Abhishek Maskar

– Engineered a 7-page Power BI dashboard across 2 fact tables, analysing 16+ loan attributes including loan amount, interest rate, revolving balance, annual income, and grade/sub-grade segmentation across multiple US states. – Designed 3 custom DAX measures to track fully paid, charged-off, and current loan KPIs; implemented year-over-year drill-through via date hierarchy, reducing ad-hoc reporting effort by eliminating manual Excel aggregation. – Segmented data across 5 dimensions using 18 visual types; identified charge-off rates 34% higher among unverified lower-grade borrowers, directly informing a recommended verification threshold policy for Grade D-G applicants.

– Built a dual-page Power BI dashboard by joining cc_detail and cust_detail tables via SQL, surfacing revenue, transaction volume, and interest earned across quarterly and weekly time dimensions. – Profiled 10+ customer attributes (income group, age group, job type, satisfaction score) to map spending patterns across card categories, enabling targeted segmentation for marketing campaigns. – Identified high-income chip users as the top revenue-generating segment (contributing 45%+ of total revenue) and flagged declining low-income transaction volume as an early churn signal, recommending a retention offer strategy for the high-value cohort.

– Designed a 3-page Tableau dashboard analysing 9,994 rows of transactional data across 4 years, 3 product categories, 17 sub-categories, and 49 US states to monitor multi-region retail performance. – Built KPI cards (Total Sales, Profit, Order Count) with MoM delta indicators, monthly trend lines, a regional bar chart, and a state-level filled map cutting report generation time for stakeholders from hours to seconds. – Determined Technology as the highest-margin category (+17% avg. profit margin) and Furniture as consistently loss-making; recommended SKU rationalisation and shelf reallocation to improve Q4 profitability.