daangn The Journey to Karrot Pay’ (opens in new tab)
Daangn Pay has evolved its Fraud Detection System (FDS) from a traditional rule-based architecture to a sophisticated AI-powered framework to better protect user assets and combat evolving financial scams. By implementing a modular rule engine and integrating Large Language Models (LLMs), the platform has significantly reduced manual review times and improved its response to emerging fraud trends. This transition allows for consistent, context-aware risk assessment while maintaining compliance with strict financial regulations. ### Modular Rule Engine Architecture * The system is built on a "Lego-like" structure consisting of three components: Conditions (basic units like account age or transfer frequency), Rules (logical combinations of conditions), and Policies (groups of rules with specific sanction levels). * This modularity allows non-developers to adjust thresholds—such as changing a "30-day membership" requirement to "70 days"—in real-time to respond to sudden shifts in fraud patterns. * Data flows through two distinct paths: a Synchronous API for immediate blocking decisions (e.g., during a live transfer) and an Asynchronous Stream for high-volume, real-time monitoring where slight latency is acceptable. ### Risk Evaluation and Post-Processing * Events undergo a structured pipeline beginning with ingestion, followed by multi-layered evaluation through the rule engine to determine the final risk score. * The post-processing phase incorporates LLM analysis to evaluate behavioral context, which is then used to trigger alerts for human operators or apply automated user sanctions. * Implementation of this engine led to a measurable decrease in information requests from financial and investigative authorities, indicating a higher rate of internal prevention. ### LLM Integration for Contextual Analysis * To solve the inconsistency and time lag of manual reviews—which previously took between 5 and 20 minutes per case—Daangn Pay integrated Claude 3.5 Sonnet via AWS Bedrock. * The system overcomes strict financial "network isolation" regulations by utilizing an "Innovative Financial Service" designation, allowing the use of cloud-based generative AI within a regulated environment. * The technical implementation uses a specialized data collector that pulls fraud history from BigQuery into a Redis cache to build structured, multi-step prompts for the LLM. * The AI provides evaluations in a structured JSON format, assessing whether a transaction is fraudulent based on specific criteria and providing the reasoning behind the decision. The combination of a flexible, rule-based foundation and context-aware LLM analysis demonstrates how fintech companies can scale security operations. For organizations facing high-volume fraud, the modular approach ensures immediate technical agility, while AI integration provides the nuanced judgment necessary to handle complex social engineering tactics.