India Analytics lead for Customer Identity & Authentication - Consumer & Community Banking (CCB)• Lead for CI&A, Secure Insights, and Fraud Diagnostics in India, focusing on deep-dive analytical work and fraud mitigation strategies for the Consumer and Small Business Retail Banking division• Serving multiple products including Customer Onboarding, Interface access (both digital and non-digital), and secure transactions, implementing authentication step-ups and voice biometric… Show more India Analytics lead for Customer Identity & Authentication - Consumer & Community Banking (CCB)• Lead for CI&A, Secure Insights, and Fraud Diagnostics in India, focusing on deep-dive analytical work and fraud mitigation strategies for the Consumer and Small Business Retail Banking division• Serving multiple products including Customer Onboarding, Interface access (both digital and non-digital), and secure transactions, implementing authentication step-ups and voice biometric enrolment for customer verification.• Collaborate with the Risk team to conduct fraud diagnostics for significant loss cases, particularly from an authentication perspective, while building and implementing fraud risk strategies using Actimize and other tools.• Designing OKRs and other key metrics to measure performance of the product like Successful authentication & verification rate, Channel containment rate, Customer Complaint rate etc. Show less

• Leading a team of data scientists & analysts in Deposit Fraud Detection & Prevention Strategy domain for a leading US bank. Team is responsible for creating, testing & implementing fraud prevention rules to generate real-time alerts for suspicious transactions, saving ~$3M of potential loss monthly for deposits.• Identification & deep dive investigation of fraud scenarios by in-depth data analysis of transaction data & customer behaviour to identify potential fraud… Show more • Leading a team of data scientists & analysts in Deposit Fraud Detection & Prevention Strategy domain for a leading US bank. Team is responsible for creating, testing & implementing fraud prevention rules to generate real-time alerts for suspicious transactions, saving ~$3M of potential loss monthly for deposits.• Identification & deep dive investigation of fraud scenarios by in-depth data analysis of transaction data & customer behaviour to identify potential fraud risks.• Collaborated with fraud investigators and risk management teams to identify emerging fraud trends and patterns. Built real-time alert dashboards to monitor suspicious check deposit transactions & report to the concerned team.• Spearheaded process optimization initiative which included time & memory optimization of complex queries by eliminating redundancy, removing bottlenecks, memory management, code refactoring, root cause analysis etc. ~70% time reduction & ~40% read memory reduction achieved.• Proficient in deploying production-grade data ingestion and transformation pipelines, adhering to Software Development Life Cycle (SDLC) principles. Orchestrated end-to-end data pipeline development from diverse sources.• Leveraged scalable infrastructure and big data technologies, such as Hadoop, Spark, and distributed computing frameworks, to process and analyse massive volumes of data efficiently. Show less

• Led a team of 10 analyst & data scientists for a project in a niche Cyber Security, Identity Risk Management & Physical Security space for one of the largest banks of US. • Developed Identity Risk Management suite to monitor roles based employee accesses, identity risk-scores for employees & contractors. Resulted in removal of ~73k idle entitlements which provided access to sensitive PII/Non-PII data.• Built extensive monitoring support for Attack Surface Management in the form of… Show more • Led a team of 10 analyst & data scientists for a project in a niche Cyber Security, Identity Risk Management & Physical Security space for one of the largest banks of US. • Developed Identity Risk Management suite to monitor roles based employee accesses, identity risk-scores for employees & contractors. Resulted in removal of ~73k idle entitlements which provided access to sensitive PII/Non-PII data.• Built extensive monitoring support for Attack Surface Management in the form of live dashboards showcasing KPIs around server scan health, % of open vulnerabilities, vulnerability severity, authenticated vs non authenticated assets etc.• Guided team in building a Random Forest regression classification model to predict the severity of vulnerabilities for internal & external servers in the bank Show less

- Developed a Random forest regression classification model to predict the severity of associated vulnerabilities for internal and external servers- End to end data pipeline creation right from data sourcing from conventional and unconventional data sources like email, API call etc. and ingestion into Hadoop using PySpark- Created multiple reports and dashboards for the business to show various KPIs around server scan health, % of open vulnerabilities, plugin review etc.

- Lead a four member team in which the major work involved in coding the credit acquisition strategies for Auto and Personal loans. Over-hauled the old strategies to build a more optimized and user-friendly interface in GDS- Converted a POC into a full-fledged project with a team size of 15 members for one of the US largest financial services organisation (~$4M incremental revenue)- Objective was to find unique high confidence matches between an in-house data with a standard third party… Show more - Lead a four member team in which the major work involved in coding the credit acquisition strategies for Auto and Personal loans. Over-hauled the old strategies to build a more optimized and user-friendly interface in GDS- Converted a POC into a full-fledged project with a team size of 15 members for one of the US largest financial services organisation (~$4M incremental revenue)- Objective was to find unique high confidence matches between an in-house data with a standard third party data for hotel chains which would eventually enable peer-level detailed analysis, benchmarking, performance and opportunity identification at merchant level. Algorithm included matches based on – Lat./Long. , Street address, Zip, City, State etc. Minimum edit distance parameters included – Damerau Levenshtein, Jaro-Winkler, n-grams etc. NLP techniques like stemming, lemmatization, TF-IDF etc Show less

- Currently leading a team of two senior analysts and two analysts for the US largest registered agent services company- Basic work involves customer segmentation into corporates, small businesses, law firms etc. based on a complex algorithm- Solved a key business issue in customer tagging of funds & alternative investment companies using web scraping (BeautifulSoup) technique in Python which lead to ~20% improvement in accuracy- Automated an entire manual search process on D&B… Show more - Currently leading a team of two senior analysts and two analysts for the US largest registered agent services company- Basic work involves customer segmentation into corporates, small businesses, law firms etc. based on a complex algorithm- Solved a key business issue in customer tagging of funds & alternative investment companies using web scraping (BeautifulSoup) technique in Python which lead to ~20% improvement in accuracy- Automated an entire manual search process on D&B Hoovers using Selenium web driver in Python. Decreased the turn-around time by 2 days. Highly appreciated by the client. Show less

- Was part of the Decision support & pricing (DS&P) team for big corporations for the US largest registered agent services company- Built a pricing tool model using linear regression to calculate the customer level price for a subscription based bundled product- Lead the quarterly surgical price increment for the client’s flagship product. Avg. annual incremental revenue of ~$1.5MM achieved- Review the performance of in-bound and out-bound sales team. Attributes like customer… Show more - Was part of the Decision support & pricing (DS&P) team for big corporations for the US largest registered agent services company- Built a pricing tool model using linear regression to calculate the customer level price for a subscription based bundled product- Lead the quarterly surgical price increment for the client’s flagship product. Avg. annual incremental revenue of ~$1.5MM achieved- Review the performance of in-bound and out-bound sales team. Attributes like customer geography, industry, customer size, product penetration etc. were considered at sales person level Show less

- Worked on to make a comprehensive report detailing about the condition of various channels of revenue. Channels included- Law firm, passive, service, online further divided into phone & web based- Identified key areas of loss of new revenue (i.e. revenue coming through new customers). In consequence to which the business ran various campaigns which in-turn lead to ~$1.2MM annual incremental revenue