Associate - Fraud Risk

Bring your expertise to JPMorgan Chase. As a part of Risk Management and Compliance, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class.

As a Risk Management - Fraud Risk Strategy - Senior Associate in Credential Management Strategy team, you will use data and analytics to identify authentication and controls gaps in IVR, Branch and Phone channels. This team develops and manages authentication strategies for Digital Payments, Check losses, Credit and Debit card losses across both monetary and non monetary flows. Leveraging data and actionable insights, you will be responsible for authentication strategies across non digital channels including credential onboarding, authentication credential flows as well as identifying the need for friction to help mitigate fraud losses.

Job Responsibilities :

• Monitoring authentication risks and fraud attack trends to identifying tactical and strategic responses to minimize fraud.
• Collaborating with product, technical and analytics teams to develop and prioritize initiatives and resources to maximize existing capabilities.
• Escalating identified risks which may result in unacceptable fraud controls and losses
• Providing support, including analysis, to various Strategic Fraud initiatives outlined by the business and Fraud Strategy leadership.
• Performing analysis of current and historic data to keep fraud systems and processes at the cutting edge of fraud detection
• Identifying data driven fraud trends and developing broad strategies to address the trend

Required Qualifications, Capabilities and Skills :

Preferred Qualifications, Capabilities and Skills :

Skills Required

Sas, Sql, sequence analysis, Powerpoint, Python, Excel, Machine Learning