Chief Credit Officer

We are seeking a highly commercial, analytically strong, and operationally rigorous Chief Credit Officer (CCO) to lead the company's end-to-end credit function. This is a critical senior role responsible for shaping credit strategy, underwriting excellence, portfolio quality, and the evolution of the company’s automated credit decisioning capabilities.

The CCO will lead both (i) traditional / expert-led credit underwriting, including policy, approvals, exception handling, and portfolio oversight, and (ii) the ongoing development, governance, calibration, monitoring, and enhancement of an automated credit decisioning tool incorporating data, analytics, and AI / machine learning components.

Role Purpose

This role sits at the intersection of credit risk, product, data science, analytics, operations, technology, and business growth. The successful candidate will ensure the company’s scales responsibly while maintaining robust credit discipline, strong customer outcomes, and a fit-for-purpose governance framework for both manual and automated decisioning.

Key Responsibilities

1. Credit Strategy & Leadership

• Own the company’s overall credit philosophy, risk appetite implementation, underwriting framework, and portfolio risk strategy.
• Develop and continuously refine credit policies, scorecards, approval criteria, early warning frameworks, and exposure limits across customer segments, products, geographies, and channels.
• Balance growth, loss performance, customer experience, and capital efficiency in line with the company’s business objectives.
• Serve as the senior sponsor for credit risk matters with senior management, board committees, funders, and key external stakeholders as required.
• Build and lead a high-performance credit organisation spanning underwriting, credit policy, portfolio management, decision science, and credit governance.

2. Manual / Expert Underwriting

• Lead the traditional underwriting function for more complex, higher-value, escalated, or exception-based transactions.
• Set clear underwriting standards for assessing borrowers using financial statements, transaction data, cashflow trends, inventory, receivables, supply chain information, and sector dynamics.
• Establish approval authorities, delegation matrices, escalation thresholds, and exception governance.
• Ensure consistency, quality, and turnaround time in underwriting decisions while maintaining robust risk discipline.
• Act as final decision-maker or chair of credit committee for material exposures, structured cases, and policy exceptions.

3. Automated Credit Decisioning & AI-Enabled Credit

• Own the vision, roadmap, and performance of the company’s automated credit decisioning platform.
• Partner with Product, Data, Engineering, and Analytics teams to design and improve credit decision engines, rules frameworks, scorecards, and machine learning models.
• Ensure automated decisions are commercially effective, statistically robust, operationally reliable, and aligned to policy intent.
• Define and oversee model segmentation, champion-challenger testing, cut-off strategies, decision flows, and periodic recalibration.
• Drive the integration of new internal and external data sources to improve predictive power, decision speed, and underwriting depth.
• Ensure that AI and model-based credit decisions are explainable, appropriately governed, monitored for model drift, and subject to rigorous human oversight where required.
• Create strong feedback loops between manual underwriters and automated systems so policy learning and expert judgment continuously improve model performance.

4. Portfolio Risk Management

• Monitor and actively manage portfolio quality across origination cohorts, products, sectors, geographies, and partner channels.
• Establish and oversee KPIs / KRIs such as approval rate, bad rate, delinquency, default, roll rates, vintage performance, recoveries, concentration risk, and profitability by segment.
• Lead portfolio reviews and recommend timely adjustments to strategy, pricing, terms, and approval rules based on performance trends.
• Design early warning and account management strategies for deteriorating borrowers or vulnerable sectors.
• Work closely with collections / recoveries teams to ensure insights from arrears and losses inform origination strategy.

5. Credit Governance, Controls & Risk Frameworks

• Maintain strong first-line credit governance across policy, procedures, controls, documentation, and audit readiness.
• Ensure decisioning and underwriting frameworks comply with internal risk standards, legal and regulatory requirements, and lender / funding-partner expectations.
• Implement robust model governance practices including model inventory, validation, change control, approval logs, periodic review, and outcome monitoring.
• Chair or contribute to relevant credit, risk, and product governance forums.
• Ensure clear documentation of credit decisions, policy rationale, model assumptions, overrides, and exceptions.

