White collar crime is one of the most significant risks facing large corporations and detection of fraud is a difficult challenge to tackle.
- Build clustering models that detect unusual transactions by training the AI model to understand what “normal” transactions look like
- Examples of transaction data captured are amounts, frequency, times, user, type of transaction, resources used by the transaction and related transactions
How to Customize
- Large amounts of transaction data capturing the data listed above
- Applicable to fraud/embezzlement and electronic theft
We were able to successfully train an AI model to recognize complex industrial parts using Neopulse 3.0 on AWS. The AI solution was built very quickly and was able to recognize objects in unpredictable, real-world environments with high accuracy.
Everybody seems confident in AI, and they actually enjoy solving various AI problems.
The vendor’s services are integral to providing AI solutions for a wider audience. They had an effective project management style, accented by a quick working style.
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