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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.

redisaillmbigquery+5
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Introducing a New A/B (opens in new tab)

LY Corporation has developed an advanced A/B testing system that moves beyond simple random assignment to support dynamic user segmentation. By integrating a dedicated targeting system with a high-performance experiment assigner, the platform allows for precise experiments tailored to specific user characteristics and behaviors. This architecture enables data-driven decisions that are more relevant to localized or specialized user groups rather than relying on broad averages. ## Limitations of Traditional A/B Testing * General A/B test systems typically rely on random assignment, such as applying a hash function to a user ID (`hash(id) % 2`), which is simple and cost-effective. * While random assignment reduces selection bias, it is insufficient for hypotheses that only apply to specific cohorts, such as "iOS users living in Osaka." * Advanced systems solve this by shifting from general testing across an entire user base to personalized testing for specific segments. ## Architecture of the Targeting System * The system processes massive datasets including user information, mobile device data, and application activity stored in HDFS. * Apache Spark is used to execute complex conditional operations—such as unions, intersections, and subtractions—to refine user segments. * Segment data is written to Object Storage and then cached in Redis using a `{user_id}-{segment_id}` key format to ensure low-latency lookups during live requests. ## A/B Test Management and Assignment * The system utilizes "Central Dogma" as a configuration repository where operators and administrators define experiment parameters. * A Test Group Assigner orchestrates the process: when a client makes a request, the assigner retrieves experiment info and checks the user's segment membership in Redis. * Once a user is assigned to a specific group (e.g., Test Group 1), the system serves the corresponding content and logs the event to a data store for dashboard visualization and analysis. ## Strategic Use Cases and Future Plans * **Content Recommendation:** Testing different Machine Learning models to see which performs better for a specific user demographic. * **Targeted Incentives:** Limiting shopping discount experiments to "light users," as coupons may not significantly change the behavior of "heavy users." * **Onboarding Optimization:** Restricting UI tests to new users only, ensuring that existing users' experiences remain uninterrupted. * **Platform Expansion:** Future goals include building a unified admin interface for the entire lifecycle of an experiment and expanding the system to cover all services within LY Corporation. For organizations looking to optimize user experience, transitioning from random assignment to dynamic segmentation is essential for high-precision product development. Ensuring that segment data is cached in a high-performance store like Redis is critical to maintaining low latency when serving experimental variations in real-time.

redisdata-architectureapache-sparkpersonalization+4
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Won't you become a hacker? (opens in new tab)

Hack Day 2025 serves as a cornerstone of LY Corporation’s engineering culture, bringing together diverse global teams to innovate beyond their daily operational scopes. By fostering a high-intensity environment focused on creative freedom, the event facilitates technical growth and strengthens interpersonal bonds across international branches. This 19th edition demonstrated how rapid prototyping and cross-functional collaboration can transform abstract ideas into functional AI-driven prototypes within a strict 24-hour window. ### Structure and Participation Dynamics * The hackathon follows a "9 to 9" format, providing exactly 24 hours of development time followed by a day for presentations and awards. * Participation is inclusive of all roles, including developers, designers, planners, and HR staff, allowing for holistic product development. * Teams can be "General Teams" from the same legal entity or "Global Mixed Teams" comprising members from different regions like Korea, Japan, Taiwan, and Vietnam. * The Developer Relations (DevRel) team facilitates team building for remote employees using digital collaboration tools like Zoom and Miro. ### AI-Powered Personality Analysis Project * The author's team developed a "Scouter" program inspired by Dragon Ball, designed to measure professional "combat power" based on communication history. * The system utilizes Slack bots and AI models to analyze message logs and map them to the Big 5 Personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). * Professional metrics are visualized as game-like character statistics to make personality insights engaging and less intimidating. * While the original plan involved using AI to generate and print physical character cards, hardware failures with photo printers forced a technical pivot to digital file downloads. ### High-Pressure Presentation and Networking * Every team is allotted a strict 90-second window to pitch their product and demonstrate a live demo. * The "90-second rule" includes a mandatory microphone cutoff to maintain momentum and keep the large-scale event engaging for all attendees. * Dedicated booth sessions follow the presentations, allowing participants to provide hands-on experiences to colleagues and judges. * The event emphasizes "Perfect the Details," a core company value, by encouraging teams to utilize all available resources—from whiteboards to AI image generators—within the time limit. ### Environmental Support and Culture * The event occupies an entire office floor, providing a high-density yet comfortable environment designed to minimize distractions during the "Hack Time." * Cultural exchange is encouraged through "humanity snacks," where participants from different global offices share local treats in dedicated rest areas. * Strategic scheduling, such as "Travel Days" for international participants, ensures that teams can focus entirely on technical execution once the event begins. Participating in internal hackathons provides a vital platform for testing new technologies—like LLMs and personality modeling—that may not fit into immediate product roadmaps. For organizations with hybrid work models, these intensive in-person events are highly recommended to bridge the communication gap and build lasting trust between global teammates.

redisaigen-aiprompt-engineering+5