The velocity crypto monitoring module provides advanced analysis of transactions on the blockchain network. It helps to screen bad transactions, terrorist financing activities, and money laundering.
With the proper implementation of cutting-edge technology (Artificial Intelligence and Machine Learning), Velocity’s crypto monitoring module analyzes the transaction patterns and tracks the risk indicators to provide end-to-end compliance for the digital currency ecosystem.
- Transaction data
- NLP augmentation techniques
- FPR module’s algorithm
- Sentiment analysis for converting linguistic to quantitative
- Machine learning for detecting pattern
- Deep learning via convolution graph
- Fuzzy matching for constructing identity from partial trace
- Risk-flagged items
- Matched transactions
Cryptocurrency empowering criminals?
The ability to hold Digital Currencies without revealing their identity is what makes them attractive to criminals. Fraudsters leverage the anonymity of blockchain technology to launder money. It is mainly because there is no proper AML regulation in the crypto market. This makes money launderers hide amongst the honest transactions and move payments across the borders with complete confidentiality.
Velocity’s Translation identifier module equips financial institutions with faster identification of crypto transactions. The data augmentation module provides an unbiased combination of transactions with matched keywords. This makes it easy for financial institutions to venture into the world of digital currency.
- Track origin of transactions using blockchain technologies.
- Remove concern about dark money and money laundering.
- Manage a diverse array of regulators and authorities across various regions.
- Provide a common diagnostic and priorities so that banks can have uniform risk policies.
- Monitor and protect cryptocurrency assets for customers and banks.
- Create opportunities for bank revenues and improved customer service.
Red flag for Criminals
Velocity’s algorithm can easily identify hidden patterns and risks.
Risk-flagged items are identified to determine true risk.
Velocity can identify the following transactions accurately and effectively:
- Dark money transactions, illicit trade, and criminal activities
- Ransom extortion or drug payment
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Reduced False Positives
Reducing false positives is important for compliance managers because it helps to minimize the amount of time and resources spent on investigating alerts that turn out to be incorrect or irrelevant. Thus, our state of the art Machine Learning algorithms will provide faster and more accurate results
Unbiased Data Augmentation
One of the most fascinating uses of Deep Learning is Natural Language Processing (NLP). Built upon the NLP augmentation technique, the data augmentation module implemented in our module is unbiased to set of biased terms
Increased efficiency due to Blockchain
Blockchains provide complete transparency, making it easier to detect crimes related to Digital Currency compared to traditional finance crimes. By searching for a crypto address on a blockchain explorer, one can access a comprehensive transaction history for that particular address. Thus, making it easier to track fraudulent transactions.
Prompt Collaboration with Authorities
In the fight against fraud, it is essential to have cooperation among the crypto industry, conventional financial institutions, and law enforcement. Our module can be integrated with our Case Management & Reporting module for efficient collaboration between various entities.
Our module helps screen transactions and protect businesses from FinCrime activities.
Faster and accurate results
With the help of AI and Machine Learning, this module provides faster and more accurate results.
The insights provided by the module allows complete breakdown and easy-to-understand analysis to decipher transactions.
By using automated and smart tools, this module can screen large amounts of data, which is not possible to process manually.
Detect high risk
The module has an algorithm that is used to identify hidden patterns and risks associated with the transactions.