NIHARKIA KATIPELLY

• Streamlined ED/LEA data requests through automation using Databricks, Python, and SQL, reduced the team's efforts by 70%.
• Performing impact assessment and RCA for the product changes.
• Identified opportunities for automation, which reduced bandwidth of stakeholders by up to 40%.
• Developed a comprehensive dashboard consolidating all metrics, saving 50% of the time previously spent on weekly and monthly reviews.
• Contributing to OKRs:

1. Enhancing shield rules, which led to a reduction of risk declines by 15%.

2. Revamping transaction monitoring alert criteria, reducing false positive alerts by 35%, and conserving operational bandwidth.

3. Resolved dashboard issues by creating Directed Acyclic Graphs (DAGs), and optimizing SQL queries.

• Comprehensive Overview Dashboard of Click Stream Data on the Client Platform.
• Executed an in-depth root cause analysis of anomalous transactional behavior across e-commerce, pharmacy, and airline sectors, identifying 15+ distinct patterns leading to a 35% reduction in fraudulent transactions within three months.
• Performing impact assessments for all the product changes and KPIs on a weekly and monthly basis, respectively.

• Initiated a detailed investigation into transactional data patterns associated with money laundering, resulting in the identification of over 150 potentially fraudulent accounts, and collaboration with regulatory teams strengthened anti-money laundering compliance.
• Designed and executed a comprehensive data cleansing framework that processed over 1M records, ensuring compliance with data quality standards; this framework is now adopted by 10+ teams for ongoing data management efforts.
• Conducted correlation analysis on transactional trends and criminal behavior across 15+ companies, identifying key patterns that informed strategic decision-making and enhanced risk assessment protocols for future operations.
• Developed and delivered over 15 comprehensive reports analyzing anti-money laundering data, which provided critical insights and facilitated informed decision-making on more than 35 cases of suspected financial misconduct.