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Product Manager – Fraud Strategy – FinTech

Zyoin
CompanyZyoin
CategoryUncategorised
LocationBangalore
RemoteOn-site (inferred)
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted8 Dec 2024
Last verified12 Aug 2026
SourceThe employer's own careers page (company_site)
Applications are handled by the employer, not by us.Apply on the employer's site →
Description
Responsibilities: – Design and execute comprehensive fraud prevention and detection strategies tailored to a fintech environment. – Monitor and analyze emerging fraud trends, adapting strategies to mitigate risks promptly. – Utilize advanced data analytics to identify patterns and anomalies indicative of fraudulent activities. – Develop and maintain real-time dashboards and reports to track fraud trends and measure the effectiveness of prevention strategies. – Present insights and recommendations to senior management to inform decision-making. – Oversee day-to-day fraud detection operations, ensuring timely and accurate identification and investigation of suspicious activities. – Implement and optimize fraud detection tools and systems to enhance the company’s fraud prevention capabilities. – Create and update fraud-related policies and procedures, ensuring compliance withv industry standards and regulatory requirements. – Collaborate with Product, Engineering, Legal, Compliance, and Customer Support teams to address fraud-related issues and implement preventive measures. – Engage with external partners, industry groups, and law enforcement to stay informed about new fraud tactics and prevention methods. Requirements: – Atleast 5-6 years of experience in fraud prevention, risk management, or a related, preferably within the fintech industry – Familiarity with US BSA/AML regulations(preferred) – Proven experience in developing and implementing effective fraud prevention strategies. – Strong analytical and problem-solving skills with prociency in data analysis tools (e.g., SQL, Python, R). – Experience with fraud detection software and machine learning techniques. – Strong understanding of regulatory requirements and industry best practices related to fraud and financial crimes.