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Mortgage servicing compliance risk rarely resides in a single policy or regulatory requirement. It often emerges where processes, data, technology, employees, and third parties intersect, particularly in loss mitigation, borrower communications, error resolution, servicing transfers, and other activ...

An AI governance policy should do more than establish broad principles for responsible technology use. It should define who is accountable, which AI activities are subject to oversight, how risks are assessed and classified, what controls apply throughout the AI lifecycle, and how significant issues...

Third-party relationships have become integral to how financial institutions deliver products, operate technology, serve customers, and manage critical business processes. As the number and complexity of these relationships grow, maintaining a list of vendors is no longer sufficient. Institutions ne...

A consumer lending compliance risk assessment should help management understand where the institution is most exposed to consumer compliance risk, how effectively existing controls address those risks, and where additional attention may be warranted. The assessment should reflect the institution's p...

Effective BSA/AML governance requires more than approving policies and receiving periodic reports. Boards and senior management need enough information, authority, and organizational visibility to understand the institution's financial crime risk profile, assess whether the program is operating as i...

Engaging with third-party AI vendors introduces opportunities for operational efficiency, improved decision-making, and enhanced customer experiences. However, this engagement also brings complex risks that extend beyond the scope of traditional Third-Party Risk Management (TPRM). Standard TPRM fram...

Table of Contents Preparing for an OCC Examination: A Framework for Risk and Compliance Leaders Foundations of Examination Readiness: Governance, Documentation, and Risk Assessment Model Risk Governance Under the 2026 Interagency Guidance Aligning Operations with Regulatory Expectations ## Prep...

Auto finance lenders operate through an extended network of dealers, technology providers, data vendors, and other third parties that can materially influence the consumer lending experience. Dealer pricing practices, add-on products, financing processes, vendor models, and data used in credit decis...

Artificial intelligence is expanding the use of models and automated decisioning across financial services, but managing the associated risk requires more than validating a model before it enters production. Model risk can arise from flawed assumptions, data limitations, inappropriate use, performan...

Digital asset activities can introduce new technologies and business models, but they do not eliminate the fundamental risk management disciplines financial institutions already use to govern complex products and activities. As institutions explore cryptocurrency, tokenization, stablecoins, custody,...

Financial institutions have moved quickly from experimenting with artificial intelligence to identifying opportunities for broader deployment. The challenge is no longer simply proving that an AI use case can work. It is determining how to move promising initiatives from isolated pilots into product...

Financial institutions often treat regulatory examinations as cyclical emergencies, triggering resource-intensive “fire drills” to assemble documentation and prepare for supervisory review. This reactive posture, however, frequently fails to address the underlying issues that attract regulatory scru...

A financial crime risk assessment should do more than document an institution's exposure to money laundering and other illicit finance risks. When designed effectively, it can help management understand how the institution's customers, products, services, geographies, delivery channels, and other ac...

Financial institutions are moving from AI experimentation toward broader deployment across business, risk, compliance, and operational functions. As AI use expands, the governance challenge is not simply to create a new set of controls. It is to establish a practical framework for identifying AI use...

Auto finance compliance continues to evolve as lenders adapt to changes in products, technology, consumer expectations, and the legal and regulatory environment. For senior leadership, the challenge is to maintain governance and risk management practices that address consumer protection, fair lendin...

Effective regulatory exam readiness is not achieved through a last-minute, document-collection exercise. Instead, it is the natural outcome of a well-designed and consistently maintained compliance and risk management framework. An institution that embeds strong governance into its daily operations...

Table of Contents Introduction: What Does “Program Effectiveness” Mean? The Evolving Focus on AML/CFT Program Effectiveness What Should Be Tested? Independent Testing, Validation, and Internal Audit ## Introduction: What Does “Program Effectiveness” Mean? BSA/AML program effectiveness is not m...

Table of Contents The Modern Regulatory Landscape for Consumer Lending Compliance Operationalizing Fair Lending and UDAAP Oversight Governance Frameworks for AI and Automated Credit Models Regulatory Examination Readiness: A Proactive Strategy ## The Modern Regulatory Landscape for Consumer Len...

Table of Contents The Evolution of Operational Risk: Beyond Process Failures to Strategic Resilience Architecting an Integrated Operational Risk Framework Operationalizing Risk: From Self-Assessment to Strategic Transparency Transitioning ORM from a Cost Center to an Innovation Enabler ## The E...

Table of Contents What the Board Should Understand About AI Risk Governance and Accountability SR 26-2 and the Board's Oversight Role The Questions Boards Should Ask Management ## What the Board Should Understand About AI Risk Artificial intelligence is moving rapidly from experimentation into...