Director of AI and Intelligent Systems for Institutional Client Onboarding and Risk Management at Bank of Montreal (BMO)
New York, United States
Lev Yatsemyrskyi
Council Member
Lev Yatsemyrskyi serves as Director of AI and Intelligent Systems for Institutional Client Onboarding and Risk Management at Bank of Montreal, bringing over 12 years of expertise in transforming financial infrastructure through artificial intelligence and agent-based technologies. Based in the United States, he leads enterprise-wide strategy for AI-powered onboarding platforms, intelligent risk controls, and digital transformation initiatives serving major institutional clients across North America.
Since joining Bank of Montreal in February 2026, Lev Yatsemyrskyi has architected and deployed next-generation agent-based and AI-powered platforms that deliver real-time risk analytics, regulatory compliance, and seamless institutional integration for one of North America's largest banks. His work directly supports the bank's mission to strengthen capital markets infrastructure while maintaining full regulatory alignment with FINRA, SEC, and international standards.
Prior to Bank of Montreal, Lev Yatsemyrskyi spent six years at Nasdaq, where he served as Director of Client Integrations and AI Functionality and Technical Product Manager for the Nasdaq Risk Platform from March 2021 through January 2026. In this capacity, he designed and executed end-to-end strategy, roadmap, and delivery for cloud-native risk management and real-time analytics systems operating in high-frequency trading environments. He served as the primary technical escalation point and integration architect for Nasdaq's largest institutional clients, including Bank of America Merrill Lynch, JPMorgan Chase, Citigroup, TradeWeb, Morgan Stanley, and Amazon. His work encompassed FIX Protocol optimizations, Kafka-based event streaming, drop-copy workflows, and AI-driven configurations supporting petabyte-scale market data feeds. Under his leadership, the Nasdaq Risk Platform achieved transformative results: 30% reductions in integration times, 200% onboarding efficiency gains, zero-delay cloud migrations for over 400 workflows, and 99.9% platform uptime while generating 80% of NRP revenue through high-frequency trading operations. He consistently delivered 95% client satisfaction across more than 100 institutional clients while ensuring full regulatory compliance with FINRA and SEC requirements.
From March 2018 to February 2020, Lev Yatsemyrskyi served as QA Manager and Technical Product Manager at Citi in New York, where he led client success, QA environments, and client integration for CitiSmart/PTE, an electronic execution platform for algorithmic equity trading. He developed test strategies using FIX Protocol, VeriFIX simulation tool, and SQL, ensuring 99% accuracy in trade and account reporting. He automated regression testing with Selenium and Cucumber, reducing testing time by 25%, and managed client change requests with 100% on-time delivery for production integrations.
Since joining Bank of Montreal in February 2026, Lev Yatsemyrskyi has architected and deployed next-generation agent-based and AI-powered platforms that deliver real-time risk analytics, regulatory compliance, and seamless institutional integration for one of North America's largest banks. His work directly supports the bank's mission to strengthen capital markets infrastructure while maintaining full regulatory alignment with FINRA, SEC, and international standards.
Prior to Bank of Montreal, Lev Yatsemyrskyi spent six years at Nasdaq, where he served as Director of Client Integrations and AI Functionality and Technical Product Manager for the Nasdaq Risk Platform from March 2021 through January 2026. In this capacity, he designed and executed end-to-end strategy, roadmap, and delivery for cloud-native risk management and real-time analytics systems operating in high-frequency trading environments. He served as the primary technical escalation point and integration architect for Nasdaq's largest institutional clients, including Bank of America Merrill Lynch, JPMorgan Chase, Citigroup, TradeWeb, Morgan Stanley, and Amazon. His work encompassed FIX Protocol optimizations, Kafka-based event streaming, drop-copy workflows, and AI-driven configurations supporting petabyte-scale market data feeds. Under his leadership, the Nasdaq Risk Platform achieved transformative results: 30% reductions in integration times, 200% onboarding efficiency gains, zero-delay cloud migrations for over 400 workflows, and 99.9% platform uptime while generating 80% of NRP revenue through high-frequency trading operations. He consistently delivered 95% client satisfaction across more than 100 institutional clients while ensuring full regulatory compliance with FINRA and SEC requirements.
From March 2018 to February 2020, Lev Yatsemyrskyi served as QA Manager and Technical Product Manager at Citi in New York, where he led client success, QA environments, and client integration for CitiSmart/PTE, an electronic execution platform for algorithmic equity trading. He developed test strategies using FIX Protocol, VeriFIX simulation tool, and SQL, ensuring 99% accuracy in trade and account reporting. He automated regression testing with Selenium and Cucumber, reducing testing time by 25%, and managed client change requests with 100% on-time delivery for production integrations.
Earlier in his career, from December 2013 to February 2018, he held the position of Lead Quality Assurance Analyst and Technical Project Manager at NEX Group Holdings (now part of CME Group) in New York, where he designed test cases for the BrokerTec fixed-income trading platform supporting Municipal, Corporate, and Treasury bond transactions. He automated testing with UFT/HP QTP, improving regression testing efficiency by 20%, and wrote SQL queries for backend validation to ensure accurate market data for a variety of client reports. His contributions to Agile SDLC improvements streamlined trade lifecycle processes for global clients.
