Dr. Roman Vichr

Roman is a senior AFC/AML / Risk Compliance Chief Scientist and Senior Compliance Model & Data Architect who specializes in Risk Modeling within the KYC, AML Transactions Monitoring & Sanctions space. Roman leads the Risk Modeling, Tuning, Optimization, Validation Practice and is based in Washington D.C. with over twenty year of experience. He focuses on Financial Services and specializes in AML (OFAC, BSA, Patriot Act) Compliance, Trade and Risk Management and Model Governance. He has implemented multiple times mission critical solutions globally for leading financial institutions, which were accepted by regulators. In early 2000’s Roman supported the original design and development of Oracle Mantas AML detection platform and data ingestion engine. Roman is an exceptional technologist with depth knowledge of regulatory compliance environment (closed multiple MRIAs, MRAs) various technologies (including graph databases and big data). Roman has extensive experience in implementing and managing large-scale mission-critical solutions for trading compliance and risk management, AML and Compliance at financial institutions like: Deutsche Bank AG, UBS AG, Morgan Stanley, Citigroup, Credit Swiss, Union Bank, LPL Financial, Bank of Montreal, USAA, BoA (Merrill Lynch), Bank of the West, Charles Schwab, FINRA, BNPP and others.

Roman demonstrated acuity in successful scenario development & deployment as well as validation (MRM) across firm’s lines of business, various products and risks. He’s performed advanced model validation, ongoing monitoring model’s threshold tuning/optimization utilizing quant as well as policy driven data mining techniques. His depth of technology understanding quarantined successfully delivered AML and Risk management solution. These effort were successfully delivered in teams under technology , time constraints and on various in accordance to OCC recommendations.

Roman lead efforts and delivered solutions to validate various CDD, negative news, OFAC compliance, Trading and AML detection techniques , rules and models across financial lines of business (equity trading, fixed income, energy trading, private banking, corresponding banking, retail and financial service banking etc.) providing analysis later accepted by regulators.

Roman applied Machine Learning (ML) and AI techniques to benefit model’s efficacy (Type I and II).