About the Role
As a Fraud Analytics, you will responsible for building a data-driven defense framework within the offline merchant ecosystem. This role focuses on developing rule-sets, modeling transaction behaviors, and monitoring risk metrics to minimize financial losses from fraud without hindering business growth.
About Flip
Rafi, Luqman, and Anjar, who were college friends in Universitas Indonesia, started Flip as a project in 2015 to transfer payments to each other at a fraction of what banks would charge them. They are pioneers in the Indonesian market, with their technology now helping millions of Indonesians, both individuals and businesses, carry out bank-to-bank money transfers through a reliable and seamless app.
After five years of operations, Flip has helped Indonesians transfer money worth several trillions of rupiah and has received double-digit funding from respectable investors such as Sequoia India, Insight Partner, and Insignia. Flip's ultimate mission is to give Indonesians access to one of the most progressive and fairest financial services in the world.
What you'll do:
- Rule Development & Optimization: Design, backtest, and implement new detection rules within the Fraud Detection System (FDS) to identify anomalous behavior in QRIS transactions.
- Transaction Monitoring: Dynamically manage transaction thresholds based on merchant risk profiles and Merchant Category Codes (MCC).
- False Positive Analysis: Conduct periodic evaluations of existing rules to ensure effectiveness and minimize impact on legitimate transactions.
- Pattern Recognition: Perform deep-dive analysis on large datasets to uncover emerging fraud trends, such as synthetic identity, merchant collusion, or settlement system manipulation.
- Performance Reporting: Build and maintain risk monitoring dashboards covering key metrics such as Fraud Rate, Detection Rate, and Loss Prevented.
- Simulation & Testing: Simulate the impact of new rules before production deployment to ensure there is no disruption to the transaction success rate.
What You'll Need:
- A minimum of a Bachelor's degree in a related field, such as Computer Science, Information Technology, Statistics, or Mathematics.
- At least 2 years of experience in the fraud/risk domain (experience within an offline merchant environment is a significant plus).
- Proficient in writing complex SQL queries to extract and manipulate data from data warehouses (e.g., BigQuery, PostgreSQL).
- Experienced in using Python or R for statistical analysis, outlier detection, and basic data modeling.
- Strong analytical and problem-solving skills.
- In-depth knowledge of fraud landscapes, including modus operandi, characteristics, and common indicators.
- Proven experience in configuring, deploying, managing, and fine-tuning rules within a specific Fraud Detection System (FDS).
