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A Holistic Approach to Counterparty Credit Risk Management


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In partnership with Treasury & Risk Magazine, this webinar helps you learn how you can adopt counterparty and credit risk management best practices. Identify credit risk challenges and the importance of data quality. Determine credit worthiness and implementing limits management. Apply early warning techniques and setting monitoring triggers.
Best practices for credit risk management for both public and private firms

Published in: Economy & Finance

A Holistic Approach to Counterparty Credit Risk Management

  1. 1. A Holistic Approach to Counterparty Credit Risk Management November 2014
  2. 2. Speakers » Mehna Raissi is a Director in Product Management in the Enterprise Risk Solutions group with Moody’s Analytics and has been with the firm for nearly six years. She manages the single obligor credit risk products suite which include RiskCalc™, CMM ™ (Commercial Mortgage Metrics) and LossCalc™. Mehna is responsible for the management and product innovation of these premier credit risk management tools. Mehna completed her Bachelors in Managerial Economics from University of California, Davis, and her MBA from University of San Francisco. » Cristina Pieretti is a Senior Director in the Content group at Moody's Analytics based in New York and has been with the firm for six years. In her current role, she is responsible for the product management of CreditEdge ®. Prior to joining Moody’s Analytics, Cristina spent more than ten years in banking where she participated in multiple transactions in Latin America. Cristina holds an engineering degree and an MBA. She is also a CFA Charterholder.
  3. 3. Agenda Topics  Identifying credit risk challenges  Determining credit worthiness and implementing limits management  Applying early warning techniques and setting monitoring triggers
  4. 4. Importance of Credit Risk Management and Challenges
  5. 5. Counterparty Risk Trading Risk Buyer Risk Vendor Risk Risk-based Pricing Benchmark Limit Setting What and where are the risks? Bad Debt Miscalculation of capital reserves Disruption to supply chain Unforeseen
  6. 6. Challenges in C&I Risk Management Data Quality & Availability What is the data quality? Standardized Processes Ongoing Monitoring Other Risk Drivers Credit Risk Models • Limited up to date data and ongoing availability • Data captured at origination may not be complete for ongoing data analysis • Data management is important for historical and forward looking analysis • Storing data in a single system of record for consistency • Improving operational controls by standardizing credit policies • Setting up workflow processes to ensure systematic loan origination processes • Improve credit origination decisions with accurate and predictive risk models • Leveraging risk models for capital allocation and reserve setting • Stress testing models that leverages baseline borrower risk • Early warning indictor of risk deteriorations • Dashboard reports showing borrower risk migration • Setting limits based on risk levels • Understand unexpected shifts that provide additional transparency • Incorporate qualitative factors for a comprehensive analysis How to minimize errors? What are the most effective credit risk tools? How to manage counter-party risk? What other factors should be taken into consideration?
  7. 7. Determining Creditworthiness
  8. 8. How to address your credit and counterparty risk Evaluate potential customer Set credit limits and terms Monitor exposures Determine credit score Perform sector analysis Your process… Choose counterparties with credit quality Your objectives… Accurate and consistent pricing of credit risk Focus on riskiest exposures Avoid overexposure to a sector Early warning Your requirements… High quality data Industry peer insight Standardized/ consistent process Accurate models Transparent scoring Effective monitoring system
  9. 9. Interpreting risk diagnostics that drive business decisions » 1-Year and 5-Year EDF™ (Expected Default Frequency) credit measures and Implied Ratings » Percentiles show the proportion of statements in the development sample that have lower (better) EDFs » Mappings to Organizational Ratings » Term structure outputs over five years providing short-term and long-term views
  10. 10. Relative Contributions provides risk driver insight 10 Ratio drivers point out many weaknesses firms financials
  11. 11. Compare a borrower against a peer group for additional transparency
  12. 12. Incorporating qualitative factors in your credit assessment for a comprehensive view Qualitative factors focused on industry/market (customer power), management (experience in industry), company (years in relationship) and balance sheet factors (audit method)
