The problem FinWise AI solves

Financial Exclusion in Underserved Communities

1.7 billion adults globally remain either unbanked or underbanked, with limited or no access to traditional financial services such as savings accounts, credit, or investment products. This issue disproportionately affects low-income individuals and communities, particularly those in rural areas or developing regions.

Key Challenges:

1. Limited Financial Literacy: Many underbanked individuals lack the basic financial education needed to manage their money effectively. Complex financial concepts like interest rates, inflation, and debt consolidation can be confusing and lead to poor decision-making.

2. Barriers to Financial Services: High fees, minimum balance requirements, and geographic limitations prevent underserved individuals from accessing traditional banking services. Many rely on costly alternatives like payday loans or check cashing services, leading to financial instability.

3. Struggling with Debt and Savings: The lack of personalized guidance means individuals often struggle with managing debt, saving for emergencies, or building long-term wealth. Without proper advice, they are at risk of falling into cycles of debt and poverty.

4. Overwhelming Financial Jargon: The financial services industry often uses complex and intimidating language, making it difficult for those with lower financial literacy to access and understand financial products or services.

Challenges we ran into

One of the most significant hurdles we faced during the project was that it was my first time ever utilizing AI and Large Language Models (LLMs). Venturing into the world of AI came with a steep learning curve, especially when it came to understanding how to set up and optimize the system for personalized financial assistance.

A major challenge involved configuring the AI infrastructure using GaiaNet and its distributed nodes. GaiaNet’s architecture, while powerful, was initially complex to navigate, requiring me to understand how data flows between nodes and how to properly scale the AI model across the network. I ran into several technical issues, particularly around:

• Integrating personalized financial data without overwhelming the model.
• Managing real-time performance across the node to ensure smooth, accurate interactions.
• Setting up my own LLM and Dataset for the system.

To overcome these challenges, I spent a lot of time researching AI infrastructure, collaborating with experts, and conducting trial-and-error testing. By breaking down each problem into manageable parts and focusing on incremental progress, I eventually managed to get the nodes functioning efficiently, enabling seamless AI responses while maintaining data security and privacy.