Financial Analyst - FP&A & Credit Risk Analytics

Ushaswini Punna

*********@**********.*** +1-716-***-**** LinkedIn

SUMMARY

Financial Analyst with 4+ years of experience specializing in FP&A, financial modeling, forecasting, and risk analysis across large-scale banking and lending portfolios. Expertise in building ROI, CAC, LTV, and driver-based financial models, integrating P&L, balance sheet, and cash flow forecasting to support strategic decision-making and capital allocation. Advanced skills in Python, SQL, Excel, Power BI, and Anaplan, with experience automating financial reporting, building dashboards, and processing large financial datasets using cloud platforms (Snowflake, BigQuery, AWS). Strong experience in credit risk modeling, scenario analysis, and delivering data-driven insights that improve profitability, forecasting accuracy, and operational efficiency. SKILLS

Financial Planning & Analysis (FP&A): Driver-Based Forecasting, Budgeting & AOP Planning, Rolling Forecasts, Variance Analysis (Actual vs Budget vs Forecast), Financial Modeling (3-Statement Models), Revenue & Profitability Analysis, Scenario & Sensitivity Analysis, Capital Allocation, Cohort & Unit Economics (CAC, LTV), KPI Development, Executive Financial Reporting Data Analytics & Programming: SQL (Advanced Queries, Joins, Window Functions), Python (Pandas, NumPy, Scikit-learn), Advanced Excel (Power Query, Pivot Tables, Power Pivot, Macros), Data Cleaning & Transformation, Statistical & Predictive Analytics Financial Systems & Tools: Anaplan (FP&A Planning), SAP FICO, Oracle Financials, Bloomberg Terminal, ERP Systems Integration Business Intelligence & Reporting: Power BI (DAX, Data Modeling), Tableau, KPI Dashboards, Financial Reporting Automation, Executive Dashboards, Data Platforms & Cloud Analytics: Snowflake, BigQuery, PostgreSQL, MS SQL Server, Data Warehousing, ETL Pipelines, Cloud-Based Financial Reporting (AWS, Databricks)

Risk & Financial Analytics: Monte Carlo Simulation, Portfolio Optimization, Credit Risk Modeling (PD/LGD), Stress Testing, DCF, NPV, IRR, Business & Collaboration Skills: Cross-Functional Business Partnering, Stakeholder Management, Budget Ownership Support, Strategic Decision Support, Agile (Scrum/Kanban), Requirements Gathering PROFESSIONAL EXPERIENCE

Capital One Jan 2025– Present New York

Financial Analyst

Orchestrated end-to-end financial investment evaluation models for digital banking products, integrating P&L and cash flow projections to prioritize high- ROI initiatives, delivering 18% ROI improvement and $25M+ incremental annual value

Engineered customer unit economics frameworks (CAC, LTV, retention economics) using structured datasets in SQL and BigQuery, optimizing marketing spend allocation and improving acquisition efficiency by 22%

Visualized executive-level KPI reporting systems in Power BI, consolidating financial and behavioral metrics to track profitability, retention, and revenue per user, reducing reporting cycle time by 40%

Quantified digital vs branch profitability trade-offs through cost allocation and FP&A modeling, identifying $120M+ optimization opportunities and enabling strategic branch rationalization

Interpreted income statement and balance sheet dynamics across digital banking revenue streams, linking interest income, fees, and operating costs to improve Net Interest Margin by 9%

Synthesized large-scale customer transaction datasets using Python and SQL to identify behavioral patterns, improving retention rates by 12% through targeted financial segmentation strategies

Unified enterprise financial data pipelines across AWS and Databricks environments to streamline forecasting workflows and accelerate executive reporting cycles by 45%

Coordinated rolling forecasts and annual planning cycles in Anaplan across product and finance teams, improving forecast accuracy by 15% and strengthening capital allocation decisions

Goldman Sachs Jan 2021–Jan 2024 India

Financial Analyst

Structured consumer lending forecast frameworks integrating revenue, loan growth, and credit performance drivers, improving forecasting precision by 20% in alignment with regulatory reporting standards and strategic business planning requirements

Streamlined financial data aggregation processes from Snowflake and BigQuery using SQL automation, processing 10M+ records and reducing manual reporting workload by 40% through scalable cloud-based workflows

Modeled credit risk exposure using Monte Carlo simulations and macroeconomic stress scenarios, enhancing portfolio resilience and reducing credit losses by 12% under adverse market conditions analysis

Evaluated portfolio performance through variance analysis across actual vs forecast vs budget, identifying key revenue leakage points and improving decision-making speed by 30% for executive-level financial insights

Simulated interest rate sensitivity scenarios using Excel and Python, enabling leadership to evaluate revenue impact under 100bps rate fluctuations across multiple macroeconomic cycles

Consolidated enterprise forecasting systems with SAP and Oracle Financials to ensure alignment between financial planning cycles and business units, improving forecast reliability by 15% and cross-functional reporting consistency

Clustered customer segments using SQL and Python to distinguish high-value and high-risk cohorts, enabling targeted lending strategies and increasing risk-adjusted returns by 10% through advanced behavioral analytics

Reconciled financial planning outputs with ERP systems (SAP FICO, Oracle), supporting month-end close processes and improving reporting consistency under GAAP/IFRS standards and audit compliance requirements PROJECTS

Portfolio Risk & Investment Optimization Model

Developed a portfolio optimization model using Modern Portfolio Theory, applying Monte Carlo simulations to analyze return distributions, construct the efficient frontier, and improve risk-adjusted returns through optimal asset allocation using Python and Excel.

Evaluated portfolio performance under varying market conditions by benchmarking optimized allocations against equal-weight strategies, improving risk- adjusted return insights and supporting data-driven investment decision-making using Python and Excel. Corporate Budgeting & Variance Analysis Model

Built a driver-based budgeting and forecasting model using a full 3-statement financial framework, enabling variance analysis, rolling forecasts, and expense tracking to improve financial planning accuracy and support data-driven decision-making using Advanced Excel, Python, and SQL.

Analyzed budget vs actual financial performance across revenue and expense drivers to identify key variances, improving cost control visibility and enhancing strategic planning accuracy through structured financial reporting using Excel, Python, and SQL. EDUCATION

Master of Science in Data Science University at Buffalo – SUNY Buffalo, New York