Loss Forecasting and Stress Testing Analytics Intermediate Analyst

The individual should demonstrate strong work ethic, teamwork, quantitative and problem-solving skills. The individual is expected to leverage technical and business acumen to deliver high quality results. Responsibilities include but are not limited to understanding the key drivers of losses and loan loss reserves, their relative importance and the current trends; apply this knowledge effectively to forecast losses / loan loss reserves meaningfully and accurately; analyze underlying model outputs relative to other business, ensure that the models provide rational and logical output, Reconcile detailed financial data from disparate data sources, be able to present the findings to their manager and various key stake-holders; ensure best in class governance and documentation practices for these functions; drive process efficiencies through automation for the underlying data, forecasting and reporting processes.

Key Responsibilities:

• Work independently to effectively execute:

  • Quarterly loss / loan loss reserve forecasting and stress testing processes (CCAR, QMMF, Recovery Plan) deliverables for one or more retail portfolios with primary focus on NA cards

  • Associated governance activities (Manager Control Assessment, End User Computing, Activity Risk Control Monitoring and its Assessment Units)
  • Cross-portfolio and cross-functional collaboration on loss / loan loss reserve forecasting and stress testing analytics
• Assist in review and challenge of existing models, and model outputs to identify areas of improvement relative to portfolio & macro-economic trends.
• Understand the calculation of reserves, components of P&L, and the impact of CECL on CCAR results besides understanding the synergies between two processes.
• Collaborate with other teams like Risk Modeling, Portfolio & New Account Forecasting, Data Reporting and Finance to complete requests on financial planning & CCAR/DFAST results and increased integration of credit risk & PPNR results
• Perform complex risk policy analytics in terms of sizing the impact of credit/business/regulatory policies on loss performance and incorporate it into the stress testing process
• Perform econometric analysis to estimate and explain the impact of changing macroeconomic trends on Portfolio Performance Losses, delinquency etc.
• Establish and continually evolve standardized business and submission documentation.
• Collaborate with Risk and Finance organization to understand sources of data and continue to improve the process of defining, extracting and utilizing data.
• Identify areas of improvement in BAU and drive process efficiency through process simplification and automation (VBA, SAS, etc.)
• Execute information controls (version control, central results summary) to meet business objectives with utmost clarity.

Qualifications:

• 4+ years work experience in financial services, business analytics or management consulting.
• Understanding of risk management. Knowledge of credit card industry and key regulatory activities (CCAR) is a plus. Experience in CCAR / DFAST/Stress Testing is preferred
• Strong understanding and hands-on experience with econometric and empirical forecasting models. Experience in data science / machine learning is preferred with ability to handle large datasets
• Experience in using analytical packages like SAS, datacube/Essbase, MS Office (Excel, Powerpoint)
• Vision and ability to provide innovative solutions to core business practices.
• Ability to develop partnerships across multiple business and functional areas.
• Strong written and oral communication skills.

Education:

• Bachelor’s/University degree or equivalent experience

Leadership Competencies:

• Ability and experience to drive changes in order to achieve business targets
• Displays flexibility to work well with varying personal styles
• Understands and appreciates diverse backgrounds.
• Demonstrates strong ethics
• Develops strong cross-functional relationships within and outside Risk Management
• Contributes to a positive work environment; shares knowledge and supports diversity