Aditya Kumar Sahu

Driven Data Analyst with over 1+ year of experience in the banking industry, specializing in data analysis, reporting, and visualization. Proficient in SAS, SQL, Python, Power BI, and Tableau. Seeking a challenging role to leverage technical skills and domain knowledge to enhance data-driven decision making, improve patient outcomes, and optimize operational efficiency

• Data Analysis
• MIS Reporting
• Data Model
• Insight Generation
• Risk Analysis

DOB:04/08/2002

GENDER: MALE

NATIONALITY: INDIA

• Developed a data model using SAS and SQL for comprehensive analysis of banking datasets, including customer transactions, credit risk, and portfolio performance. Created a decision master file to track loan application rates: received, booked, and cancelled applications.
• Preparing Loan Portfolio Reports using SAS Identified patterns and trends in customer behavior, assessed risk exposure, and provided actionable insights to optimize business strategies. Directed the creation of daily, weekly, and monthly reports through in-depth analysis, delivering actionable information for decision-making.
• Visualization: Presented analysis findings and recommendations to stakeholders, including senior management, using clear and concise visualizations and reports created with Power BI and Tableau.
• Generating Dashboards using Excel and Power BI Developed and maintained interactive dashboards and monthly MIS reports to monitor key performance indicators and track business metrics.
• Portfolio performance Analysis: Interpreting loan portfolio data to find the areas of improvement and presenting ways for increasing loan performance by developing different portfolio KPIs.
• Conduct ad-hoc analyses and data mining to address specific business questions or challenges using data science applications such as Excel, SQL, Tableau, Alteryx, and SAS.
• Risk Management: Examined the bank's loan portfolio to assess risk and recommend management strategies. Collected and analyzed loan data, developed risk models, and presented findings and recommendations to stakeholders.
• Collaborated with cross-functional teams to define data requirements, ensure data integrity, and implement data-driven solutions.
• Collection and Recovery analysis: Analyzing debt recovery data to identify areas for improvement and make increasing the recovery rate.