Revathi Sampathkumar

Data scientist with 8 years in financial services and 4 years in fraud detection at PNC Bank's cybersecurity organization. I build rule-based alerts in Python and SparkSQL and ML models for alert triage, with recent focus on Positive-Unlabeled learning and class-imbalance methods for cases where confirmed fraud is rare. Comfortable owning work from problem framing through production code and analyst handoff.

Data Scientist - PNC Bank, Cybersecurity Organization - Columbus, OH

(2021-08)

Fraud detection team that catches internal-actor fraud across PNC's banking products. Work splits between production rule-based alerts and ML triage models. Stakeholders: cybersecurity, compliance, operations.

• Built a Bagging PU (Positive-Unlabeled) classifier to triage alerts where confirmed fraud is a small fraction of total alert volume for Same Day Withdrawal fraud model.
• Lifted F1 from 2 percent to 8 percent at 80 percent recall. Analysts now triage off model output rather than raw rule output, which has cut the time spent on false alerts.
• Consolidated six fraud rules (Same Day Withdrawals, Teller Reversals, Self Dealing, DDA Non-Employee to Employee, SVG Non-Employee to Employee, Teller Override) into one unified training set spanning 2021 to 2024 to prevent overfit.
• Engineered transaction-pair behavioral features: how often a specific customer and teller interact, same-branch visit patterns, employee tenure flags to cut false positives.
• Used frequency encoding for high-cardinality fields (Bank No, Branch No) since one-hot would have blown up the feature space.
• Applied time-based split for validation: trained on 2021 to 2024, validated on the next 7 months, tested on the 7 months after to avoid information leakage.
• Wrote the production pipeline for data ingestion, feature engineering, and alert scoring.
• Built Private Bank Address Change rule combining Jaro-Winkler and Levenshtein fuzzy matching with geospatial proximity scoring against 1,000-plus PNC branch locations.
• Built Self-Dealing Fee Refund rule catching employees processing fee refunds on their own accounts.
• Built Employee Phone Number Linking rule firing when an employee links the same phone number to multiple customer accounts.
• Built DDA Non-Employee to Employee rule (SparkSQL) flagging transfers moving from a regular customer checking account into an employee-owned account.
• Built EDGE Executive Snooping rule (SparkSQL) detecting employees pulling up executive accounts without a documented business reason.
• Built Demo Check Abuse rule (SparkSQL) triggering when an account receives more than five demo checks inside a 90-day rolling window.
• Built Quick Balance Anomaly rule catching employees skipping a full cash drawer count in favor of posting a lump-sum quick balance.
• Built Instant Card Order self-issuance rule flagging cases where the issuing employee is also listed as Cardholder Name or Authorized Signer.
• Built Tableau dashboards and emulation reports for production fraud rule monitoring.
• Set up the framework for recurring monthly fraud reporting.
• Pushed updates to demand-deposit account fraud rules into production along with matching reports.
• Designed and shipped the phone-number-change alert system from scratch including requirements, build, testing, and deployment.
• Rewrote fragile legacy query logic against Criss Index databases and Hadoop extract tables for self-dealing fee-refund investigations.
• Fixed broken production code on the debit card disputes pipeline after business-rule drift and reworked the logic.
• Served as stakeholder lead across cybersecurity, compliance, and operations with regular updates and on-time delivery.

Career Break

(2015-10 - 2021-08)

Career break to care for my first child during a period when my husband's role required frequent travel.

Data Analyst - BNP Paribas Bank

(2012-06 - 2015-10)

Data analysis and validation across the lending portfolio: Consumer Credit Card, Home Lending Servicing, Auto, and Personal Loan Originations. Worked closely with developers, BAs, and PMs.

• Led the Home Lending lead-creation project, which fed into the customer acquisition pipeline by translating business requirements into data analysis across multiple lending systems.
• Ran data quality checks and validation, catching consistency issues before they hit production reports.
• Validated mappings on imports and exports.
• Analyzed JSON and XML payloads and ran API analysis on REST/HTTP and SOAP to trace data flows.
• Used POSTMAN heavily for API validation.
• Worked with integration partners on process improvements and reliability fixes.

Applied Data Science Program - MIT Professional Education

Bachelor of Engineering - Anna University, India