Samit S Email & Phone Number

Wilmington, Delaware, United States

• Work with senior leaders, business units, and partners to influence business and technology roadmaps
• Set up AI/ML function for First Party Fraud detection. POC for portfolio-level credit abuse ML model
• Developed 3000 model features using python and hive for credit abuse Light GBM model
• Apply encoding to categorical features to develop Neural Network model for credit card transaction fraud
• Developed and implemented NLP models using deep learning techniques such as LSTM and Transformer to perform sentiment analysis on customer reviews, resulting in improved product development and customer satisfaction.
• Developed and implemented linear and logistic regression models to get la customer churn for a telecom company, resulting in a 20% reduction in churn rate and increased revenue.

Wilmington, Delaware, United States

• Lead a team of 6 in design, development, and production implementation of machine learning models to mitigate: Portfolio Credit Abuse Risk, Credit Card Return Payment Fraud Risk and Acquisition Fraud Risk. Expected.
• Credit Abuse Fraud:Developed model to detect fraudulent customers post acquisition for Credit CardsDeveloped and implemented Credit Abuse Fraud model leveraging distributed Xgboost package in in SCALAUsed a open source.
• Credit Card Return Payment Fraud:Developed Return Payment Fraud machine learning model to detect high risky credit card paymentsModel implemented in production is used to place hold on high-risk payments and prevent.

Gurugram, Haryana, India

• Built logistic regression/random forest models in SAS and R to predict fraud claims in worker’s compensation insurance
• Generated model hints/reason codes to help insurance analyst to evaluate claims
• Applied NLP using R programming to extract themes from eight million unstructured call center texts generated from inbound and outbound call notes
• Categorized calls based on call themes/Topic Identification using clustering algorithms, including hierarchical and K-Means algorithms