This year there were almost (500,000) suspicious activity reports that were received and processed by the National crime agency period (80.21% of which came from banks); there has been a 52.72% increase in the amount of suspicious activity responses lately. For this hackathon I wanted to create a webapp that aids organizations by accurately stopping this problem and finding out whose done it.
What it does
Uses progressive NLP models and sentiment analysis to detect suspicious behavior and activity through entries, tweets, texts, etc.
How we built it
Used several api's, machine learning concepts like sentiment analysis, OCR, cockrach DB
Accomplishments that we're proud of
First time using a sentiment analyzer Achieved 99.98% confidence Was able to detect lots of emotions and scale it from -1,1 Used OCR with a 100% confidence rate .. compared it with google cloud vision api to make sure accuracy is right
What's next for Bust --> Breaking (down) User's Suspicious Texts
Promote this webapp .. and allow organizations + government + interrogators to use this to aid them in solving cases, etc.
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