Explore my AML and financial crime experience, certifications, interactive project demos and SQL work samples.
Work experience · Projects · SQL examples · Contact
I'm Brandon Candela, an AML transaction monitoring professional applying investigative experience to public blockchain data, sanctions review and adverse media screening workflows. I build educational portfolio projects with AI-assisted development, focusing on evidence, alternative explanations, clear documentation and quality control (QC).
Try the consulting case · Source and tests
Compare payments growth, fraud losses and review capacity using a transparent financial model. Stress the assumptions, evaluate staffing economics and write a decision memo for a 90-day pilot. An independent synthetic work sample demonstrating business analysis and risk strategy, not a real client engagement.
Try the public demo · Source code · SQL queries
Explore three transaction-monitoring scenarios, adjust thresholds and see both alert-volume changes and missed seeded cases. Inspect real SQLite queries, document an analyst assessment and role-play QC review. Includes synthetic fixtures, SQL tests and an implementation/UAT guide. Reviews stay in the visitor's browser; this is an independent educational work sample, not a production compliance platform.
My background is in AML transaction monitoring. These public work samples show how I apply investigative judgment to identity comparisons, evidence gaps, documented decisions and QC, with SQL and AI-assisted application development.
For name-screening work, explore Adverse Media Review Desk for SQL and name-screening work: try the demo, inspect the SQL schema and examples, and review the SQL triage view.
SQL demonstrated: relational tables, foreign keys, JOINs, CTEs, CASE expressions, GROUP BY aggregations, JSON extraction and database migrations in SQLite / Cloudflare D1. Training data is fictional; this is a portfolio demonstration, not a claim of production SQL experience.
Role interests: AML investigations, transaction monitoring, adverse media screening, sanctions review, crypto compliance and AML data analysis.
| Project | What you can explore | Links |
|---|---|---|
| SQL Financial Crime Lab | Real SQLite monitoring queries, threshold tradeoffs, seeded-case evaluation, analyst assessments and local QC role-play. | Demo · SQL, code and tests |
| Trace Desk: USDT Investigator | A saved Ethereum case with 15 addresses, recorded transfers, a relationship graph and an investigation report. | Demo · Code and methodology |
| Trace Desk AML Workspace | Bounded USDT transaction reviews, pinned evidence, portable case files, analyst assessments and QC feedback. | Demo · Code and methodology |
| Sanctions Review Desk | OFAC SDN name and alias search, individual and business case comparisons, evidence requests and documentation review. Official-data fallback refreshes hourly and rejects copies older than 24 hours. | Demo · Code and methodology |
| Adverse Media Review Desk | SQL / SQLite training database, JOINs, CTEs, CASE-based triage and aggregate reporting, alongside customer-to-media comparisons and QC. | Demo · Code and methodology |
Start with the evidence. Separate observations from assumptions. Consider what could challenge an interpretation. Give another analyst enough context to review the conclusion.
These are independent educational work samples, not production compliance systems or employer-endorsed products. A transaction pattern or name match is an investigation lead, not proof of wrongdoing or sanctions clearance. Each project documents its scope and limitations.