Fraud Data Analyst

About Artajasa

PT Artajasa Pembayaran Elektronis (ATM Bersama) is one of Indonesia's leading payment infrastructure providers, delivering secure, reliable, and high-availability transaction services for banks and financial institutions. As a key enabler of the national payment ecosystem, Artajasa operates mission-critical platforms that support interbank connectivity, fund transfers, switching, clearing, and settlement services across Indonesia. Through continuous innovation and technology advancement, Artajasa is committed to building scalable and resilient payment solutions that support the future of digital finance.

About the Role

We are looking for Fraud Data Analyst to transform transaction data into actionable insights that strengthen fraud detection and prevention. This role is responsible for analyzing transaction patterns, identifying emerging fraud trends, and supporting the development of data-driven strategies to mitigate fraud risks across payment systems.

Key Responsibilities

Analyze transaction data to identify fraud patterns, anomalies, and emerging risks, develop dashboards and analytical reports to support fraud monitoring and decision-making, perform data mining and trend analysis to improve fraud detection capabilities, collaborate with Fraud, Risk, IT, and Operations teams to support fraud investigations and root cause analysis, support the development and optimization of fraud detection rules and analytical models, ensure data quality, accuracy, and consistency across fraud analytics activities, and provide insights and recommendations to strengthen fraud prevention strategies.

Qualifications

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Information Systems, Accounting, or a related field.
• Minimum 2 years of experience in data analysis, fraud analytics, risk analytics, or a related field.
• Strong proficiency in SQL and Microsoft Excel for data extraction and analysis.
• Experience with Power BI, Tableau, or similar data visualization tools.
• Basic knowledge of Python or R for data analysis is an advantage.
• Strong analytical thinking with the ability to identify patterns, anomalies, and fraud indicators from large datasets.
• Understanding of electronic payment systems, fraud monitoring, or financial transaction analysis is highly preferred.
• Excellent problem-solving skills, attention to detail, and ability to communicate analytical findings effectively.