Virtual, Reality Part 1: Using Artificial Intelligence Effectively

Generative artificial intelligence (referred to as GenAI, or AI here) is more than just a tech headline – it’s reshaping how law students and new lawyers are making money decisions as they budget for bar prep, repay student loans, and choose between public and private careers.

According to a recent survey from Intuit Credit Karma, about eight out of 10 Gen Z and Millennial respondents who have used GenAI before say they use it to seek financial advice, asking everything from basic budgeting questions to questions about more advanced topics like investing and taxes.

While AI is making financial information more accessible than ever, these statistics do raise important questions: How and when do we use AI in the decision-making process? How should it augment, but not replace human support? And can we trust the information we receive for some of life’s biggest decisions?

Using AI at the Beginning of the Decision-Making Process, Not the End

It’s widely known – and even disclaimed on most platforms – that, as powerful as they are, AI models aren’t always 100% accurate. And because they can lag real-time data and events and must be prompted to ask open-ended, exploratory, personal questions about your situation (which may give you pause) to arrive at more nuanced outputs, they are not good replacements for the empathetic, emotional intelligence of an experienced professional.

That said, AI models have become far more capable than simple chatbots of the past. Today’s models can:

• Summarize complex financial topics in plain language.
• Compare repayment and refinancing options using recent or real-time data.
• Simulate multiple budget or career scenarios.
• Help develop frameworks or planning tools to crunch your own numbers.
• Act as “agents” at the core of a more complex system to track data and automate certain processes.

AI can effectively summarize complex topics, generate rough projections instantly, or run various scenarios to give you a clear starting point for deeper analysis.

Think of your decision-making process as a funnel. At the top, you gather all relevant information, perspectives, and inputs – this is where AI models excel, helping you process and organize vast amounts of data and information. But the next layer is critical: professional verification. This step reviews and enriches AI-generated insights with human judgment, contextual understanding, and details that the AI may have missed or underemphasized. Together, these layers create a more informed, reliable, and responsible decision-making process.

AI Outputs, Explained?

But how do you know if the outputs you are receiving from your AI model of choice are accurate (or even unbiased) enough to include at the top of your decision funnel? This is one of the biggest questions facing AI users in these early days of the technology’s widespread adoption, particularly regarding how an output was produced and by which sources. This complex process – often referred to as a “black box” by AI developers – has necessitated the movement for explainable AI (XAI).

The goal of XAI is to make complex AI models more transparent, easy to understand, and accountable by showing how they arrive at certain outcomes. This is obviously critical in financial decision-making, which is heavily dependent on accurate data, numbers, assumptions, regulations, and real-world economic trends. The good news is that XAI has already made its way into operations of some financial institutions and tech companies – such as lenders using XAI to justify automated credit approvals and denials, fraud and money laundering detection, and algorithmic trading and investor risk assessment on robo-advising platforms.

Using AI Wisely: A Quick Checklist

1. Define your goal (e.g., I’m researching the benefits and drawbacks of refinancing my federal student loans).
2. Manage your privacy settings before inputting your questions (e.g., opt out of using prompts for training purposes).
3. Provide context based on your comfort level with managed security options.
4. Always avoid inputting personally identifiable information or information you wouldn’t otherwise share online.
5. Ask for multiple scenarios. Best case, worst case, and middle ground.
6. Define how you would like the AI to respond (e.g., “Explain this like I’m a first-year law student.”)
7. Ask for sources, then verify them.
8. Ask the model to ask clarifying questions for more relevant outputs.
9. Confirm with a professional. Don’t act solely on AI output.

Verify, Verify, Verify

As stated, AI tools are impressive, but they still make mistakes – sometimes so confidently that it’s hard for us to spot inaccuracies.

So, before you act on financial information:

• Cross-check key facts on official sources like StudentAid.gov, IRS.gov, FTC.gov, or Investor.gov.
• Ask the AI to cite sources or assumptions (“What data is this based on?”).
• Check time relevance to ensure the model has regenerated its data to reflect the most up-to-date information.
• Always use human confirmation – like free coaching calls with Accredited Financial Counselors through AccessConnex by AccessLex^℠ – for big financial moves, like refinancing, retirement contributions, and home purchases.

If something sounds too optimistic (or too grim), always double-check before you act.

The Bottom Line

AI won’t replace sound financial judgment, but it can make learning and comparing options much easier. Used wisely, it’s a tool that empowers you to understand your money better, clarify your goals, and ask sharper questions.

So: explore, verify, and keep your human advisers in the loop. Done right, AI can be a powerful tool on your path to financial capability – from 1L year to professional practice and beyond.

Read Part 2 of “Virtual, Reality” to take a deeper dive into practical application of AI to financial decisions!