Commercial Credit Modeler – User Guide and FAQ
The Commercial Credit Model (CCM) is designed to provide you with forward looking, reliable credit default probability data. The default probability data are easy to purchase through a 'grab and go' program. Importantly, the data are very easy to implement in developing a credit risk/loss estimate model. In fact, we provide a free Excel table which is used as the foundation for creating loss estimates. This table enables you to segment your portfolio into classes to which you assign loss probabilities. And each step of the CCM program guides you to develop your loss estimates in an easy and intuitive manner. Cumulatively, the loss probability model you'll build with the CCM can become the basis for your compliance with CECL standards.
- Excel table - segments an A/R portfolio into 'risk buckets'. These risk buckets are broken out by industry sectors, states, company size and company age.
- Credit Index – provides current credit conditions trend data, within the range of an indexing system. This enables you to quickly determine whether credit conditions are improving, unchanged, or deteriorating, compared to the previous month. Queries are performed by geographic region and industry sector.
- The CCM – provides and default probability of businesses within specific industry sectors and states, and can be additionally filtered by company size and company age.
Commercial Expected Credit Loss (CECL) standards are an excellent concept. The guidelines will enable businesses to implement sensible and standardized policies that should reduce future loan losses. However, when attempting to generate a loss estimate for Small and Mid-sized Businesses (SMB) a key component is missing – data. There is a glaring absence of loss probability data relating to SMBs. Simply ‘adjusting’ last years’ losses can’t possibly be reliable in an environment of historically rapid interest rate hikes, and a slowing economy. This deficiency of data is an enormous obstacle to implementing CECL policies. One simply cannot construct a loss estimate without credible inputs.
The default probability data provided by the CCM fills this void. The CCM provides specific default probability estimates for industry sectors, geographic regions, and company size and age. These values are the FOUNDATION of your expected loss probability model.
With the default probability data provided by the CCM, risk is quantified. Informed decisions can then be made. Policies can be implemented, based on facts, not guesstimates.
- Implementation of a structure for you’re A/R portfolio is necessary to get the process underway. This is why we provide the free Excel template which is a very straightforward framework. What this framework allows you to do is divide you’re A/R portfolio into classes which carry unique risk characteristics. The main stratifying criteria are: A - geographic region, B – industry sector, and C – company size.
- An important aspect of this process of creating a CECL model is understanding the rationale for dividing you’re A/R portfolio by the aforementioned criteria of: states, industries, company sizes. Default probabilities vary significantly by these criteria. When you have reviewed the significant variance in default probabilities by the across classes, that is the point at which the value of the exercise is completely evident. See below.
- Run analyses with the CCM. Pull the data you need to represent the regions and industry sectors in which you have A/R exposure.
- Enter the values you obtained from the CCM and enter them into the Loss Weighting Module. Then add you’re A/R values to the Loss Weighting Module. Hit the Submit button and you’ll receive a worksheet that is the foundation of a CECL compliant model.
- Additional applications of the CCM data
- Collections policy
- Business planning
- Sales guidance
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Our CCM analyzes the changes in economic conditions which have material effects on the liquidity ratios and solvency of businesses. These factors include inflation, interest rates, labor force productivity, expenditures by consumers for discretionary and non-discretionary goods, capital expenditures by businesses, and other factors which relate to economic activity, and ultimately, credit conditions.Our methods do not rely on single entity records or filings. Our methods are based on the aggregation of 'similarly situated' businesses (industry sector, geographic region and company size) into classes, to which default probability values are assigned.
Our sources of data include the BLS, DOL, BEA, NY Fed, US Dept of Treasury, and NCES, to create predictive default probabilities which represent commercial sectors of the economy. Through the analysis of data which are correlated with changes in specific liquidity metrics of businesses, we estimate the probability of default of businesses within the next 12 months.
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