Head – Retail Risk Management

Finance House; Abu Dhabi; On-site; AED 900,000 - 1,200,000; Full time

Job summary

Finance House is seeking a Head of Retail Risk Management in the UAE to own end-to-end retail credit risk strategy from origination to portfolio management. The role requires leveraging analytics, AI/ML, and big data to optimize portfolio performance, profitability, and risk posture while guiding executive decision making.

The incumbent will lead a cross-functional team, shape risk appetite, and drive scalable, data‑driven risk frameworks across the credit lifecycle, with significant influence

Qualifications

• Deep expertise in predictive modelling and advanced retail credit risk management techniques.
• Big data analytics using internal and external data sources, including credit bureaus.
• AI and machine learning applications in credit risk decisioning.
• Knowledge of credit cards and retail lending products, including acquisition and portfolio optimization.
• Credit policies, portfolio management, and delivery systems.
• Operational and reputational risk assessment and mitigation.
• Decision support systems and automated decision platforms.

Responsibilities

• Design, build, and lead a high performing Retail Risk department and foster collaboration with cross-functional teams.
• Drive portfolio analytics and performance measurement to identify opportunities for risk and profitability.
• Oversee AI-based decision engines across origination, underwriting, and pricing.
• Develop and governance of advanced credit risk models, including ML/AI models.
• Establish data infrastructure supporting analytics and real-time decisioning.
• Provide risk insights to support acquisition, cross-sell, and campaign optimization.
• Ensure regulatory compliance and robust risk reporting to the Board.

Skills

Predictive modelling

Big data analytics

AI in credit risk

Credit cards

Retail lending

Decision support systems

Education

Bachelor’s degree

Tools

OMDM

TRIAD

SMG

Probe

Job description

Summary

The Head of Retail Risk Management holds end-to-end ownership of all retail credit risks, spanning origination and portfolio management. The role is responsible for defining and executing a forward looking, data driven credit risk strategy that enables Finance House to achieve its retail business objectives while positioning the organization as a leading institution in the market. The incumbent will drive innovative, prudent, and scalable risk strategies across the credit lifecycle, leveraging advanced analytics, decision science, big data, machine learning, and AI to enhance portfolio performance, profitability, and long term sustainability.

This is a highly complex, strategic, and technical leadership role with significant influence over retail portfolio growth, asset quality, profitability, and overall risk posture. Decisions made by the incumbent have material financial, operational, and reputational impact on the organization and require a balance of strategic vision, advanced technical expertise, and strong executive influence.

Key Responsibilities

Leadership & Strategy

• Design, build, and lead a high performing Retail Risk department, ensuring optimal talent utilization, capability development, and operational excellence.


• Act as a collaborative executive leader, partnering closely with Product Management, Retail Lending, Sales, Marketing, Operations, Technology, and Service teams.


• Define, own, and continuously evolve the retail credit risk appetite and strategy, aligned with Finance House’s fintech vision and business goals.


• Provide strong risk leadership to support sustainable and profitable growth while optimizing the risk return trade off across retail portfolios.

Risk Analytics, Modelling & Decision Technology

• Drive deep portfolio analytics and performance measurement to identify opportunities for risk, profitability, and process optimization.


• Lead the design, development, deployment, and continuous enhancement of AI based decision engines, leveraging either leading vendor platforms or in-house built decisioning frameworks, across origination and underwriting, limit management and pricing, and early warning systems.


• Oversee the development, validation, governance, and lifecycle management of advanced credit risk models, including machine learning and AI based models.


• Establish robust challenger and test-and-learn frameworks to drive continuous improvement in decisioning outcomes.


• Contribute to the design and enhancement of scalable data infrastructure supporting analytics, modelling, and real time decisioning.


• Develop customer risk segmentation and differentiated credit strategies to optimize risk reward and net financial contribution.


• Forecast key business and risk metrics; oversee stress testing, sensitivity analysis, and scenario planning.


• Establish standardized portfolio, segment level, and executive risk reporting frameworks.

Portfolio Management

• Provide end-to-end leadership of portfolio management activities, including credit policy, analytics and MIS, systems, technology, and capacity planning.


• Define, own, and continuously optimize portfolio strategies across the customer lifecycle to minimize credit losses and enhance portfolio performance.


• Establish and deploy proactive early-warning indicators and monitoring frameworks across products, segments, and acquisition channels to enable timely risk intervention.

Governance, Regulatory & Compliance

• Ensure full compliance with all applicable regulatory and accounting standards, including Central Bank regulations, credit bureau requirements, IFRS 9, Basel frameworks, and local capital adequacy requirements.


• Maintain strong relationships with regulators and external stakeholders, safeguarding FH from regulatory and reputational risk.


• Ensure compliance with BCBS 239 principles, including accuracy and integrity, completeness, timeliness, and adaptability.


• Establish strong risk data governance, model risk management, and control frameworks to support effective oversight and auditability.

Business Partnership & Advisory

• Partner with Retail business teams to deliver actionable risk insights supporting acquisition, cross-sell, offer sizing, and campaign optimization.


• Enable growth in digital and non-branch channels through effective utilization of automated credit risk and fraud controls.


• Provide thought leadership on consumer and SME automated decision systems across origination, portfolio management, collections, and provisioning.


• Act as a trusted advisor to Senior Management and the Board on credit bureaus, risk systems, analytics, IFRS 9, Basel, and collections initiatives.

Knowledge & Skill Requirements

• The incumbent must demonstrate deep expertise and hands-on experience in:


• Predictive modelling and advanced retail credit risk management techniques.


• Big data analytics using internal and external data sources, including credit bureaus.


• AI and machine learning applications in credit risk decisioning.


• Credit cards and retail lending products, including acquisition and portfolio optimization.


• Credit policies, portfolio management, and delivery systems.


• Operational and reputational risk assessment and mitigation.


• Decision support systems and automated decision platforms.


• Financial risk-return measures including profitability, NPV, CLV, break-even analysis, and customer value management.

Decision Engine Expertise

• Extensive experience in AI driven decision engines, including architecture design, rule based and model driven decisioning, real time decisioning, challenger frameworks, and lifecycle optimization.


• Proven capability in building in house decision engines, including strategy orchestration, policy management, model integration, explainability (XAI), governance, and regulatory compliance.


• Deployment and management of decision engines integrating machine learning models, traditional scorecards, rules engines, and adaptive learning frameworks.


• Strong understanding of regulatory expectations around model risk management, auditability, explainability, fairness, and bias mitigation.

Qualifications

• Bachelor’s degree (or higher), preferably in Mathematics, Statistics, Computer Science, Engineering, or a related quantitative discipline.

Experience

• 15+ years of experience in retail credit risk, preferably including exposure to developed markets.


• Proven experience building, validating, and implementing strategies using decision support systems such as OMDM, TRIAD, SMG, Probe, or similar platforms.


• Extensive experience in implementing and/or building AI based decision engines with full ownership of design, development, validation, governance, and production deployment.


• Excellent verbal and written communication skills, with strong experience in executive presentations, management reporting, and board level engagement.