The United States has a spending problem—not just at the government level, but among individual households as well. Americans collectively hold over $1 trillion in credit card debt, and in a recent survey of 1,000 Americans, roughly one-third reported being maxed out on their credit cards. Overspending and undersaving aren’t just math problems; they’re deeply psychological ones. (I highly recommend The Psychology of Money for anyone interested in this topic.)
Tracking personal finances is harder than it sounds, especially when you’re busy or raising a family. For many people, “budgeting” ends up being a rough estimate—spending money and hoping the total stays below the next paycheck. Even for someone who pays attention, it’s difficult to keep track of every credit card purchase, watch for duplicate charges, or notice subtle fraudulent activity.
There are also broader financial decisions that most people know they should optimize but rarely do: shopping around for better car insurance every six months, understanding whether their insurance coverage is actually appropriate, or evaluating whether the terms in a property purchase contract are fair. These are high-impact decisions, and they’re exactly the kinds of problems where AI can be genuinely useful.
To explore this, I started building a personal finance application for my own use. The first capability I added was simple: detecting duplicate charges. I connected my bank and credit card accounts, downloaded my transaction history, and used a local Qwen-3 model with a custom prompt to analyze the data. The model flagged potential duplicates quickly, saving me the manual effort of scanning through hundreds of transactions.
Next, I wanted to see whether AI could surface anything of value in my spending patterns—not just errors, but insights. I wrote a second prompt designed to look for anomalies, unusually high recurring expenses, or optimization opportunities. In my case, my finances were generally in good shape, but the model did identify one useful suggestion: my car insurance premium was slightly high compared to similar spending profiles, and I should consider shopping around.
The most impactful use case, however, came when I purchased my apartment. At the time, I was busy with work and other life commitments, yet suddenly faced a stack of legal agreements, disclosures, and contracts that I was expected to review and sign within days. Before large language models, I likely would have skimmed these documents—or not read them at all.
Instead, I fed the contracts into an LLM and asked it to explain the terms, highlight risks, and point out areas that might be negotiable. To my surprise, the analysis was genuinely helpful. It identified issues I would have missed and gave me the confidence to push back. As a result, I saved a few thousand dollars, had several repairs addressed, and even received a cash credit for a non-functional refrigerator that the seller agreed to cover.
This experience made something clear to me: many personal finance mistakes aren’t caused by irresponsibility, but by cognitive overload, time pressure, and complexity. AI doesn’t replace judgment—but it can dramatically reduce the cost of paying attention.
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