  13. 13. Setting limits that help manage business goals » Pre-qualification module to streamline business operations » Setting limits to manage concentration goals by Industry » Risk based pricing to ensure systematic framework for tying risk to interest rates Zero Limits Low Limits Medium Limits High Limits 0.02% 35.00% 0.50% 10.00% 2.00% 5.00% 1.00% 0.20% EDF 0.05% 0.10% Exposure
  14. 14. Early warning techniques and monitoring triggers
  15. 15. Can we detect potential defaulters early enough? One-Year Expected Default Frequency (EDF™) Measures Best practices - Taking a closer look at monitoring credit risk and early warning
  16. 16. Default probabilities are ideal metrics for early warning Expected Default Frequency (EDF) measures have the advantages of being: » Point-in-time » High frequency » Granular » Unbiased » Global coverage
  17. 17. Best practices - Monitoring credit risk and early warning EDF Level EDF Change Relative EDF Level EDF Relative Change Monitoring & Early Warning Toolkit
  18. 18. Companies that underperform their industry sectors historically experience much higher default risk Historical Default Rates for Firms Whose EDFs Underperform their Sectors are Significantly Higher
  19. 19. Negative EDF momentum signals higher default risk One-year default rates conditioned on EDF momentum
  20. 20. Default rates are sensitive to EDF momentum vs. sector One-year default rates conditioned on EDF decile and EDF change vs. sector change 1 2 3 4 5 6 7 8 9 10 ALL 1 0.05% 0.03% 0.02% 0.00% 0.00% 0.01% 0.03% 0.00% 0.00% 0.00% 0.02% 2 0.10% 0.05% 0.06% 0.06% 0.00% 0.00% 0.02% 0.07% 0.11% 0.27% 0.05% 3 0.10% 0.06% 0.01% 0.03% 0.01% 0.03% 0.07% 0.06% 0.03% 0.18% 0.05% 4 0.28% 0.12% 0.17% 0.15% 0.09% 0.10% 0.08% 0.09% 0.17% 0.30% 0.15% 5 0.32% 0.23% 0.24% 0.32% 0.22% 0.24% 0.21% 0.27% 0.22% 0.46% 0.27% 6 0.62% 0.44% 0.45% 0.34% 0.44% 0.56% 0.44% 0.72% 0.51% 0.97% 0.55% 7 0.71% 0.56% 0.66% 0.80% 0.64% 0.72% 0.73% 1.06% 1.18% 1.63% 0.89% 8 1.01% 1.01% 1.19% 1.25% 1.27% 1.44% 1.58% 1.65% 2.05% 3.10% 1.68% 9 3.14% 2.22% 4.83% 5.16% 5.25% 4.34% 4.87% 5.75% 6.37% 8.39% 5.60% 10 6.43% 4.68% 5.76% 7.70% 7.70% 6.96% 7.67% 9.31% 9.99% 13.70% 8.94% All 0.66% 0.63% 1.08% 1.73% 1.73% 1.83% 2.24% 2.92% 3.13% 5.96% 2.16% FirmEDFLevel EDF Change Relative to Industry Peer Group Change Default risk increases with poor performance vs. industry Default risk rises with EDF level
  21. 21. Monitoring the EDF Level Sears Holdings Corp.’s EDF measure has signaled heightened risk of default over the past year Sears Holdings’ One-Year Expected Default Probability
  22. 22. Relative EDF level Sears Holdings Corp.’s default probability is among the highest in its industry sector One-Year Expected Default Probability for Sears Holdings and its Industry Sector 90th Percentile
  23. 23. Relative EDF level Sears Holdings Corp.’s default probability is among the highest in its industry sector One-Year Expected Default Probability for Sears Holdings and its Individual Peers
  24. 24. Set Alerts to Monitor PD Level, PD Change and Relative Performance
  25. 25. Best Practices - Risk Monitoring Template EDF level EDF change Current Percentile Rank Momentum
  26. 26. Putting It All Together
  27. 27. There are multiple challenges and risks associated with credit risk monitoring Your Challenges » Multiple counterparties » Non-standardized credit risk assessment/monitoring processes » Inaccurate models » Limited industry/peer insight » Lack of early warning or effective risk monitoring system » Loss of income or liquidity, bad debt » Disruption of distribution/supply chain » Potential bankruptcy » Miscalculation of capital reserves » Unforeseen Damages Your Risks
  28. 28. An Effective Credit Risk Monitoring System can be leveraged in several ways Adherence to Accounting rules » Calculate amortization schedules and credit reserves » Estimate credit impairments (OTTI) for investment portfolios » Accurately and consistently price credit risk » Avoid overexposures to a single client, industry or region Risk-Based Pricing & Limit Tracking » Focus on riskiest exposures » Early detection of deterioration in the credit risk of a counterparty Credit Risk Monitoring & Early Warning Downstream & Upstream Credit Risk » Qualify new customers » Choose vendors and suppliers with high credit quality
  29. 29. Key Requirements for an impactful Credit Risk Monitoring Framework » Consistency » Efficiency » Transparency » Accuracy Risk Models Risk Analysis Peer Analysis Early Warning Monitoring Reporting
  30. 30. Monitoring and early warning playbook summary Our research suggests a general approach to effective early warning using EDF measures: EDF Level EDF Change Relative EDF Level EDF Relative Change Monitoring & Early Warning Toolkit
  31. 31. Questions?