Case Study
Trade based money laundering detection
Data | Banks | Amsterdam & Milan
The Background
- A large, Netherlands based bank, with a global footprint, asked Quantexa to support them in the identification of fraud and money laundering activities within their Trade Finance book
The Challenge
- Disparate data across multiple systems
- No single view of the client across location
- Lack of an understanding of fraud in the book
- Limited ways of identifying fraud and, as such, no real early warning signals to identify and stop fraud
The Approach
- Provision of a Quantexa and NextWave team
- Project scoping and planning
- Data sourcing and cleansing
- Delivery of the single client view
The Impact
- Within a 6 month period, data was sourced, cleansed and channeled through the Quantexa platform
- Deployment to production immediately led to numerous potential fraud cases being identified
- Further use cases are now being deployed for Trade across other locations
Related Case Studies
Big data engineering
Data strategy for a global reinsurance business
Trade based money laundering
Using decision intelligence to identify prospective customers
Data and technology strategy for an insurance and technology group
Trade anti-money laundering for a major Japanese investment bank
Corresponding banking: Improving Transaction Monitoring Controls (TMC)
Interested in digital acceleration?
Subscribe to NextWave for occasional email updates on the latest technology, acceleration approaches, news and NextWave events. You can unsubscribe from these communications at any time.