Enterprise Architect Financial Crimes

This role requires on-site presence at our Seneca One Tech Hub in Buffalo, New York, for one week per month.

Founded in 1856, M&T Bank is a regional financial institution with a long-standing commitment to financial strength, community banking, and responsible innovation. The bank invests in technology, data, and processes that improve customer outcomes, strengthen resilience, and support sustainable growth.

Provides enterprise architecture leadership across financial crime and fraud prevention capabilities. Defines technology strategies, target-state architectures, integration patterns, data foundations, and roadmaps spanning Anti-Money Laundering (AML), Know Your Customer (KYC), Customer Due Diligence (CDD), sanctions, transaction monitoring, adverse media, fraud prevention, digital identity, behavioral analytics, case management, and investigator workflows across Consumer, Commercial, Institutional, Payments, and enterprise operations.

• Define and evolve financial crime and fraud prevention architecture strategies and roadmaps aligned with business priorities, risk appetite, regulatory obligations, enterprise standards, and the evolving threat landscape.
• Maintain current-state and target-state architectures across customer due diligence, KYC, sanctions and watchlist screening, adverse media, transaction monitoring, fraud detection, identity verification, authentication, behavioral analytics, alerting, case management, investigations, remediation, and regulatory reporting.
• Define and connect the distinct capability needs of AML and financial crime compliance, fraud prevention, identity and authentication, and insider threat management, where applicable.
• Define enterprise patterns for customer and counter party identity, entity resolution, graph analytics, event streaming, feature and decision services, model execution, alert generation, case integration, evidence, and data lineage.
• Guide the integration of platforms such as NICE Actimize, Lexis Nexis Bridger, Bio Catch, Prove, Socure, Bottomline Insider Threat, and comparable services.
• Product-specific experience is beneficial, but platform choices remain subject to enterprise standards and strategy.
• Embed cybersecurity, privacy, model risk, resilience, explainability, auditability, data governance, and evidence requirements into architecture decisions.
• Define production controls for analytics and AI/ML models, including approval, validation, monitoring, performance thresholds, drift, explainability, false-positive management, and investigator outcome feedback.
• Partner with Financial Crime, Fraud, Operations, Payments, Digital Identity, Cybersecurity, Data, Legal, Compliance, Model Risk, business, and technology stakeholders to resolve cross-domain dependencies and sequencing decisions.
• Evaluate vendor and platform options for functional coverage, enterprise fit, data needs, integration, cost, resilience, third-party risk, and the avoidance of unnecessary duplication.
• Provide architecture governance and lifecycle oversight from strategy through implementation and production operation, while maintaining the distinction between architecture accountability and day-to-day delivery ownership.

• AML, KYC, customer due diligence, beneficial ownership, sanctions and watchlist screening, adverse media, transaction monitoring, case management, suspicious activity investigation, and regulatory reporting.
• Fraud typologies and controls, including account takeover, payment fraud, check fraud, social engineering, mule networks, synthetic identity, first-party fraud, and insider threats.
• Digital identity, identity proofing, authentication, behavioral analytics, device and network intelligence, customer communications, and step-up or interdiction workflows.
• Financial crime and fraud data architecture, including entity resolution, customer and counter party identity, event streaming, graph analytics, lineage, provenance, quality, retention, and authoritative sources.
• Analytics and AI/ML architecture, including model lifecycle management, validation, explainability, performance monitoring, drift, false-positive reduction, human review, and feedback loops.
• Modern architecture patterns, including APIs and API management, microservices, event-driven architecture, Kafka, Azure cloud services, Snowflake, Databricks, workflow, and enterprise integration.
• Applicable regulatory frameworks and guidance, including BSA/AML…