AML Investigations • Transaction Monitoring • KYC/EDD • SAR • Sanctions • Fraud | SQL • Python • Tableau • Streamlit

I combine hands-on AML and financial crime investigation experience with data analytics, SQL, Python, Tableau, and Streamlit to investigate suspicious activity, analyze financial crime risk, improve transaction monitoring, and transform complex compliance data into actionable intelligence.

My portfolio demonstrates the intersection of:

AML Domain Expertise + Investigations + Data Analytics + Business Intelligence

📍 Oakland, CA

📧 denislinkme@gmail.com

🔗 LinkedIn

🐙 GitHub

I am a Financial Crime & AML Analytics professional with 5 years of experience across AML investigations, transaction monitoring, KYC/CDD/EDD, sanctions screening, fraud investigations, customer risk assessment, alert review, escalation, and SAR decision support.

My professional experience includes working with complex transaction activity, customer profiles, high-risk relationships, sanctions and PEP screening, fraud indicators, and system-generated AML alerts.

I complement that domain experience with technical analytics skills in:

SQL • Python • Tableau • Streamlit • Data Analysis • Feature Engineering • Risk Analytics • Dashboard Development

This combination allows me to approach financial crime from both an investigator's perspective and an analytics perspective.

My portfolio follows an end-to-end financial crime analytics workflow:

Customer / Transaction Data
            ↓
Data Quality & EDA
            ↓
Feature Engineering
            ↓
Customer & Transaction Risk Analysis
            ↓
Transaction Monitoring / Screening
            ↓
Alert Generation & Prioritization
            ↓
Investigation
            ↓
KYC / EDD / OSINT Review
            ↓
Case Decision & Escalation
            ↓
SAR Decision Support
            ↓
QA / Rule Performance Analysis
            ↓
Management Reporting & Dashboards

The objective is not simply to generate alerts.

The objective is to turn data → risk signals → investigation → defensible decisions → actionable financial crime intelligence.

An end-to-end transaction monitoring and AML investigation project designed to identify unusual transaction activity, prioritize alerts, analyze customer behavior, and support investigator decision-making.

• Transaction Monitoring
• AML Alert Investigation
• Suspicious Activity Detection
• Transaction Pattern Analysis
• Customer Behavior Analysis
• Risk-Based Alert Prioritization
• Investigation Analytics
• Escalation Decision Support

Transactions
     ↓
Monitoring Scenarios
     ↓
Risk Indicators
     ↓
Alerts
     ↓
Alert Prioritization
     ↓
Investigation
     ↓
Close / Escalate

SQL Python Tableau Streamlit EDA Feature Engineering

Live App: Streamlit App

🔗 Repository:AML Transaction Monitoring

A case investigation analytics project demonstrating how transaction alerts, customer risk information, transaction history, and investigative findings can be brought together to support AML case decisions and SAR escalation.

• AML Case Investigation
• Transaction Analysis
• Customer Risk Review
• Case Prioritization
• Suspicious Activity Assessment
• SAR Decision Support
• Investigation Narratives
• Escalation Analysis

Alert
   ↓
Customer Profile
   ↓
Transaction History
   ↓
Investigation
   ↓
Risk Assessment
   ↓
Case Decision
   ↓
Close / Escalate / SAR Review

SQL Python Tableau Streamlit AML Analytics

Live App: Streamlit App

🔗 Repository:AML Case-SAR-Decision

An AML quality assurance and transaction-monitoring analytics project focused on evaluating alert effectiveness, investigator outcomes, false positives, scenario performance, and monitoring-rule quality.

• AML Quality Assurance
• Alert Quality
• Transaction Monitoring Rules
• False-Positive Analysis
• Scenario Performance
• Alert-to-Case Conversion
• Investigator Outcomes
• Rule Effectiveness
• QA Reporting

Monitoring Rules
       ↓
Generated Alerts
       ↓
Investigation Outcomes
       ↓
QA Review
       ↓
False Positive Analysis
       ↓
Rule Performance
       ↓
Optimization Opportunities

SQL Python Tableau Streamlit QA Analytics

Live App: Streamlit App

🔗 Repository: AML Alert Quality

A customer-risk analytics project demonstrating how KYC information, customer characteristics, risk indicators, transaction behavior, and enhanced due diligence can be combined to identify and prioritize higher-risk customers.

