Product Manager - Credit Risk (Finchoice) at Weaver Fintech Ltd

Role Overview

• FinChoice and PayJustNow are building scalable, customer-centric financial products at pace. As we grow, our Credit Risk and Fraud platforms are critical to protecting customers and the business while enabling responsible lending at scale.
• We’re looking for a Product Manager to own and drive the roadmap for Credit Risk & Fraud. This role focuses on how we assess creditworthiness, detect and prevent fraud, automate decisioning, and continuously improve model and policy performance — all while balancing risk appetite with customer experience.
• You’ll lead product strategy and delivery across credit decisioning, fraud detection and prevention, identity verification, and risk operations tooling — with a strong bias toward measurable impact: reduced losses, faster decisioning, improved approval rates, and lower fraud rates.
• This role works closely with Engineering, Data Science, Risk, Compliance, Operations, and Commercial teams.

What You’ll Be Responsible For

Product Strategy & Roadmap

• Own the product vision and roadmap for Credit Risk & Fraud at FinChoice.
• Define clear product outcomes aligned to business goals such as portfolio quality, fraud loss reduction, decisioning speed, and regulatory compliance.
• Identify and prioritise opportunities across credit scoring, policy automation, fraud signals, and identity verification.
• Balance tactical risk controls with longer-term platform and model capability investments.

Discovery & Problem Definition

• Build a deep understanding of the credit lifecycle, fraud typologies, and the operational constraints that shape risk decisions.
• Collaborate with Data Science and Risk teams to understand model performance, policy gaps, and emerging fraud patterns.
• Use data and insights to identify root causes of fraud losses, bad debt, manual review bottlenecks, and false positive rates.
• Frame problems clearly and validate solutions before committing to build.

Delivery & Execution

• Lead cross-functional delivery squads from problem definition through to launch and optimisation.
• Produce high-quality product briefs, PRDs, and outcome-focused backlogs.
• Partner closely with Engineering and Data Science to design scalable, secure, and explainable solutions.
• Manage dependencies across internal platforms, data vendors, bureau integrations, and compliance teams.

Stakeholder Collaboration

• Act as the primary product partner for Risk, Fraud, Collections, and Compliance leadership.
• Communicate trade-offs, progress, and impact clearly to senior stakeholders and the Risk Committee where relevant.
• Align multiple teams around shared outcomes in a complex, regulated operating environment.
• Build trust by delivering improvements that materially reduce risk exposure and improve decisioning quality.

Measurement & Continuous Improvement

Define and track success metrics such as:

• Credit loss rate and arrears performance
• Fraud detection rate and false positive rate
• Straight-through processing (STP) and auto-decisioning rates
• Application approval and decline rates by segment
• Manual review volumes and queue throughput
• Time-to-decision and customer drop-off rates
• Use data to evaluate outcomes post-launch and iterate continuously.
• Champion continuous improvement and pragmatic experimentation across risk policies and fraud rules.

First 90 Days — What Success Looks Like

First 30 Days: Understand & Connect

• Build strong relationships with Risk, Fraud, Data Science, Engineering, Compliance, and Operations stakeholders.
• Get hands-on with existing decisioning systems, fraud tooling, and credit policy frameworks.
• Review current model performance, fraud trends, and known gaps in detection or prevention capability.
• Review the current roadmap, in-flight initiatives, and vendor landscape.
• Develop a clear view of key risk metrics and baseline performance.

Days 31–60: Shape & Prioritise

• Clearly articulate the problem space and opportunity areas across credit risk and fraud.
• Validate and refine the roadmap with stakeholders based on impact, risk appetite, and feasibility.
• Define measurable outcomes and success metrics for priority initiatives.
• Identify quick wins — such as rule improvements or signal additions — and longer-term platform opportunities.
• Align delivery teams around a shared plan and priorities.

Days 61–90: Deliver & Lead

• Drive delivery of at least one meaningful risk or fraud improvement into production.
• Demonstrate early impact through data, reduced losses, or improved decisioning performance.
• Establish a strong operating rhythm with Data Science, Engineering, and stakeholders.
• Clearly communicate progress, learnings, and next priorities.
• Be recognised as the go-to product owner for Credit Risk & Fraud.

What We’re Looking For

Experience

• 5+ years’ experience as a Product Manager in complex, data-intensive environments.
• Proven track record owning and delivering outcomes in risk, fraud, or financial services product areas.
• Experience with credit decisioning systems, fraud detection platforms, identity verification tools, or bureau integrations.
• Comfortable working in regulated environments — fintech or consumer lending experience is a strong advantage.
• Familiarity with machine learning model deployment, policy rule engines, or decisioning infrastructure is highly desirable.

Skills & Strengths

• Strong analytical thinker who can turn complex risk data into clear product direction.
• Excellent prioritisation skills with a focus on risk-adjusted business and customer outcomes.
• Data-driven decision-maker who is comfortable with model performance metrics, scorecards, and statistical concepts.
• Confident communicator who can engage technical teams, risk specialists, and senior leaders alike.
• Able to influence without authority and navigate competing priorities across risk appetite, customer experience, and commercial growth.

Mindset

• Risk-aware and customer-conscious — you understand that good risk management enables better customer outcomes.
• Pragmatic and delivery-focused — you know when progress beats perfection.
• Curious about AI, machine learning, and how emerging fraud patterns and data signals can improve detection and decisioning.
• Comfortable operating in fast-moving, evolving environments where the threat landscape changes quickly.

Closing Date 10 April 2026