Global Financial Regulatory Transformation & Remediation Program Core Forensic Team Member

Global Financial Regulatory Transformation & Remediation Program Core Forensic Team Member | Tier...Global Financial Regulatory Transformation & Remediation Program Core Forensic Team Member | Tier...

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Mahdiyar Balar

• Jul 18

Global Financial Regulatory Transformation & Remediation Program

Core Forensic Team Member | Tier-1 Global Financial Institution Core Mandate Executed remediation program mandated by US/European regulators addressing high-priority findings within Financial Crime Compliance (FCC) framework Key Contributions • Forensic Look-Back Reviews: Performed retrospective analysis of complex transaction data to identify potential financial crime risks • KYC/AML Remediation: Led remediation of high-risk adherence gaps • Regulatory Assurance: Supported preparation of data artifacts/reports demonstrating institutional progress closing compliance deficiencies Outcome Played key role clearing backlog cases and validating bank’s compliance data integrity, directly supporting regulatory status stabilization.

Business Strategy Project Management Microsoft Excel Microsoft Power BI Microsoft PowerPoint

omowunmi ayobami

• Jul 15

FTMO $200K Challenge Passed | Prop Firm EA Recovery

Description

The client came to me with an FTMO $200K Challenge account that was already 9% into drawdown. I adjusted and optimized the trading bot to better manage risk and improve execution. The account recovered and successfully passed Phase 1.

What I did:

Optimized the Expert Advisor

Improved risk management

Fine-tuned trade entries and exits

Reduced unnecessary trades

Made the EA more stable for prop firm rules

If you need help building, fixing, or optimizing an MT4/MT5 EA for prop firms like FTMO, feel free to get in touch.

Financial Analysis TradingView

Advik Rathee

• Jul 14

Built this one for an e-commerce customer behavior project - 18,000+ customers, segmented using RFM (Recency, Frequency, Monetary) analysis.

The interesting part wasn't the segmentation itself, it was what it revealed: nearly 22% of customers were "at risk" of churning, and most businesses wouldn't know that until those customers had already stopped buying.

This is the kind of insight that's usually sitting in your order data already, it just needs the right lens. Happy to take a look at your numbers if you're curious what's hiding in yours.

Tools used: Power BI, SQL, Python (Pandas, Scikit-learn for clustering)