* Built an end-to-end AML simulation using **Python**, generating **9M+ records** across customers, transactions, and alerts to mimic real-world financial behavior and suspicious activity patterns.
* Wrote advanced **SQL (CTEs + joins)** to segment **high-risk customers**, calculate alert counts, and filter transactions over the past 90 days with aggregated exposure metrics.
* Engineered a custom **risk scoring model** in Python using transaction thresholds and alert volume to classify customers as Elevated or Critical risk.
* Designed **interactive Tableau dashboards** (Risk Heatmap, Alert Efficiency, Risk vs. Avg Amount) to visualize cross-country AML exposure and alert effectiveness.
* Normalized multi-currency data and integrated key AML metrics like **alert rate (%)**, **avg USD amount**, and **transaction volume** for actionable reporting.
- **Built KPI-ready metrics** (alert rate, avg USD exposure, transaction volume) to support AML performance reporting and enable cross-country risk comparisons.
* Built an end-to-end AML simulation using **Python**,
generating **9M+ records** across customers,
transactions, and alerts to mimic real-world
financial behavior and suspicious activity patterns.
* Wrote advanced **SQL (CTEs + joins)** to classify
**high-risk customers**, calculate alert counts, and
filter transactions over the past 90 days with
aggregated metrics.
* Engineered a **risk scoring model** in Python
using transaction thresholds and alert volume to
classify customers as Elevated or Critical risk.
* Designed **interactive Tableau dashboards** (Risk
Heatmap, Alert Efficiency, Risk vs. Avg Amount) to
* **Reduced reporting deployment time by 66%** by building a CLI-based solution to push compiled binaries directly into Kubernetes/Openshift clusters, accelerating turnaround for testing and data validation workflows.
* **Decreased manual configuration errors by 80%** by automating service discovery and dynamic config updates, aligning with AML’s goal of minimizing operational risk and improving data integrity.
* **Improved system reliability** during production launches by implementing startup probes for legacy services, reducing downtime and enhancing stability for automated monitoring/reporting pipelines.
* **Enhanced CI pipeline reproducibility and performance** by rewriting the Jenkins nightly pipeline to support automated PR-level testing with reusable parameters, improving report consistency across environments.
* **Collaborated cross-functionally** with developers and testers to maintain reliable infrastructure, echoing the AML role’s emphasis on stakeholder partnership for building robust reporting systems.
- **Decreased manual configuration errors by 80%** by
automating service discovery and dynamic config
updates, aligning with AML goals of minimizing
operational risk and improving data integrity.
- **Enhanced CI pipeline reproducibility and
performance** by rewriting the Jenkins nightly
pipeline to support automated PR-level testing with
reusable parameters, improving report consistency
across environments.
- **Collaborated cross-functionally** with developers
and testers to maintain reliable infrastructure,
echoing the AML role's emphasis on stakeholder
partnership for building robust reporting systems.
- **Improved system reliability** during production
launches by implementing startup probes for legacy
services, reducing downtime and enhancing stability
for automated monitoring/reporting pipelines.
- **Reduced reporting deployment time by 66%** by
building a CLI-based solution to push compiled
binaries directly into Kubernetes/Openshift clusters,
