Banks worldwide spend more than $8 billion annually on Anti-Money Laundering (AML) activities, and the market is predicted to have a compound annual growth rate of almost 20% through 2024. Unfortunately, criminals are also accelerating their activity, exploiting increased volume and breadth of transactions across financial institutions. Only by maximizing efficiency and sophistication in examining suspicious transactions can enterprises stay ahead.

Complexity and conflict
Financial regulations require banks to Know Your Customer, to understand the relationships between transacting parties and to be able to match against watch lists. Yet, increasing privacy regulations, including GDPR and CCPA, require even tighter control of personal customer data, which affects how organizations can safely use data in AML/anti-fraud processes.

Larger teams increase risk
Many organizations now leverage third parties and are expanding their Anti Money Laundering departments to ensure compliance with government regulations and company policies. But with more data users comes greater risk. In addition to outsider threats, in 2019, 60% of reported data breaches were due to insider threats or negligence.

Data security is not enough
AML/anti-fraud analysts and automation tools must make extensive use of sensitive customer data. Traditional security measures to protect data at rest and in transit are commonplace yet insufficient. Data must also be protected in use. And organizations must implement defensive measures in anticipation of the inevitable breach.

Maintain data utility while protecting sensitive data

• Privacy protection of sensitive customer information
• Controlled linkability of datasets
• Separation of authority for managing data privacy policies
• Centralized control for systematically enforcing policies and procedures
• Dataset tracking during all steps of the AML process

AML/Anti-fraud processes now require data privacy
With the Privitar Data Privacy Platform, enterprises can maximize their data usage in accordance with increasing regulations while also leveraging automation that allows them to simultaneously improve AML efficiency and output. Adding Privitar to an organization’s AML/Anti-Fraud procedures will provide a scalable, holistic approach to data privacy that includes:

• De-identification of primary and pseudo-identifiers
• Centralized policies for consistency and control
• Managed linkability provides referential integrity where required and prevents it elsewhere
• Role separation of analysis, re-identification and investigation
• Watermarks for dataset identification and traceability