How BlockFi Is Using Machine Learning To Take Crypto Safety to the Moon!
BlockFi is a cryptocurrency platform that allows its clients to grow wealth through various financial products capabilities, including loans, trading, and interest accounts.
The safety of our client's assets and personal information is taken very seriously by the security operations team.
What You'll Learn
This session showcases how BlockFi uses Splunk to identify operational risks and ensure the safety of client assets leveraging different machine learning techniques.
Machine Learning Techniques
Anomaly Detection
- Identifying unusual transaction patterns
- Detecting account takeover attempts
- Monitoring for suspicious behavior
Forecasting
- Predicting potential security threats
- Capacity planning for infrastructure
- Risk assessment modeling
Graph Analytics
- Data mining blockchain transactions
- Tracing fund flows across wallets
- Identifying connected threat actors
Use Cases
Fraud Identification
Machine learning models to detect fraudulent transactions and activities in real-time.
Account Takeover Prevention
Behavioral analytics to identify and prevent unauthorized account access.
Blockchain Analysis
Using graph theory to analyze blockchain data and ensure client safety through:
- Transaction tracing
- Address clustering
- Risk scoring
ML Operations
Improving machine learning operations through:
- Summarization - Efficient data aggregation for model training
- Feature Development - Engineering meaningful features from crypto data
- Model Deployment - Operationalizing models in production environments
Presentation Materials
Presented at Splunk .conf21