• KYC
• CDD
• Enhanced Due Diligence
• Customer Risk Rating
• High-Risk Customer Identification
• Risk Segmentation
• Customer Profiling
• Ongoing Monitoring
• Risk-Based Review Prioritization

Customer Onboarding
       ↓
KYC / CDD
       ↓
Risk Factors
       ↓
Customer Risk Score
       ↓
Low / Medium / High Risk
       ↓
EDD
       ↓
Ongoing Monitoring

SQL Python Tableau Streamlit Risk Analytics

Live App: Streamlit App

🔗 Repository: AML Customer-KYC-EDD

A sanctions-screening analytics project designed to evaluate customer and transaction screening alerts, potential sanctions exposure, match quality, and investigation outcomes.

• Sanctions Screening
• OFAC
• Watchlist Screening
• PEP Risk
• Customer Screening
• Counterparty Screening
• Transaction Screening
• Match Analysis
• False-Positive Review
• Compliance Reporting

Customer / Counterparty / Transaction
                 ↓
           Screening
                 ↓
        Potential Match
                 ↓
       Alert Investigation
                 ↓
True Match / False Positive / Escalate

SQL Python Tableau Streamlit Sanctions Analytics

Live App: Streamlit App

🔗 Repository: AML Sanctions Compliance

A broader financial crime intelligence project bringing together transaction monitoring, customer risk, suspicious activity, investigative outcomes, geographic exposure, and management reporting.

• Financial Crime Analytics
• Transaction Risk
• Customer Risk
• Suspicious Activity Trends
• Geographic Risk
• Product Risk
• Case Analytics
• Investigation Funnel Analysis
• Financial Crime Intelligence
• Management Reporting

Customers + Transactions + Alerts
              ↓
       Financial Crime Data
              ↓
       Risk Segmentation
              ↓
       Pattern Analysis
              ↓
     Investigation Trends
              ↓
       Risk Intelligence
              ↓
     Management Dashboard

SQL Python Tableau Streamlit Financial Crime Analytics

Live App: Streamlit App

🔗 Repository: AML Financial-Crime

A fraud and suspicious-transaction analytics project focused on detecting unusual customer and transaction behavior through engineered risk indicators and behavioral patterns.

• Fraud Detection
• Suspicious Transaction Detection
• Behavioral Analysis
• Transaction Velocity
• Unusual Amount Patterns
• Customer Risk Signals
• Pattern Detection
• Risk Scoring
• Fraud Analytics
• Investigation Prioritization

Transaction Data
       ↓
EDA & Data Quality
       ↓
Feature Engineering
       ↓
Behavioral Indicators
       ↓
Suspicious Patterns
       ↓
Risk Scoring
       ↓
Investigation Prioritization

SQL Python Tableau Streamlit Fraud Analytics

Live App: Streamlit App

🔗 Repository: AML Fraud-Suspicious

Each project includes a structured analytical workflow to demonstrate that financial crime analysis begins before the dashboard.

1. Load Data
2. Dataset Review
3. Missing Value Analysis
4. Duplicate Validation
5. Datatype Cleaning
6. Column Standardization
7. Data Quality Checks
8. Outlier Detection
9. Range Validation
10. KPI Validation
11. Feature Engineering
12. Business Rule Validation
13. Summary Statistics
14. Final Dataset Export
15. Insight Generation

• Transaction Monitoring Alert Review
• Customer Activity Analysis
• Suspicious Activity Investigation
• Alert Disposition Analysis
• Case Escalation
• SAR Decision Support
• Investigation Documentation
• High-Risk Account Review

• KYC
• CDD
• Enhanced Due Diligence
• Customer Risk Assessment
• High-Risk Customer Reviews
• Risk Segmentation
• Customer Profiling
• Ongoing Monitoring
• PEP Review
• Negative News / Adverse Media Research

• OFAC Screening
• Sanctions Alert Review
• Customer Screening
• Counterparty Screening
• Transaction Screening
• Potential Match Analysis
• False-Positive Analysis
• Escalation Support

• Fraud Investigation
• Suspicious Transaction Analysis
• Behavioral Pattern Detection
• Transaction Pattern Analysis
• Risk Indicators
• Anomaly Investigation
• Fraud Risk Analytics

