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The-Black-Needles/README.md

Ricardo Mezonato de Almeida

Fraud Analytics · Financial Crime · Risk Decisioning · Applied AI

I work at the intersection of fraud prevention, financial crime, security and data analytics, with a growing focus on explainable decisioning, intelligent automation and responsible AI.

My work and projects emphasize turning behavioral and transactional evidence into auditable decisions while balancing fraud detection, customer impact and operational constraints.

Current Focus

  • Fraud analytics and behavioral risk signals
  • Financial crime / AML analytics
  • Explainable fraud controls and decisioning
  • SQL and Python for analytical workflows
  • Applied AI and agentic automation
  • AI governance, security and human oversight
  • DFIR and threat investigation

Selected Projects

Case 06 — Financial Risk Decision Intelligence

case06-financial-risk-decision-intelligence

Explainable Fraud Analytics, Entity Intelligence & Controlled Decisioning

Reproducible synthetic fraud analytics with authorization-safe temporal and entity features, explainable deterministic controls, customer-friction trade-offs, independent out-of-time validation, delayed-feedback monitoring and a governed read-only AI analyst assistant.

The project includes a self-contained five-scenario quick demo and explicit boundaries around leakage, customer impact, ML complexity and responsible AI.

Case 04 — AML / Financial Crime Analytics

case04-aml-ft-case

Reproducible financial-crime analytics combining deterministic rules, backtesting, customer-aware evaluation, explainable ML and controlled AI-assisted architecture.

Case 02 — DFIR & Financial Security

case02-reveal-dfir-volatility3-aml

Memory forensics and behavioral investigation with Volatility 3, Python/SQL, timeline reconstruction, IoCs and AML-oriented analytics.

Case 03 — SOC / Threat Hunting

case03-paloalto-rce-elastic-soc

Elastic/Kibana investigation of a simulated remote-code-execution incident using hypothesis-driven hunting, queries, timeline, IoCs and detections.

How I Work

Evidence over intuition · Reproducibility · Explainability · Adversarial validation · Customer centricity · Responsible AI

I prefer simple, auditable solutions first and add complexity only when it demonstrates measurable incremental value.

Core Toolkit

Data: Python · SQL · Pandas

Fraud & Risk: behavioral analysis · rule engineering · backtesting · false-positive analysis · decisioning

Security: SIEM · DFIR · threat investigation · IoCs

Applied AI: LLM workflows · agentic systems · MCP · evaluation · governance

Pinned Loading

  1. case06-financial-risk-decision-intelligence case06-financial-risk-decision-intelligence Public

    Explainable fraud analytics and financial risk decisioning with temporal entity intelligence, OOT validation, customer-impact analysis, and governed AI.

    Python

  2. case04-aml-ft-case case04-aml-ft-case Public

    Reproducible AML/FT and financial crime analytics with rules, backtesting, explainable ML, customer-aware evaluation, and human-governed AI architecture.

    Python 1

  3. case05-ai-career-intelligence case05-ai-career-intelligence Public

    AI Career Intelligence portfolio project with a deterministic synthetic requirement/evidence demo, strict validation, traceability, and human review.

    Python