VP, Enterprise Fraud Modeling

Bank OZK•Dallas, TX

About The Position

Responsible for building, documenting, and maintaining the fraud detection and prevention models used to identify, quantify, monitor, and mitigate the company's fraud risk exposure across enterprise operations. This role works within the Enterprise Model Development Team to advance the Bank's fraud detection capabilities using statistical modeling, machine learning techniques, rules-based systems, and hybrid approaches appropriate to the fraud risk landscape.

Requirements

Comprehensive knowledge of fraud risk management principles and fraud typologies, including deposit fraud, digital banking fraud, payment fraud, identity fraud, and account takeover.
Comprehensive knowledge of Model Risk Management concepts, validation expectations, and regulatory guidance applicable to banking models.
Comprehensive knowledge of analytical and modeling tools such as SAS, R, Python, SQL, and related data analysis technologies.
Knowledge of model performance evaluation, monitoring metrics, and explainability techniques.
Knowledge of banking operations, digital banking channels, payment systems, and fraud operations workflows.
Ability to communicate complex analytical concepts clearly to non‑technical audiences, including executives, regulators, and business partners.
Ability to demonstrate leadership, mentoring, and team development skills.
Ability to demonstrate strong problem‑solving, critical thinking, and analytical skills.
Ability to produce high‑quality documentation, presentations, and governance materials.
Ability to demonstrate effective interpersonal skills, including working in a team environment and building cross-functional relationships.
Ability to travel for business purposes as needed.
Advanced quantitative and statistical skills, including classification modeling, anomaly detection, clustering, network analysis, machine learning techniques, and explainability methods used in fraud detection and prevention.
Skill in using computers and Microsoft Office, including Word, Excel, Access, PowerPoint and Outlook.
Master’s degree in mathematics, statistics, data science, computer science, finance, economics, or a related quantitative field required; PhD or other advanced degree preferred.
Two or more years of progressively responsible experience in fraud analytics, fraud modeling, financial crime analytics, or quantitative risk management within banking or financial services, required.
Experience with statistical modeling tools and data extraction technologies required.

Nice To Haves

PhD or other advanced degree preferred.

Responsibilities

Provides strategic leadership for fraud analytics and modeling that influences executive management decisions related to fraud risk management, regulatory compliance, and operational resilience.
Develops quantitative and analytical monitoring metrics to measure and manage fraudulent activity across Bank OZK’s key channels, including digital account opening, mobile and merchant remote deposit capture (RDC), ACH and wire origination, Zelle, debit card transactions, ATM activity, and branch-based deposits.
Develops, enhances, and maintains fraud detection and prevention models across multiple fraud domains, including deposit fraud, digital channel fraud, payment fraud, identity fraud, account takeover, authorized push payment scams, first‑party fraud, and application fraud.
Guides and critically evaluates third‑party vendors and consultants providing fraud models or decisioning tools. Ensures vendor models are appropriately documented, validated, monitored, and governed prior to and after production use.
Ensures all fraud models comply with SR 26‑2 guidance and the Bank’s Model Development and Risk Management Program Standards, including conceptual soundness, data integrity, methodology documentation, model tiering, risk rating, validation readiness, change management, and governance controls.
Designs and executes comprehensive monitoring programs for fraud models, including performance metrics, implementation verification, threshold management, alert optimization, drift detection, breach management, escalation protocols, and periodic performance reviews.
Ensures fraud models meet regulatory expectations for explainability, transparency, and auditability. Develops interpretation frameworks, feature analysis, and decision logic documentation suitable for management, audit, and regulatory review.
Provides expert guidance to executive management, Fraud Risk Management leadership, risk committees, and the Board on fraud risk trends, model performance, loss drivers, and mitigation strategies.
Leads planning and execution of fraud model implementation in partnership with Technology, Data Engineering, and Fraud Operations. Ensures production accuracy, operational readiness, user understanding, and controlled deployment.
Serves as subject‑matter expert in discussions with regulators, independent auditors, and internal audits regarding fraud model design, governance, performance, and remediation activities. Prepares materials and responses for examinations and reviews.