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userenigmatic/README.md

Emmanuel Oyebamiji — Data Scientist (AML/Risk)

I build practical AI/analytics for KYC, sanctions screening, and transaction monitoring. My focus is high-signal detection with low false-positives, plus clean dashboards stakeholders can trust.

Focus areas: Transaction anomaly detection · KYC risk scoring · Sanctions/PEP fuzzy matching · SAR drafting support
Tooling: Python (pandas, scikit-learn, rapidfuzz), SQL (T-SQL), Power BI, Streamlit, Git/GitHub

Works

  • Sanctions & PEP Screening — Fuzzy matching + Streamlit dashboard (tests included) → repo
  • KYC Risk Dashboard (SQL + Power BI) — Rules engine + visuals → repo
  • SAR Drafting Prototype — First-draft narratives + completeness/readability checks → repo
  • Transaction Anomaly Detection — Isolation Forest vs z-score vs DBSCAN (benchmark & trade-offs) → repo

-- - - - - - - -- -- - - - - - - - - -

  • Reducing alert noise while keeping investigators focused on real risk
  • Transparent rules + explainable ML that auditors can follow
  • Clean repos that others can clone and run in minutes

Pinned Loading

  1. kyc-risk-dashboard kyc-risk-dashboard Public

    Rules based KYC Risk Scoring Dashboard -SQL and PowerBI. Automates customer classification into Low/Medium/High risk tiers using onboarding data.

    TSQL

  2. sanctions-pep-screening sanctions-pep-screening Public

    Fuzzy matching for sanctions & PEP screening (OFAC list + Onboarding Customers) with Python, Streamlit dashboard - tests.

    Python

  3. transaction-anomaly-detection transaction-anomaly-detection Public

    Detect unusual credit card transactions with unsupervised anomaly detection. compared three methods (z-score, isolation forest, dbscan) and explained trade-offs.

  4. sar_drafting_prototype sar_drafting_prototype Public

    Prototype for automating Suspicious Activity Report (SAR/STR) drafting. Transforms structured transaction records into compliance-ready narratives using Python templates, with built-in evaluation f…

    Jupyter Notebook

  5. false-positive-reduction-lab false-positive-reduction-lab Public

    False-Positive Reduction Lab : rule-based transaction monitoring with threshold tuning and cost trade-offs. Demonstrates how adjusting detection rules reduces noise, lowers investigation cost, and …

    Jupyter Notebook

  6. aml-case-desk aml-case-desk Public

    AML case-management app (SQL Server ---- Python ------ Streamlit): triage, case actions, QA sampling, audit hash-chain, SAR exports.

    Jupyter Notebook