Smart Data Pipelines for Instant Fraud Detection
Keywords:
Fraud Detection, FinTech Security, Real-Time Data Monitoring, Predictive Analytics, Anomaly Detection, Supervised Learning, Semi-Supervised Learning, Financial Fraud Prevention, Data Engineering Pipelines, Real-Time Data Ingestion, Low-Latency Decisioning, Streaming Data Platforms, Feature Engineering and Storage, Data Quality Management, Transaction Monitoring Systems, Regulatory Compliance in Finance, Attack Vector Analysis, Scalable Data Frameworks, Adaptive Feature Space Management, Financial Risk Analytics.Abstract
Detecting fraudulent activities is a persistent challenge for financial institutions and FinTech companies. For many organizations, monitoring their operations is a significant expenditure. The threat landscape is increasingly dynamic and requires constant vigilance. An attractive alternative is to use real-time data monitoring with predictive analytics to discover anomalies indicative of fraud. Such a system could complement or replace many of the existing detection solutions. If implemented appropriately, it could help with regulatory compliance as well as providing data on new emerging attack vectors. This paper describes a comprehensive architecture designed to enable such detection of possible fraud in FinTech companies. The focus is on data engineering pipelines for supervised, semi-supervised, and anomaly detection based on historical and real-time data. The objective is to build a data framework that can support real-time data ingestion and low-latency decisioning. For the pipelines, common components for ingestion, cleaning, feature storage, and quality are proposed and documented. Existing streaming platforms are evaluated with respect to suitable message schema for real-time decisioning. Road-mapping techniques are discussed to deal with feature-space changes over time. The section is suitable for those considering a data framework to support real-time fraud detection in FinTech or financial companies.
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