We are currently seeking a high calibre professional to join our team as a Payment Fraud Product Owner, Non-financial Risk Management.
In this role you will:
Own the end-to-end product vision, strategy, roadmap and backlog for corporate payment fraud detection and prevention, aligning Risk, Technology, Data/Analytics, Operations and business stakeholders.
Define and prioritise data ingestion and fraud feature requirements across channels/payment types; ensure secure, resilient pipelines with strong data quality, integrity and lineage.
Drive delivery of data transformation, enrichment and feature enablement to support model training, monitoring and ongoing optimisation.
Set the strategy for the fraud decision engine (onboarding and early-life), partnering with Data Science to develop and iterate models/rules that improve detection and reduce friction.
Own model performance management by defining KPIs/thresholds (e.g., detection, false positives, alert volumes, loss prevented, customer impact) and running continuous tuning/champion–challenger where applicable.
Ensure explainability and governance are fit-for-purpose (reason codes, auditability, documentation) for investigators, risk committees and regulatory expectations.
Design and implement interventions and playbooks (approve/decline/step-up/hold-review/routing), integrating with downstream systems and agreeing SLAs with Fraud Operations/Investigations.
Establish closed-loop feedback from investigations, disputes and chargebacks; coordinate UAT, controlled rollouts and post-release monitoring to prevent false-positive spikes or payment-flow disruption.
To be successful you will need:
Degree in a relevant discipline (advanced degree preferred) and strong Product Owner experience in financial services.
Deep knowledge of Wholesale Banking non-financial risk and relevant regulatory expectations.
Drive delivery of data transformation, enrichment and feature enablement to support model training, monitoring and ongoing optimisation.
Set the strategy for the fraud decision engine (onboarding and early-life), partnering with Data Science to develop and iterate models/rules that improve detection and reduce friction.
Own model performance management by defining KPIs/thresholds (e.g., detection, false positives, alert volumes, loss prevented, customer impact) and running continuous tuning/champion–challenger where applicable.
Ensure explainability and governance are fit-for-purpose (reason codes, auditability, documentation) for investigators, risk committees and regulatory expectations.
Design and implement interventions and playbooks (approve/decline/step-up/hold-review/routing), integrating with downstream systems and agreeing SLAs with Fraud Operations/Investigations.
Establish closed-loop feedback from investigations, disputes and chargebacks; coordinate UAT, controlled rollouts and post-release monitoring to prevent false-positive spikes or payment-flow disruption.
Degree in a relevant discipline (advanced degree preferred) and strong Product Owner experience in financial services.
Deep knowledge of Wholesale Banking non-financial risk and relevant regulatory expectations.