• Transaction Monitoring
• Scenario Analysis
• Alert Analytics
• Alert Quality
• False-Positive Analysis
• Rule Performance
• QA Analytics
• Investigation Outcomes
• Alert-to-Case Analysis

CTEs • Window Functions • Joins • CASE WHEN • Subqueries • Aggregations • Date Functions • Risk Segmentation • KPI Development • Transaction Analysis

I use SQL to move beyond simple querying and answer financial crime questions such as:

• Which customers demonstrate unusual transaction behavior?
• Which monitoring scenarios generate the highest number of alerts?
• Which customers have multiple risk indicators?
• Which transaction patterns warrant investigation?
• Which alerts are repeatedly closed as false positives?
• Where are financial crime risks concentrated?

Pandas • NumPy • Data Cleaning • EDA • Feature Engineering • Risk Analysis • Pattern Detection • Data Validation

Python is used throughout the portfolio for data preparation, exploratory analysis, engineered risk indicators, transaction analysis, customer profiling, and analytical datasets.

I use Tableau to transform financial crime data into investigator and management-facing visual analytics covering:

• Alert Trends
• Customer Risk
• Transaction Risk
• Investigation Outcomes
• Fraud Exposure
• Sanctions Screening
• QA Performance
• Geographic Risk
• Financial Crime KPIs

Streamlit is used to turn analytical projects into interactive applications where users can explore financial crime data, apply filters, investigate risk patterns, review KPIs, and interact with analytical outputs.

My professional and portfolio experience covers the financial crime lifecycle:

KYC / Customer Onboarding
          ↓
Customer Risk Assessment
          ↓
Transaction Monitoring
          ↓
Alert Review
          ↓
Investigation
          ↓
EDD / Additional Research
          ↓
Case Escalation
          ↓
SAR Decision Support
          ↓
QA / Monitoring

AML • BSA • KYC • CDD • EDD • Transaction Monitoring • SAR • OFAC • Sanctions • PEP • Fraud • Customer Risk • Alert Investigation • Financial Crime Analytics

Professional exposure includes financial crime, investigation, case-management, analytics, and research platforms such as:

Actimize • SAS • Unit21 • Verafin • LexisNexis • RDC • Salesforce • Looker • Zendesk • Documentum

Combined with:

SQL • Python • Tableau • Streamlit

This portfolio is not designed to demonstrate dashboards alone.

It demonstrates how AML domain knowledge and analytics work together.

AML Investigation Experience
            +
Transaction Monitoring Knowledge
            +
KYC / EDD / Sanctions / Fraud
            +
SQL & Python
            +
EDA & Feature Engineering
            +
Tableau & Streamlit
            =
Financial Crime & AML Analytics

I approach financial crime analytics with the question:

What does the data tell the investigator, what risk does it reveal, and what decision should it support?

My primary career focus is at the intersection of financial crime investigations and analytics.

• 🔎 Financial Crime Analyst
• 🏦 AML Analyst / AML Investigator
• 📊 Financial Crime Analytics
• 💳 Transaction Monitoring Analytics
• 👤 KYC / CDD / EDD Analytics
• 🌐 Sanctions Analytics
• 🚨 Fraud Analytics
• 📈 AML Data Analytics
• ⚙️ AML Alert Quality / QA Analytics
• 🧠 Financial Crime Intelligence

My goal is to use AML domain expertise + SQL + Python + Tableau + Streamlit to help financial institutions identify suspicious activity, prioritize risk, improve investigations, reduce ineffective alerts, and make stronger data-driven financial crime decisions.

🎓 Bachelor of Education — Mathematics

• Tableau Desktop Specialist
• Data Science
• Generative AI
• SQL
• Python
• Business Intelligence
• Data Analytics

I'm interested in opportunities involving:

Financial Crime Analytics • AML Investigations • Transaction Monitoring • KYC/EDD • Sanctions • Fraud Analytics • AML Data Analytics

🐙 GitHub: github.com/Denis0242

Building financial crime analytics solutions that connect data, risk, investigations, and decisions.

Financial Crime & AML Analytics | AML Investigations • Transaction Monitoring • KYC/EDD • SAR • Sanctions • Fraud | SQL • Python • Tableau • Streamlit