A Holistic Approach to Counterparty Credit Risk Management November 2014
Jul 15, 2015
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.
Agenda Topics
Identifying credit risk challenges
Determining credit worthiness and implementing limits management
Applying early warning techniques and setting monitoring triggers
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
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?
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
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
Relative Contributions provides risk driver insight
10
Ratio drivers point out many weaknesses firms financials
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)
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
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
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
Best practices - Monitoring credit risk and early warning
EDF Level
EDF Change
Relative EDF Level
EDF Relative Change
Monitoring & Early Warning
Toolkit
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
Negative EDF momentum signals higher default risk One-year default rates conditioned on EDF momentum
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 ALL1 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%
Firm
EDF
Leve
l
EDF Change Relative to Industry Peer Group Change
Default risk increases with poor performance vs. industry
Default risk rises with EDF level
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
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
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
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
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
Key Requirements for an impactful Credit Risk Monitoring Framework
» Consistency
» Efficiency
» Transparency
» Accuracy
Risk Models
Risk Analysis
Peer Analysis
Early Warning
Monitoring
Reporting
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