Can AI Help with Your Personal Finances?

Abstract: In recent years, LLMs have emerged as a transformative development in 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 paper demonstrates that leading LLMs achieve about 70% accuracy overall in addressing US personal finance queries, with some models reaching up to 78%.
• It employs comparative analysis across topics like mortgages, taxes, loans, and investments to highlight strengths and variability among different AI models.
• The study underscores the potential of AI to democratize financial advisement while noting challenges such as complex queries, ethical concerns, and algorithmic bias.

Can AI Help with Your Personal Finances?

The paper "Can AI Help with Your Personal Finances?" evaluates the capacity of LLMs to address issues in personal finance within the United States. The inquiry stands at the intersection of technological advancement and financial advisory, scrutinizing whether AI can effectively fulfill the role traditionally held by financial experts. Prominent models such as OpenAI's ChatGPT, Google's Gemini, Anthropic's Claude, and Meta's Llama are considered, with notable attention paid to their comparative performance across various finance-related topics: mortgages, taxes, loans, and investments.

Key Findings

The study reveals that these LLMs, on average, offer approximately 70% accuracy in answering personal finance questions. Performance differs significantly depending on the complexity and subject matter of the questions. Specifically, models like ChatGPT 4 and Claude 3.5 Sonnet outperform others, achieving accuracy rates upward of 78%, whereas Llama 3 8B lags with an accuracy rate of around 53%.

A critical aspect evaluated was the models’ consistency. Across multiple trials, the models maintained consistent accuracy levels, assuaging concerns regarding variability in AI-driven responses. The investigation further reveals an iterative improvement in LLM capabilities, with newer iterations consistently outperforming their predecessors, suggesting ongoing enhancements in model training and algorithmic sophistication.

Topic-Based Performance

The paper categorizes LLM performance into several financial domains. Analyzing various topics provides insight into the strengths and limitations of these models. In areas concerning 'women's financial literacy,’ most models demonstrated high performance—reflecting the diverse datasets employed in their training. In contrast, domains such as 'credit card literacy' evidenced a greater variance in accuracy, with ChatGPT versions leading in accuracy. When assessed on complex financial processes like mortgage and investment strategies, models generally achieve moderate success, indicating room for advancement in handling intricate financial advisement.

Implications and Future Directions

While the present capabilities of LLMs in personal finance underscore considerable promise, there remain notable limitations, particularly in addressing complex and nuanced queries. The implications for broader AI applications in finance are significant. For instance, technology could democratize access to financial advisement, offering cost-effective, scalable solutions to individuals who might otherwise lack access to personal advisors. Moreover, AI could enhance professional advisory capacity by enabling deeper analytical insights and efficiency in financial planning processes.

Nevertheless, the advancement of LLMs introduces ethical considerations, notably concerning data privacy, algorithmic bias, and the potential for misuse in providing misleading financial advice. Addressing these concerns requires robust privacy protections, continuous audit measures to counter bias, and regulated frameworks to prevent high-risk advisement.

For future research, this paper suggests avenues such as integrating real-time market data into LLM advisement to enhance decision accuracy and exploring AI's applicability in behavioral finance. Further development of model interpretability is essential to facilitate trust in AI-provided guidance, thereby broadening the scope of AI-based financial advisory services.

In conclusion, as LLMs continue to improve their grasp of personal finance, their role in both everyday financial literacy and sophisticated advisory systems is likely to expand, promising advancements in personal and professional financial decision-making.