Sweta Kumari

Experienced Business/Data Analyst with 10 years of expertise in the banking and financial services industry. Proven ability to extract valuable insights from complex datasets using SQL, Excel, Tableau, Power BI, and Python. Skilled in data modeling, KPI dashboards, and automated reporting, consistently delivering actionable results. Deep understanding of retail banking, loan lifecycle management, and regulatory reporting. Applied AI/ML techniques to tackle real-world business challenges such as fraud detection, churn prediction, and lead scoring. Recognized for effective collaboration with cross-functional teams, influencing stakeholders, and driving strategic data initiatives.

• Credit Card Fraud Detection, Built classification models (Logistic Regression, Random Forest); achieved 96% fraud detection accuracy. Used SMOTE and under-sampling to balance data and reduce false positives.
• Customer Churn Analysis, Identified churn drivers using behavioral data; applied Decision Trees and XGBoost. Delivered insights that influenced customer retention strategy.
• Lead Score Analysis, Developed a Logistic Regression model to prioritize high-conversion leads, improving outreach and ROI.
• Car Insurance Claim Prediction, Applied SVM and Decision Trees to predict claim probability, aiding underwriting decisions.
• Flight Price Prediction, Created regression models with scikit-learn for dynamic pricing simulation on travel platforms.

MACHINE LEARNING WITH SPECIALIZATION IN NLP

Financial Institutions TelemarketingFinancial Institutions Telemarketing

• Identification of the customers, who are most likely to convert beforehand so that they
  can be specifically targeted via call. The business goal is to identify whether a customer will subscribe for a Term Deposit.

Credit Card Fraud Detection

• Built classification models (Logistic Regression, Random Forest); achieved 96% fraud detection accuracy.
• Used SMOTE and under-sampling to balance data and reduce false positives.

Customer Churn Analysis

• Identified churn drivers using behavioral data; applied Decision Trees and XGBoost.
• Delivered insights that influenced customer retention strategy.

Lead Score Analysis

• Developed a Logistic Regression model to prioritize high-conversion leads, improving outreach and ROI.

Car Insurance Claim Prediction

• Applied SVM and Decision Trees to predict claim probability, aiding underwriting decisions.

Flight Price Prediction

• Created regression models with scikit-learn for dynamic pricing simulation on travel platforms.