SHIVPRASAD KANNADE

• Conducted data mining and transaction analysis to detect fraudulent activities, reducing financial risk by 30%.
• Developed real-time fraud detection dashboards in Power BI, cutting response time by 25%.
• Automated SQL-based compliance reporting, reducing manual effort and report generation time by 40%.
• Analyzed payment processing failures, identifying patterns that optimized transaction success rates by 20%.
• Provided KPI-driven insights to cross-functional teams, improving banking security strategies.

Project: Banking Analytics & Fraud Detection

• Objective: Enhance fraud detection and improve transaction security in the banking sector.
• Tools Used: SQL, Power BI, Excel, Python.
• Approach: Extracted and analyzed millions of transactional records using SQL queries to detect fraudulent patterns.Built and integrated real-time fraud risk alerts within a Power BI dashboard for continuous monitoring. Developed fraud detection models using customer spending behavior to improve fraud prevention accuracy. Automated fraud reporting using SQL procedures and Excel macros to streamline compliance processes.
• Impact: Reduced fraud-related financial losses by 30%.Improved fraud detection speed by 25% through real-time dashboards. Increased compliance efficiency by 40% with automated reporting.

• Analyzed student engagement metrics to optimize learning strategies, boosting trial conversions by 8% and increasing ARPU by 12%.
• Designed real-time Power BI dashboards for sales performance tracking, enabling quicker, data-driven decisions.
• Built SQL-based lead scoring models, enhancing lead prioritization and increasing conversion rates by 15%.
• Automated revenue KPI tracking, reducing report retrieval time by 50%.

Project 1: Lead Scoring & Sales Optimization

• Objective: Improve lead conversion by prioritizing high-potential leads using data-driven insights.
• Tools Used: SQL, Power BI, Python (Pandas, Seaborn), Excel.
• Approach: Collected and segmented customer data from CRM systems.Developed an automated lead scoring model using SQL, ranking leads based on engagement and demographics. Integrated lead scores into a Power BI dashboard for real-time sales tracking, enabling optimized sales efforts.
• Impact: 15% increase in conversion rates by prioritizing high-value leads. 12% increase in ARPU and 20% improvement in sales team efficiency.

Project 2: Market Trend & Customer Retention Analysis

• Objective: Reduce churn and improve retention with personalized engagement strategies.
• Tools Used: SQL, Python (Pandas, NumPy), Power BI.
• Approach: Extracted and cleaned historical sales and engagement data.Conducted churn analysis and retention modeling to identify at-risk customers. Collaborated with teams to implement a predictive churn model, offering personalized retention strategies. Developed a Power BI dashboard for real-time monitoring of customer lifecycle.
• Impact: Reduced churn by 12% through targeted retention strategies.Increased CLTV by 15% and improved CSAT by 10%.