However, I believe that many individuals tend to connect finance overall with the process of learning. People are eager to grasp various methodologies and understand how finance operates—they aim to make more informed decisions by acquiring knowledge, not just about market trends or budgeting techniques, but also about forecasting, making predictions, and planning for future scenarios, which essentially is what budgeting entails.

In reality, investing follows a similar pattern. Naturally, it yields better results when guided by a long-term strategy.

AI in Finance

Considering all this, finance appears to be an ideal field for implementing solutions powered by large language models. These highly cognitive analytical systems can offer predictions and enhance our understanding of managing finances effectively.

Recently, I came across several presentations delivered by talented young professionals who are actively working on practical applications of artificial intelligence.

Out of these, two focused on finance, though they approached the subject from distinct perspectives.

Predicting Events

The first project was a predictive engine developed by Aroosh Krishna that analyzed the Kalshi exchange—despite its Indian-sounding name, the platform originated in the U.S., fairly recently.

I wasn’t previously aware of Kalshi, but upon researching, I discovered that the platform consistently attracts at least 1 million users, offering opportunities to bet on everything from natural disasters and elections to predicting what Trump might say within a given timeframe.

They refer to it as event-based investment.

In any case, a project named AnalyseKalshi utilizes sentiment analysis to assist in predicting outcomes. According to a flow chart illustrating how this system functions, there are two APIs involved.

Krishna emphasizes that one of the goals is to create a fairer environment between hedge funds and what he calls “casual investors.”

I found this to be a fascinating use of AI, even if it's applied to a market that seems somewhat lacking in seriousness.

In other words, our tools can provide highly accurate recommendations regarding specific types of outcomes, based either on the unpredictable emotions of, at times, unstable individuals, or on more substantial and meaningful predictions driven by advanced pattern recognition capabilities offered by LLMs.

Budgeting for Teens

The second presentation revolved around building personal finance tools tailored for teenagers or young adults.

Presenter Viren Kedia explained that the app aims to help teens learn how to invest, budget, and understand taxation.

Research notes displayed on the presentation banner indicated that within two days, teens experienced a 30% improvement in their financial literacy quiz scores.

This is significant because the quiz results demonstrate to users what they're learning and support the benchmarking features that the app provides.

Kedia further mentioned that users appreciate three key aspects of the application: the free version, an effective interactive chatbot, and video features.

Teens gain clarity on the distinction between a budget and a financial plan, as well as how to set realistic financial goals.

All of this contributes to better financial education for this user group, helping them develop improved money management skills, and yes, even certain predictive abilities.

Are these tools capable of delivering what’s expected? The proposed plans seem feasible, and both projects clearly present strong value propositions. I’ll keep sharing some of the most intriguing initiatives on the blog, so keep following along.

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