EdTech Trust & Safety Risk Scoring Platform
Automated risk scoring system and operational dashboard evaluating accounts across 7 fraud vectors, scoring approximately 2,000 accounts at launch.
Context
The Trust & Safety and Risk teams at an enterprise course creation and creator education platform relied on manual, ad-hoc investigations to flag suspicious accounts for payment abuse, identity fraud, chargebacks, and buy-now-pay-later (BNPL) exploitation. They needed an automated scoring engine coupled with operational dashboards to surface high-risk accounts systematically.
What I Built
- Co-designed the initial database schema, entity-relationship diagrams, and scoring data models from day one.
- Successfully proposed and locked in an unbounded, additive scoring methodology over a rigid 0–100 scale, preventing score saturation and maintaining mathematical composability.
- Owned client-facing technical discovery and execution: defined integration requirements for payment verification and external fraud-detection APIs, resolved warehouse permissions, and coordinated Web Application Firewall (WAF) rule adjustments.
- Designed and estimated task breakdowns covering all 7 weighted risk dimensions: Payment & Financial Risk, Identity Integrity, Network/Device Risk, Buyer Risk, Content Legitimacy, Reputation History, and Behavioral Sensitivity.
- Implemented background cron workflows, operational risk dashboards with dynamic filtering/search, and administrative controls.
Decisions & Trade-offs
- Additive vs. Bounded Normalization: Defended keeping raw risk points additive in the database and data warehouse rather than clamping scores to a fixed 0–100 range in storage. This ensured historical comparisons remained consistent as new risk signals were introduced, isolating percentage normalization purely to the frontend display.
- Batching vs. Real-time API Quotas: Because the external fraud scoring and identity APIs enforced tight rate limits, scheduled cron jobs were partitioned into micro-batches with retry strategies and allowlisted IP routing to prevent throttling errors.
Result
Shipped the live Risk Dashboard and automated workflows, ingesting and scoring approximately 2,000 accounts with identity and network telemetry at launch. The platform remained in active production operation for months, providing risk analysts with centralized triage.
Verified Engineering Claims
Every statement below is validated against production records and verified sources.
Engineered a Trust & Safety risk-scoring pipeline that ingested and scored approximately 2,000 accounts with identity and network data at launch.
Architected a 7-vector weighted scoring model evaluating payment risk, identity integrity, network attributes, buyer signals, and behavioral changes.
Advocated for and implemented an unbounded additive scoring architecture, keeping percentage normalization isolated to the frontend presentation layer.
Orchestrated complex background cron workflows and scheduled data pipelines integrating cloud data warehouse tables with third-party fraud APIs.
Managed tight third-party API rate limits and IP allowlisting constraints to ensure uninterrupted fraud evaluation without throttling.