Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer-based time series models with explainable artificial intelligence (XAI) to enhance the interpretability and accuracy of stock price predictions. The analysis focuses on the daily stock prices of the five highest-volume banks listed in the BIST100 index, along with XBANK and XU100 indices, covering the period from January 2015 to March 2025. Models including DLinear, LTSNet, Vanilla Transformer, and Time Series Transformer are employed, with input features enriched by technical indicators. SHAP and LIME techniques are used to provide transparency into the influence of individual features on model outputs. The results demonstrate the strong predictive capabilities of transformer models and highlight the potential of interpretable machine learning to empower individuals in making informed investment decisions and actively engaging in financial markets.

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.

The use of small language models (SLMs), herein defined as models with less than three billion parameters, is increasing across various domains and applications. Due to their ability to run on more accessible hardware and preserve user privacy, SLMs possess the potential to democratize access to language models for individuals of different socioeconomic status and with different privacy preferences. This study assesses several state-of-the-art SLMs (e.g., Apple's OpenELM, Microsoft's Phi, Google's Gemma, and the Tinyllama project) for use in the financial domain to support the development of financial literacy LMs. Democratizing access to quality financial information for those who are financially under educated is greatly needed in society, particularly as new financial markets and products emerge and participation in financial markets increases due to ease of access. We are the first to examine the use of open-source SLMs to democratize access to financial question answering capabilities for individuals and students. To this end, we provide an analysis of the memory usage, inference time, similarity comparisons to ground-truth answers, and output readability of prominent SLMs to determine which models are most accessible and capable of supporting access to financial information. We analyze zero-shot and few-shot learning variants of the models. The results suggest that some off-the-shelf SLMs merit further exploration and fine-tuning to prepare them for individual use, while others may have limits to their democratization.

Financial literacy (FL) represents a person's ability to turn assets into income, and understanding digital currencies has been added to the modern definition. FL can be predicted by exploiting unlabelled recorded data in financial networks via semi-supervised learning (SSL). Measuring and predicting FL has not been widely studied, resulting in limited understanding of customer financial engagement consequences. Previous studies have shown that low FL increases the risk of social harm. Therefore, it is important to accurately estimate FL to allocate specific intervention programs to less financially literate groups. This will not only increase company profitability, but will also reduce government spending. Some studies considered predicting FL in classification tasks, whereas others developed FL definitions and impacts. The current paper investigated mechanisms to learn customer FL level from their financial data using sampling by synthetic minority over-sampling techniques for regression with Gaussian noise (SMOGN). We propose the SMOGN-COREG model for semi-supervised regression, applying SMOGN to deal with unbalanced datasets and a nonparametric multi-learner co-regression (COREG) algorithm for labeling. We compared the SMOGN-COREG model with six well-known regressors on five datasets to evaluate the proposed models effectiveness on unbalanced and unlabelled financial data. Experimental results confirmed that the proposed method outperformed the comparator models for unbalanced and unlabelled financial data. Therefore, SMOGN-COREG is a step towards using unlabelled data to estimate FL level.

In todays competitive landscape, businesses grapple with customer retention. Churn prediction models, although beneficial, often lack accuracy due to the reliance on a single data source. The intricate nature of human behavior and high dimensional customer data further complicate these efforts. To address these concerns, this paper proposes a multimodal fusion learning model for identifying customer churn risk levels in financial service providers. Our multimodal approach integrates customer sentiments financial literacy (FL) level, and financial behavioral data, enabling more accurate and bias-free churn prediction models. The proposed FL model utilizes a SMOGN COREG supervised model to gauge customer FL levels from their financial data. The baseline churn model applies an ensemble artificial neural network and oversampling techniques to predict churn propensity in high-dimensional financial data. We also incorporate a speech emotion recognition model employing a pre-trained CNN-VGG16 to recognize customer emotions based on pitch, energy, and tone. To integrate these diverse features while retaining unique insights, we introduced late and hybrid fusion techniques that complementary boost coordinated multimodal co learning. Robust metrics were utilized to evaluate the proposed multimodal fusion model and hence the approach validity, including mean average precision and macro-averaged F1 score. Our novel approach demonstrates a marked improvement in churn prediction, achieving a test accuracy of 91.2%, a Mean Average Precision (MAP) score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid fusion learning technique compared with late fusion and baseline models. Furthermore, the analysis demonstrates a positive correlation between negative emotions, low FL scores, and high-risk customers.

NY Giants quarterback Eli Manning plays a video/computer game from Visa with USA TODAY's Ed Baig that teaches financial literacy to kids USA TODAY Eli Manning has some coaching tips for kids, but they have nothing to do with throwing the perfect spiral. Instead, the New York Giants quarterback is encouraging young people to learn about finance by helping promote a brand-new version of a trivia-based video game from Visa called "Financial Football 3.0." Manning doesn't appear in the game itself, but his participation, and that of his rookie teammate running back Saquon Barcley, is part of a longstanding NFL partnership with the global payments company. The app, which I got to play alongside Manning, is available as a free download for Android, iOS, Windows and Mac – Manning not included. New York Giants quarterback Eli Manning is lending his name to a video/computer game from Visa that is supposed to teach financial literacy to kids.

Banks must embrace digital transformation to create an entirely new banking experience that meet the demands and expectations of today's Millennials. They are looking for help, and you can either jump in and provide the assistance they need... or watch them save with someone else. According to Bankrate, 24% of adults say they have no money saved for an emergency like a layoff or medical bill. Even though this is its lowest level since the survey began in 2011, a whopping 25% of Millennials say they have no emergency savings whatsoever. "A lot of Millennials have seen their parent suffer financially through the recession," explains Andrea Woroch, a consumer saving expert explains.

Mumbai, March 20, 2017: Disruptive technologies lead to better customer experiences - from Internet to touch screens. We believe Artificial Intelligence is'The Fourth Transformation' in technology that will fundamentally redefine customer experiences. Today, YES BANK is proud to announce a big step towards adopting Artificial Intelligence (AI), in partnership with Payjo, a leading AI Banking Platform based out of Silicon Valley, California. The YES Pay Bot will be the first AI-driven Bot for a wallet and will complement the already trusted and popular YES Pay wallet service with over half-a-million users. YES BANK is launching its wallet services through a chat-based financial assistant in partnership with Payjo on Facebook Messenger.

From there, it won't be long before we begin to wonder how we ever lived without artificially intelligent financial advisors implementing our own personal monetary policy. U.S. financial literacy levels are unacceptably low, and the widespread availability of artificially intelligent money-management tools won't change that. By enabling us to make simple, direct decisions while taking care of the rest, artificially intelligent financial advisors will decrease the prevalence of consumer mistakes and prompt improvement in our overall financial health.I'm actually a perfect example of this point. And while this figures to make things physically easier, the process still won't be simple.