Greater Visibility into Trading Floor Communications Saves This Bank $7 Million in Projected Investigation Costs
Customer Company Size
Large Corporate
Region
- Europe
Country
- France
Product
- Forcepoint Behavioral Analytics
Tech Stack
- Natural Language Processing
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
Technology Category
- Analytics & Modeling - Big Data Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Regulatory Compliance Monitoring
- Fraud Detection
Services
- Data Science Services
About The Customer
The customer is an international banking group with a presence in more than 70 countries. In addition to retail activities, it is also a leading global investment bank. The bank operates in a highly regulated world of investment banking, where new communication channels like Skype and WhatsApp, and web forums like Investopedia, are raising the risk of widespread abuse, whether through insider trading, fraud, or leaks of sensitive company information. The bank's operations and compliance leadership knew they needed a better way to stay close to trader conversations and safeguard the trading floor.
The Challenge
The international banking group was facing challenges in meeting SEC compliance regulations due to the increasing use of personal messaging channels and web forums by traders. The bank's existing monitoring technology was outdated and required a high level of manual intervention to identify and investigate potential compliance issues. The system was not covering the full spectrum of modern communication channels and offered no systematic ways to flag potential indicators of fraudulent behavior. These time-consuming investigations, a vast majority of them false positives, were costing the bank millions of dollars.
The Solution
The bank partnered with Forcepoint to build a program to modernize and optimize its monitoring technology. Forcepoint Behavioral Analytics was used to provide a robust picture of trader activity and communications, with deeper context into what was really happening on the trading floor. The solution ingested old email data as well as other electronic communications like Skype chats, Bloomberg terminals, and more, and analyzed them for potential indicators of insider trading activity. Natural language processing was used to analyze unstructured data, like the contents of email and chat messages. The bank also monitored all information that its traders send and receive. When all of these data sources were compiled and analyzed, a very clear picture of behavior and context began to emerge.
Operational Impact
Quantitative Benefit
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