Fraud Data Analyst

Fraud Analytics 1. Develop Fraud Detection Models: Design and deploy predictive analytics models and machine learning algorithms to identify suspicious activities and patterns. 2. Enhance Systems: Continuously optimize fraud monitoring systems to improve detection accuracy and reduce false positives. 3. Data-Driven Insights: Analyze internal and external data sources to identify vulnerabilities and recommend proactive measures. 4. Performance Metrics: Define and track key performance indicators (KPIs) to measure the effectiveness of fraud detection strategies.Market Intelligence 1. Industry Monitoring: Stay updated on emerging fraud tactics, industry benchmarks, and regulatory changes. 2. Competitor Analysis: Analyze competitors’ risk management strategies to identify opportunities for improvement. 3. Market Trends: Provide actionable insights from market intelligence to inform strategic decision-making.Team Leadership 1. Build and Lead: Recruit, develop, and mentor a high-performing team of data analysts, data scientists, and market intelligence specialists. 2. Stakeholder Collaboration: Work closely with risk management, compliance, IT, and other departments to ensure alignment and effective communication.QualificationsEducation • Master’s degree in data science, Statistics, Economics, Business Administration, or a related field.Experience • 10+ years of experience in fraud analytics, risk management, or data analytics • Proven track record of developing and implementing fraud detection systems. • Experience with market intelligence tools and competitive analysis.Technical Skills • Expertise in data analytics tools (e.g., Python, R, SQL, SAS). • Familiarity with machine learning platforms and fraud detection software. • Knowledge of industry-specific regulations and compliance standards.Key Competencies • Strong analytical and problem-solving skills. • Excellent leadership and team-building abilities. • Strategic thinker with strong business acumen. • Effective communicator with the ability to convey complex data insights to non-technical stakeholders.

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