Fraudcatcher.ml

Inspiration

In a world of evolving threats, with the rise of advanced technology, fraudsters are tricking consumers out of more money than ever before. In the first half of 2023, the fraud transaction count record is 1.1 million which increased to 1.3 million in 2024. This reason inspired us to select this project and want to make sure a secure environment and a safer transaction.

What it does

We designed a machine learning model, which detects the fraud transactions using previous transaction history, which helps banks to prevent fraud transactions and protect the customers. So, it is all about detect.prevent.protect.

How we built it

As data is everywhere, we tried to collect the data from the government source. Using this, we leveraged various machine learning models and selected the model which gives the better performance in detecting the fraud transaction.

Challenges we ran into

The challenges we faced during the design of machine learning system are working on sensitive data, it is difficult to collect and get insights from it. Additionally, it consists of imbalance data and the other challenging figuring the best machine learning model which leverages the good metrics...

Accomplishments We Are Proud Of

We developed an effective machine learning model for detecting fraudulent transactions, ensuring better protection for consumers and banks. We successfully gathered and utilized sensitive financial data, optimized our model for accuracy, and prioritized user experience in our design. Our strong teamwork and adaptability in overcoming challenges enhanced our project, and we gained valuable insights that will aid us in future endeavors.

What We Have Learned

We learned how to handle sensitive financial data while ensuring privacy and security. We became skilled at comparing different machine learning models to find the best one for detecting fraud. We also tackled the issue of imbalanced data to make our model more effective. Understanding the importance of feature selection helped us improve our model’s accuracy. Working together improved our teamwork and communication. We recognized the importance of continuous learning in the fast-paced field of machine learning. Finally, we focused on user experience to make our system easy to use.