6. Cross-Functional Partnership

• Work closely with the Executive Committee on growth and risk trade-offs.
• Partner with Product & Technology on decision engine architecture and workflow design.
• Collaborate with Data / Analytics on model development, feature engineering, monitoring, and experimentation.
• Coordinate with Operations on straight-through processing, service levels, and underwriting efficiency.
• Work with Legal / Compliance on regulatory and governance requirements.
• Support Finance on portfolio performance, lender reporting, and funding considerations.
• Engage Commercial / Partnerships teams on channel strategy and partner-level risk performance.

7. Team Building & Capability Development

• Recruit, lead, and develop a best-in-class credit team combining deep underwriting judgment with data-led decisioning capability.
• Foster a culture of analytical rigor, accountability, constructive challenge, and continuous improvement.
• Upskill teams in the use of data, automation, and AI-enabled decision support tools.
• Define organisational design and hiring strategy across underwriting, policy, portfolio analytics, and model governance.

Candidate Profile

Experience

• 10+ years of relevant experience across credit risk, underwriting, portfolio management, structured lending, trade finance, working capital finance, SME lending, or fintech lending.
• Significant leadership experience in a bank, non-bank lender, fintech, trade finance platform, digital lender, or specialty finance business.
• Proven experience leading both manual / judgment-based underwriting teams and data-driven or automated credit decisioning environments.
• Strong track record in building or overseeing credit scorecards, rules engines, decision platforms, or ML-enabled lending models in production.
• Experience in SME, cross-border trade, e-commerce, supply chain, receivables, inventory, or cashflow-based lending is highly desirable.
• Experience managing portfolio performance through different market cycles and scaling credit frameworks in high-growth environments.
• Familiarity with Hong Kong / Greater China / regional Asian lending environments is strongly preferred.

Technical / Functional Expertise

• Deep understanding of credit risk principles, underwriting, portfolio analytics, and loss performance management.
• Strong command of data-driven lending concepts including scorecards, feature design, segmentation, validation, cut-off strategy, override analysis, and monitoring.
• Practical understanding of AI / machine learning use cases in credit, including model risk, explainability, bias / fairness considerations, and drift monitoring.
• Strong quantitative capability; able to challenge model outputs and translate analytics into credit policy decisions.
• Comfort working with alternative data, transaction data, platform data, and ecosystem-derived risk signals.
• Strong knowledge of governance and control requirements for credit policy and model lifecycle management.

Leadership & Personal Attributes

• Executive presence with the ability to influence senior stakeholders, investors, funders, and boards.
• Commercially minded, with the judgment to balance prudent risk management and growth ambition.
• Hands-on, intellectually curious, and comfortable operating in a fast-paced fintech environment.
• Able to move between strategic design and practical execution.
• Strong communicator who can bridge credit, data, technology, and business teams.
• High integrity, sound judgment, and a bias for disciplined decision-making.

Success Measures / KPIs

• Portfolio quality and loss performance
• Approval quality and consistency
• Underwriting turnaround times and operational efficiency
• Performance of automated decisioning tools and model monitoring metrics
• Credit policy effectiveness and governance quality
• Portfolio profitability and risk-adjusted returns
• Reduction in avoidable manual touchpoints through effective automation
• Quality of lender / investor / board credit reporting
• Team capability, retention, and leadership effectiveness

Example First-Year Priorities

• Review and refine the company’s end-to-end credit strategy and approval architecture.
• Assess the current underwriting framework and identify opportunities to improve consistency, speed, and quality.
• Conduct a full review of the automated credit decisioning tool, including data inputs, rules, model performance, override rates, explainability, and governance.
• Implement a robust model monitoring and validation framework for all production credit models and AI components.
• Define a target operating model for hybrid decisioning across manual underwriting and automated approvals.
• Improve portfolio surveillance, early warning indicators, and vintage-level performance reporting.
• Strengthen credit committee governance, policy documentation, and exception management.
• Build stronger integration between credit, product, engineering, and data teams.