Job Purpose • Manage portfolio risk strategy and performance management for unsecured retail assets, including credit cards, personal loans, BNPL and other digital lending products. • Own the end-to-end risk decision framework across acquisition, account management, line assignment, pricing, collections and customer lifecycle management. • Use advanced analytics, segmentation and AI/ML models to optimize growth, credit quality, customer outcomes and risk-adjusted profitability. • Provide the senior leadership team with forward-looking portfolio insights, clear risk appetite recommendations and timely actions to maintain losses, returns and capital consumption within plan. Key Result Areas • Set and execute portfolio strategies for credit cards, personal loans, BNPL and emerging digital credit propositions, aligned with approved risk appetite, growth plans and regulatory requirements. • Own portfolio performance across approval rates, activation, utilization, balances, yield, delinquencies, roll rates, loss rates, risk charge, vintage performance, expected credit loss, capital and risk-adjusted returns. • Develop granular customer, product, channel, employer, income and behavioral segmentation to identify growth pockets, emerging risks and differentiated treatment strategies. • Design and govern risk decision frameworks covering eligibility, score cut-offs, affordability, limits, tenor, pricing, line management, cross-sell, collections and exit strategies. • Lead champion–challenger tests and controlled experiments; define success metrics, guardrails and post-implementation monitoring to scale profitable strategies safely. • Partner with data science teams to develop, validate and deploy application, behavioral, propensity, fraud and early-warning models using traditional and alternative data. • Establish responsible AI and model governance, including explainability, fairness, drift, stability, override, monitoring and human-oversight standards. • Build executive dashboards, forecasts and scenario analyses linking risk drivers to revenue, margin, credit cost, operating expense, capital and lifetime customer profitability. • Define early-warning indicators, portfolio triggers and remediation playbooks for adverse trends, concentration risks and macroeconomic or market shocks. • Provide independent retail credit risk challenge for new products, partnerships, fintech propositions and material changes to customer journeys or decision engines. • Ensure compliance with Central Bank requirements, internal policies, model risk standards, consumer protection expectations and audit requirements. • Lead, coach and develop a high-performing portfolio analytics team and strengthen data-driven decision making across Retail Banking. Knowledge, Skills and Experience • Education: Bachelor’s degree in Statistics, Mathematics, Economics, Finance, Engineering, Computer Science, Data Science or a related quantitative discipline; postgraduate qualification is preferred. • Experience: Typically 12–15 years in retail credit risk, portfolio management or risk analytics, including substantial leadership responsibility and direct exposure to credit cards, personal loans and digital lending/BNPL. • Portfolio expertise: Proven ownership of acquisition and account-management strategies, vintages, roll rates, delinquency, loss forecasting, provisions, risk-adjusted profitability, limit management, collections strategies and portfolio optimization. • Analytics and modeling: Strong command of statistical analysis, experimentation, segmentation, forecasting, scorecards and AI/ML applications across underwriting, behavioral risk, propensity, fraud and early warning. • Technical capability: Hands-on proficiency in SQL and at least one analytical language such as Python, R or SAS; strong working knowledge of Power BI/Tableau, large datasets, data pipelines and cloud analytics environments. • Decision frameworks: Demonstrated ability to translate analytics into executable cut-offs, pricing, limits, treatments and growth strategies with clear economics, controls and performance measurement. • Governance: Sound understanding of model risk, responsible AI, explainability, fairness, data privacy, consumer protection, IFRS 9/provisioning concepts and Central Bank requirements relevant to retail lending. • Leadership: Strong executive communication, stakeholder influence and people leadership, with the ability to challenge constructively and present complex risk-return choices to senior committees.