Bad Debt Forecasting: Two Methods to Accurately Calculate Future Trade Credit Losses
Tuesday, January 22, 2013 from 10:00 AM to 11:30 AM (PST)
Palo Alto, CA
This webinar highlights the evolution in methodologies for forecasting trade credit bad debts. Many companies have relied on historical data or a flat percentage of sales to help forecast future credit losses. However, these methodologies don’t allow for changing markets, customer growth, or even increasing geographical diversity. A more accurate measure of future bad debt would examine your existing customer risk and determine what these risk elements tell you about the future possibility of credit losses.
Why Should You Attend:
With the increase in global credit risk, companies must fully understand the risk involved within their trade accounts receivable portfolios. Companies need to identify and utilize the existing risk factors within their portfolio to more accurately assess reserve needs and future write-off probabilities. In addition, they must learn from past bad debt expenses to forecast future credit losses.
This informative “how to” webinar highlights how companies can mine their existing customer data and outstanding balance information to create a dynamic forecasting model. We will study the evolution of forecasting bad debt models to show how the top companies in the world create simple, dynamic, accurate reserves no matter how their markets and customer base evolve over time. The webinar will detail the two methods that can be used for any company wishing to accurately calculate bad debt reserves for their accounts receivable portfolio. We will study these two methods in detail and use case studies to show how the methodology works in trade receivables portfolios worldwide.
At the end of this webinar, the speaker will handle your specific questions you may have regarding bad debt forcasting.
Areas Covered in the Webinar:
- What role a company’s historical bad debt expenses play in future losses?
- How your credit policy should help you forecast future credit losses?
- When to risk rate customer delinquency for your bad debt projections?
- How to measure risk of default on all existing customers?
- How to mine data from your own technology to help you more accurately forecast bad debt expense?
- Specific calculations of the reserves.
When & Where
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