Using the right tools for anti-money laundering compliance
Technology is one of a number of components in an effective global anti-money laundering (AML) compliance framework. By using current technology tools, organizations can improve their ability to mitigate financial crime risk. PwC has created a set of proprietary AML automated tools and techniques that can help.
These tools have been developed by our team of financial services, data, technology, risk and regulatory subject matter specialists. Our tools have gone through several iterations and benefit from the cumulative knowledge of our specialists. They are designed to help our customers meet their complex AML compliance challenges.
Customer Due Diligence Tool (CDD) : Web-based tool that acts as the single data entry point and risk rating for all existing and new customer and account data in support of Know Your Customer (KYC) requirements. Additional customer and account information captured includes, ultimate beneficial owners, officers/directors (non-individuals and financial institutions only), power of attorney, co-signers, and other related parties
Name/entity matching : Sophisticated matching and scoring tools and techniques that improve the searching of account and transaction information across systems, regions and business lines to create one view of the customer or to improve the name/entity screening (e.g. OFAC, PEP, etc) and matching processes (e.g. 314a, subpoenas, NSL, ad-hoc searches, etc)
Suspicious activity detection tuning : Advanced methods and techniques that improve the efficiency and effectiveness of transaction surveillance technology. We apply an empirical approach with an emphasis on statistical analysis of historical transaction data and alert output. By analyzing the population of data, institutions can identify trends and patterns and better determine which behaviors fall outside an acceptable range. Statistical analysis can be a first step in selecting appropriate rules and thresholds. Equally important is the reassessment of the monitored behaviors and thresholds over time. On-going analysis can be used to determine correlations and trends between productive and non-productive alerts allowing refinements that better target potentially suspicious activity, reducing overall review efforts.