Decision Tree (Day 103)

Image credit: Titans Finance

Decision tree is a quantitative way to select the best action. For example, for 50% of the Americans who have credit card debts, how can we optimize the distribution of disposable income to maximize utility (happiness) and minimize fees?

The payout is actually very simple to calculate. Let's say Alice has $500 disposable income, paying down credit cards (usually around 20-30% interest rate), Alice can save on the interest. But it is also important to have savings for emergencies, and Alice would also like to go out and have fun.

While the math is simple, there are many combinations and it is not trivial to reach an optimal strategy. We can run all different combination of distributions and help Alice to find an optimal strategy.

Once we have enough users, it is easier to get a better benchmark for each probability based on the cohort, and the analysis would become smarter through more data and more data feedback.

Instead of building a car that auto-drives, why can't we build a financial engine that helps people auto-navigate the complicated financial world?

That is the whole motivation behind the FAIR (Financial A.I. Recommendation) Engine project in Titans Finance.