ASHIKA M S

Project 2: Financial Crime Controls Framework (GIFSIT)

Financial Crime Controls Framework (GIFSIT) was designed to establish a comprehensive Data Assurance and Technology Control capability across Financial Crime systems to ensure compliance with APRA and AUSTRAC regulatory requirements. The framework acts as a governance and control layer across multiple Financial Crime obligations and mitigates data risks across the entire data lifecycle.

• Designed and implemented scalable batch and streaming data pipelines using PySpark on Databricks following Medallion Architecture (Bronze, Silver, Gold).
• Built Kafka-based real-time ingestion pipelines for high-volume financial crime transaction datasets.
• Developed ETL pipelines using Python and SQL for structured and semi-structured datasets stored in Amazon S3.
• Implemented automated data validation controls including row count reconciliation, checksum validation, duplicate detection, and job monitoring.
• Engineered data quality frameworks including null checks, range validation, pattern validation, and referential integrity constraints.
• Implemented statistical profiling and anomaly detection for monitoring data stability and distribution changes.
• Embedded preventative and detective technology controls across ingestion, transformation, and consumption layers.
• Orchestrated workflows using AWS services ensuring dependency management, retry mechanisms, and SLA compliance.
• Developed Power BI dashboards to monitor data quality KPIs, control metrics, and regulatory indicators.
• Optimized Spark jobs using partitioning strategies and performance tuning for large datasets.

Project 1: Financial Crime Transaction Monitoring System:

Worked on a Financial Crime Transaction Monitoring platform that detects suspicious activities such as Anti-Money Laundering (AML), fraud, and illegal financial transactions. The system applies SAS-based rules on high-volume banking transactions to generate alerts which are further analyzed and reported to AUSTRAC regulatory authority.

• Developed automation test scripts using Selenium WebDriver with Java and BDD Cucumber.
• Automated end-to-end testing of financial crime transaction monitoring workflows.
• Validated SAS rule-based alert generation logic for suspicious transaction detection.
• Performed database validation using SQL queries to verify transaction data and alert accuracy.
• Logged and tracked defects using JIRA while working in an Agile Scrum environment.

Verity : End-to-end Automation testing of Guidewire Claimcenter Application integration with Third-Party Application for Subrogation Claims.

• GW PC Upgrade: Testing of Guidewire Policycenter Application Upgrade to latest version.
• Led automation testing initiatives for Guidewire ClaimCenter enterprise applications.
• Developed Selenium automation frameworks using Java and BDD Cucumber.
• Performed Integration Testing, Regression Testing, Smoke Testing, and API Testing.
• Designed data-driven automation test cases and managed defect lifecycle using Jira.

EWS (Embedded Web Server) : Test Automation framework development for handling the execution of test requirements of a EWS page which can be accessed through a web browser.

• Developed Selenium WebDriver automation scripts for web-based applications.
• Performed functional testing, cross-browser testing, and compatibility testing.
• Built and maintained automation frameworks for regression test coverage.
• Executed automated test suites and tracked defects using Jira.