Rule-Based Classifiers for Financial Fraud

Inspiration

After our first hackathon, we became very interested in machine learning, and because of our interest in both finance and technology, we decided to learn about what Fintech applications could detect financial fraud effectively.

What it does

Our business model presentation first explains the concept of financial fraud and showcases its consequences, and then dives into what machine learning is and how it can be used in this context. We specifically focus on rule-based classification and the decision tree classifier and added an example for a better understanding. Finally, it is explained what makes machine learning more effective and concluded with what action should be taken.

How we built it

We used Figma to design and create the visuals for our presentation.

Challenges we ran into

Many of the machine learning concepts such as Gradient Boosting was extremely difficult to understand and thus, we weren't able to incorporate them into our presentation. Within both the machine learning and financial scope, there were some terms that we needed to look more thoroughly into for us to understand.

Accomplishments that we're proud of

Being able to grasp a solid understanding of the decision tree with machine learning in a limited time frame. Researching a Fintech topic that can and should be applied to the real world.