Lev Yatsemyrskyi began his career in 2012 at PrivatBank, Ukraine's largest bank, where he served as Client Onboarding and Integration Specialist, managing high-volume corporate client connections and developing foundational expertise in regulatory compliance and data integration processes. From May 2014 to October 2015, he gained critical international experience as International Client Integration Analyst at Danske Bank in Copenhagen, Denmark, where he specialized in cross-border institutional onboarding, EU regulatory alignment, and early digital transformation projects that established the global perspective and technical foundation for his subsequent success in U.S. capital markets.
Since September 2024, Lev Yatsemyrskyi has founded and leads Xentaura, a fintech startup that delivers the world's first Nasdaq-style, S&P 500-style, and Dow Jones-style indexes tailored specifically to the cryptocurrency ecosystem. As Founder and Chief Architect, he built a fully operational proprietary platform establishing institutional-grade standards for digital-asset performance measurement, risk assessment, portfolio benchmarking, and market transparency. The platform already serves paying customers globally, demonstrating consistent month-over-month and quarter-over-quarter growth in active usage. Supported by an engineering team of four professionals, he is simultaneously developing a suite of next-generation financial instruments and a proprietary market-prediction AI-forecasting engine that employs large language models, agent-based architectures, and multimodal AI to identify future trends across cryptocurrency and virtually any asset class or economic domain. Early internal tests of this forecasting mechanism have shown exceptionally promising results, delivering high-confidence trend predictions that outperform traditional models in volatility detection and directional accuracy. Several of these AI-driven instruments and predictive frameworks are targeted for patent filing in 2027 and are designed to enhance liquidity, reduce systemic volatility risks, enable more accurate institutional decision-making, and accelerate mainstream adoption of digital assets.
Lev Yatsemyrskyi authored three peer-reviewed scholarly publications accepted in 2026: "Transformation of Institutional Client Onboarding Processes Through Agent-Based Intelligent AI Systems in High-Risk Financial Infrastructure," "Transformation of Corporate Client Onboarding Procedures Through AI-Enhanced Intelligent Recognition and Integration Systems," and "Automation of Client Interface Integration into Banking and Fintech Ecosystems Using Large Language Models," which was presented at the 3rd International Scientific and Practical Conference on Innovative Research in Science and Economy. He developed novel frameworks combining Retrieval-Augmented Generation, microservices, voice interfaces, and real-time high-frequency trading risk query systems, systematically evaluated for scalability, latency, security, and regulatory compliance in mission-critical environments. His work bridges academic theory with regulated practice, drawing on direct deployments at Nasdaq and Bank of Montreal to substantiate risk-managed AI adoption that reduces operational risk without increasing architectural complexity.
Lev Yatsemyrskyi began his career in 2012 at PrivatBank, Ukraine's largest bank, where he served as Client Onboarding and Integration Specialist, managing high-volume corporate client connections and developing foundational expertise in regulatory compliance and data integration processes. From May 2014 to October 2015, he gained critical international experience as International Client Integration Analyst at Danske Bank in Copenhagen, Denmark, where he specialized in cross-border institutional onboarding, EU regulatory alignment, and early digital transformation projects that established the global perspective and technical foundation for his subsequent success in U.S. capital markets.
Since September 2024, Lev Yatsemyrskyi has founded and leads Xentaura, a fintech startup that delivers the world's first Nasdaq-style, S&P 500-style, and Dow Jones-style indexes tailored specifically to the cryptocurrency ecosystem. As Founder and Chief Architect, he built a fully operational proprietary platform establishing institutional-grade standards for digital-asset performance measurement, risk assessment, portfolio benchmarking, and market transparency. The platform already serves paying customers globally, demonstrating consistent month-over-month and quarter-over-quarter growth in active usage. Supported by an engineering team of four professionals, he is simultaneously developing a suite of next-generation financial instruments and a proprietary market-prediction AI-forecasting engine that employs large language models, agent-based architectures, and multimodal AI to identify future trends across cryptocurrency and virtually any asset class or economic domain. Early internal tests of this forecasting mechanism have shown exceptionally promising results, delivering high-confidence trend predictions that outperform traditional models in volatility detection and directional accuracy. Several of these AI-driven instruments and predictive frameworks are targeted for patent filing in 2027 and are designed to enhance liquidity, reduce systemic volatility risks, enable more accurate institutional decision-making, and accelerate mainstream adoption of digital assets.
Lev Yatsemyrskyi authored three peer-reviewed scholarly publications accepted in 2026: "Transformation of Institutional Client Onboarding Processes Through Agent-Based Intelligent AI Systems in High-Risk Financial Infrastructure," "Transformation of Corporate Client Onboarding Procedures Through AI-Enhanced Intelligent Recognition and Integration Systems," and "Automation of Client Interface Integration into Banking and Fintech Ecosystems Using Large Language Models," which was presented at the 3rd International Scientific and Practical Conference on Innovative Research in Science and Economy. He developed novel frameworks combining Retrieval-Augmented Generation, microservices, voice interfaces, and real-time high-frequency trading risk query systems, systematically evaluated for scalability, latency, security, and regulatory compliance in mission-critical environments. His work bridges academic theory with regulated practice, drawing on direct deployments at Nasdaq and Bank of Montreal to substantiate risk-managed AI adoption that reduces operational risk without increasing architectural complexity.