, difficulties, and promise, this research paper provides a thorough analysis of the chatbot technology environment as it exists today. It provides a very flexible chatbot system that makes use of reinforcement learning strategies to improve user interactions and conversational experiences. Additionally, this system makes use of sentiment analysis and natural language processing to determine user moods. The chatbot is a valuable tool across many fields thanks to its amazing characteristics, which include voice-to-voice conversation, multilingual support [12], advising skills, offline functioning, and quick help features. The complexity of chatbot technology development is also explored in this study, along with the causes that have propelled these developments and their far-reaching effects on a range of sectors. According to the study, three crucial elements are crucial: 1) Even without explicit profile information, the chatbot system is built to adeptly understand unique consumer preferences and fluctuating satisfaction levels. With the use of this capacity, user interactions are made to meet their wants and preferences. 2) Using a complex method that interlaces Multiview voice chat information, the chatbot may precisely simulate users' actual experiences. This aids in developing more genuine and interesting discussions. 3) The study presents an original method for improving the black-box deep learning models' capacity for prediction. This improvement is made possible by introducing dynamic satisfaction measurements that are theory-driven, which leads to more precise forecasts of consumer reaction.

AI chatbots can write a poem, do your homework, draft lawsuits, and maybe even take your job, if the hype is to be believed. Can they handle your investments, too? While the use of artificial intelligence in the realm of financial advice is nothing new--"robo-advisors" have been around for years, some of which use AI--the chatbot technology is rapidly becoming more accessible to individual investors. Google's Bard and Microsoft's chatbot, powered by ChatGPT and integrated into its Bing search engine, can interact with users in plain English and can engage in surprisingly human-seeming interactions. To test the investing abilities of Microsoft and Google's respective products, we challenged each one to pick two stocks--one growth stock and one value stock--and see how they did over a three-week span compared to one another as well as a human.

Since the launch of OpenAI's ChatGPT, the chatbot had taken the world by storm. No wonder ChatGPT has made its way to the FinTech Industry too. OpenAI's ChatGPT is revolutionizing FinTech Industry and for all the right reasons. The usage of chatbots in the financial sector to enhance customer service and assistance is transforming the way we interact with technology. Compliance is a major issue in the heavily regulated financial sector.

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This article introduces one of my value investments starting about a month ago. C3.Ai appears to be incredibly undervalued, the earnings report is bullish along with the recent Ai momentum. Ai stocks have taken a massive hit during this bear market presenting a possibly massive discount on the sector. Artificial Intelligence is growing and advancing whether we like it or not. Those who do not embrace, or least educate themselves on the topic may fall behind in the next few years.