Created on 4th November 2023
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In the world of digital finance, the rise of cryptocurrencies has been meteoric. While they offer unprecedented opportunities for investment and transaction, they have also opened up new avenues for financial misconduct, such as fraud and money laundering. Traditional methods of tracking and scrutinizing financial transactions are often ill-suited to the decentralized and anonymous nature of blockchain-based currencies. This is where "DMD" steps in as a game-changer.
Our web app, "DMD," is designed to tackle this very challenge by providing a robust analysis of wallet transactions within the blockchain ecosystem. Here’s how "DMD" stands out and solves pressing problems:
In summary, "DMD" is not just a tool—it's an essential ally in maintaining the integrity of financial transactions within the digital space. It transforms the complex and often opaque landscape of blockchain transactions into a more secure and transparent domain, ensuring that the digital economy remains a level playing field for all participants.
Our initial challenge was sourcing the right dataset. Cryptocurrency transactions are diverse and complex, and finding a dataset that accurately represented this complexity was not straightforward. We searched through numerous sources to find data that was both comprehensive and relevant to our needs.
Once we had a dataset that closely aligned with our requirements, but the dataset was smaller than we hoped. It was about 7K entries and we needed more data to train our model effectively.
To tackle this, we tried synthetic data augmentation, but this introduced a risk of polluting the model with data that could lead it astray. We had to be meticulous in cleaning and serializing the dataset, ensuring that each entry was relevant and accurately represented a real-world scenario.
Despite these efforts, our initial model iterations didn't perform as expected. The outputs were not reliable enough for a tool that aims to provide security and confidence in financial transactions.
The breakthrough came when we combined advanced data augmentation techniques with rigorous validation methods. We implemented a multi-tiered approach to model training, which involved:
We trained the model and it could discern patterns and flag anomalies with high precision making it reliable. success :)
Tracks Applied (4)
Polygon
Polygon
Replit
Major League Hacking