Pranay Krishna Navath

• Developed end-to-end credit loss forecasting models using R/Python to support capital stress testing initiatives.
• Conducted back-testing and model diagnostics to evaluate model accuracy and ongoing performance.
• Performed detailed model reviews and provided technical feedback to development teams prior to independent validation.
• Validated CVA models created by Front Office Quant teams and CCR models from the Counterparty Credit Analytics Group.
• Analyzed portfolio stress testing results based on portfolio composition, underlying shocks, and trend patterns.
• Assessed portfolio risk exposure and identified areas of excessive risk through quantitative analysis.
• Designed hypothetical and historical stress scenarios using concentration analysis techniques.
• Gained exposure to pricing, calibration, risk, and capital models across diverse financial products and derivatives.
• Strengthened quantitative programming capabilities and market/product knowledge through applied financial modeling.

• Collect and clean data from diverse sources to ensure accuracy and integrity.
• Analyze data using statistical methods and data mining techniques to uncover patterns and trends.
• Generate reports and deliver presentations to communicate analysis findings to stakeholders effectively.
• Create visualizations and graphs to present data in a visually compelling and understandable manner.
• Collaborate with team members and stakeholders to understand data requirements and objectives.
• Develop and implement data cleaning and transformation processes to prepare raw data for analysis.
• Use advanced analytics techniques, such as machine learning, to extract valuable insights from data.
• Monitor and track key performance indicators (KPIs) to evaluate the effectiveness of data analysis efforts.
• Stay updated with the latest trends and technologies in data analysis to continuously improve processes.
• Conduct regular audits to ensure data accuracy, completeness, and consistency.
• Work with IT teams to optimize data storage, retrieval, and processing capabilities

• Trained on Commercial Mortgage-Backed Securities (multifamily) properties and analyzed different kinds of properties including set of multi properties and gained knowledge in Vanilla Note, Variance Note, and Root cause Analysis.
• Performed analysis by identifying FYE using proper research by referring to Appraisal, Mortgage deed of trust, and other loan agreements. For the low-performing properties contacted Borrower/ Management companies to get information on the plans that are being implemented to improve its DSCR. Occasionally supported clients by performing analysis on an SOS basis.
• Trained and analyzed Commercial Properties and actively participated in providing OSARs for both Periodic and SOS types to CRM’S. Involved in Raising B. C’s regarding Property performance.
• Assisted with preparing and presenting the results of the analysis and I had a strong working relationship with our counterparts to accomplish our client's requirement.
• Quality Checks of CMBS, PGI Portfolio, Hospitality and HealthCare Properties
• Quality Feedbacks to analyst’s. Suggestions for process improvements If any new deals added, working on them and explaining to the team

• Directed financial analysis for the Global Budget & Forecast Cycle Overhaul, establishing standardized forecasting models and processes across global business units.

• Consolidated regional budget submissions into a unified corporate framework, ensuring consistency in assumptions and alignment with company strategy.

• Developed robust variance analysis methods that uncovered cost inefficiencies and revenue risks ahead of schedule.

• Enhanced forecast accuracy by 20% through dynamic Excel- and SAP-based models, reducing errors and increasing visibility.

• Partnered with cross-functional leaders to refine cost allocation practices, improving transparency of overhead and operational expenses.

• Streamlined reporting workflows by implementing standardized templates, cutting the monthly forecasting cycle by two weeks.

• Delivered executive-level presentations highlighting performance drivers, risks, and mitigation strategies for senior management.

• Ensured compliance with US GAAP and IFRS by embedding accounting standards directly into forecasting and reporting activities.

• Conducted scenario and sensitivity modelling on revenue growth and expense strategies, enabling leadership to make data-driven, forward-looking decisions.