Card Fraud Prevention Senior Analyst

Company Description

Wise is a global technology company, building the best way to move and manage the world’s money. Min fees. Max ease. Full speed. Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money. As part of our team, you will be helping us create an entirely new network for the world's money. For everyone, everywhere.

Job Description

More about our mission and what we offer. Salary Ranges: 8,500 - 10,000 SGD Per Month + RSUs.

Card payments represent a large part of the funds moved to Wise daily, and card fraud disputes are an inevitable part of that. Our Card Fraud team thus supports Wise’s mission in a very impactful area. We have an awesome operational team handling these Card Fraud disputes in four countries across the globe; a unified team that works very closely with our dedicated engineering and product teams.

As a Card Fraud Prevention Senior Analyst, you get to support your team’s operational success and bring your expertise to upscale the domain; driving detection and prevention focused initiatives with long-lasting impacts will be part of your daily work.

Key Responsibilities and Contributions to the Card Disputes Team

Reducing Card Fraud Loss (Spend Product Costs)

• Analysing Card Fraud dispute trends, independently querying databases and doing data deep dives.
• Prioritising and delegating Card Fraud prevention workflows in line with our KPIs.
• Card Fraud prevention rule creation: rule scope validation, rule creation, and monitoring post rule implementation.
• Prioritising customer experience/impact from rule declines by writing rules with a high level of accuracy and quality that do not unnecessarily decline transactions causing a loss of revenue for Wise.
• Working closely with Product and Engineering teams to drive optimal customer experience from fraud declines and alerts.
• Working closely with the Data science team on machine learning models.
• Leading long term Card Fraud prevention projects contributing to the KRIs/KPIs/OKRs.
• Providing data and writing rules in relation to incidents, and managing incidents if needed.
• Effectively communicate and explain Card Fraud prevention strategies to Wisers who are not on the fraud prevention team.

Card Fraud Prevention and Risk Management

• Protect our Wise cardholders from unauthorised card usage, scams, and payment instrument and account takeover fraud.
• Spend fraud rule creation and optimisation to mitigate the impact of card fraud on Wise cardholders suffering monetary loss, Spend fraud BPS (scheme compliance & fees), and unrecoverable fraud loss (product cost).
• Understand and implement machine learning scores within static fraud rules to improve rule precision and recall.
• Work with data science on the optimisation and periodical retraining of the internal card fraud machine learning model.
• Own longer term projects targeted at card fraud reduction.
• Engage Spend Product and Engineering teams to drive rule engine improvements, with the focus of optimising the card fraud customer journey from rule declines to rule alerts and dispute submission.
• Staying up to date with fraud trends highlighted by the card schemes, governmental organisations, and the media.

Team Direction and Analytics Management

• Prioritise and delegate work for a small team of analysts, effectively driving the team to focus on highest leverage activities. Plan, structure and coordinate analyst work effectively, responding appropriately to challenges as they arise.
• Ensure that card fraud prevention work is prioritised in line with our OKRs, KPIs, and overall strategy. Deliver or materially contribute to the prevention of large scale card fraud waves or prevention projects, and is good at helping others overcome blockers.
• Lead projects and develop up to 1-2 analysts by coaching and supporting analysts in a manner that is aligned with their development needs and with the aim of increasing their impact.

KPI & Performance Measurement Management

• Managing and ensuring KPI and performance metrics accessibility to Teams.
• Ensuring any required data is available to the Card Fraud Ops team - either by providing the data in an accessible manner or by driving engineering changes where necessary.
• Strong collaboration with Leadership on KPI (maintenance & building).
• Identifying the problematic areas based on KPI performance.
• Identifying product issues and improvement areas (cross team).

Incident Management

• Providing data and relevant insights to all stakeholders for the purposes of incident management.
• Able to identify card fraud prevention gaps and come up with strategies to mitigate risk and resolve the incident.
• Able to perform in an incident manager role if needed.
• Escalating risks sitting outside of the Card Fraud domain (or appetite) to external stakeholders.

Qualifications

About You

• Your verbal and written English skills are excellent. If you speak other languages then that’s a bonus!
• You have strong attention to detail, punctuality, and are comfortable taking initiatives.
• You are good with routine but can also adapt and keep up with fast changes.
• You are able to work independently but also know how important good team-work is.
• You can make decisions in critical situations and have the ability to multitask.
• You have a genuine enthusiasm for the FinCrime industry.
• You have experience with SQL, or a similar coding language, and have experience with FinCrime prevention strategy creation and management.

Your Analytical And Strategic Abilities

• Able to competently think through analytical tasks and fully understand the impact of card fraud rules on the spend product and on our customers.
• Deliver quality analysis that addresses the business problem.
• Comfortable with complexity and aware of key factors underlying a decision.
• Able to propose ideas autonomously to deflect card fraud waves.
• Able to code both independently and in collaboration with other analysts, and write legible code that is usable by others.
• Able to test fraud rule conditions autonomously and troubleshoot code and risk engine related issues.
• Able to understand the interaction between metrics over multiple products/services across multiple teams and directs effort towards high leverage activities.
• Comfortable learning new data analysis techniques and methods.

Key Skills/Competency

• SQL
• Data Analysis
• Fraud Prevention
• Risk Management
• Machine Learning Models
• Incident Management
• KPI Management
• Stakeholder Communication
• Strategic Thinking
• FinCrime Industry Knowledge