Required stack
필요 기술
Cloud ComputingData ScienceData AnalyticsAnomaly DetectionFraud DetectionAPI DevelopmentApache KafkaApache SparkPayment ProcessingArchitectureReal-time ProcessingData StreamingData LakeMachine LearningRules EngineCloud ArchitectureMonitoringAlerting Systems
Project brief
프로젝트 내용
I need a clear, scalable architecture that can detect and instantly alert on unusual spending patterns in our payment flow. The focus is narrow and well-defined: draw insights from live and historical transaction history, spot deviations from normal customer behavior, and trigger actionable alerts before settlement is completed.
Scope of work
• Map the entire transaction-processing path and identify points where real-time analytics can be inserted without adding noticeable latency.
• Define the data lake or streaming layer that will store raw and enriched transaction history.
• Recommend the analytics engine—rules, machine-learning models, or a hybrid—best suited for spotting spending anomalies while remaining extensible to other fraud signals later (for example, unauthorized access or multiple failed attempts).
• Outline the alerting pipeline, including severity tiers, notification channels, and feedback loops for analysts.
• Produce an architecture diagram, tech-stack rationale, and a brief PoC plan showing how data will move from acquisition to alert.
Acceptance criteria
1. Architecture diagram in PDF or PNG with all major components labeled.
2. Written description (max 5 pages) explaining data flow, detection logic, scalability assumptions, and monitoring strategy.
3. PoC plan proving sub-second alert generation on a synthetic data set of at least 1 M transactions.
I will provide anonymized transaction logs, sample API schemas, and current infrastructure details as soon as we kick off.
Scope of work
• Map the entire transaction-processing path and identify points where real-time analytics can be inserted without adding noticeable latency.
• Define the data lake or streaming layer that will store raw and enriched transaction history.
• Recommend the analytics engine—rules, machine-learning models, or a hybrid—best suited for spotting spending anomalies while remaining extensible to other fraud signals later (for example, unauthorized access or multiple failed attempts).
• Outline the alerting pipeline, including severity tiers, notification channels, and feedback loops for analysts.
• Produce an architecture diagram, tech-stack rationale, and a brief PoC plan showing how data will move from acquisition to alert.
Acceptance criteria
1. Architecture diagram in PDF or PNG with all major components labeled.
2. Written description (max 5 pages) explaining data flow, detection logic, scalability assumptions, and monitoring strategy.
3. PoC plan proving sub-second alert generation on a synthetic data set of at least 1 M transactions.
I will provide anonymized transaction logs, sample API schemas, and current infrastructure details as soon as we kick off.