Improving Portfolio Management with External Data

How do you rate the effectiveness of your current portfolio management practices?  Does your practice create actionable inputs for planning business strategy and for tactical account management?  Even if the answer is “yes” and you have fully optimized all of the analysis and reporting available from your system’s data, there is still room for improvement by adding external data.  Using external data opens new dimensions for analysis and reporting.  Beyond analysis, it will facilitate more active Portfolio Management practices and the potential for incremental financial impact across your business, particularly within the small business and middle-market segments.  When considering external sources, there are two distinct types of data:

Segment level: Data on industry or regional trends of origination, delinquency and default.  Segment level data provides a benchmark for your portfolio performance.  Users need to take into account the methodology and properties of the data to determine that any comparison to your portfolio or performance is “apples to apples”.  And typically, this data is subjective and is difficult to use consistently or in statistical analysis.

Account level: Data on some or, even better, all of your customers on a regular basis (e.g. monthly) that allows you to monitor your accounts.  As the lowest common denominator, account level data enables you to aggregate reporting on any available dimension (e.g. business unit, industry, etc.) while still allowing you to drill back down to the customer level.  Another major benefit is their independent and timely risk assessments which will help you further identify the outliers and migrations in your portfolio.

Each data type is a valuable addition to portfolio management and working together they have a powerful synergy.  The key to accessing that synergy begins with obtaining a discrete risk assessment for each customer. Given that discrete assessment, portfolio managers now have an independent and objective tool to rank customers by credit quality and identify trends and migration. The independent assessment becomes especially valuable when it represents a more recent and frequent assessment than your own.   For example, if you don’t annually review exposures below $250k, then an independent assessment received on a monthly or quarterly basis will dramatically improve your portfolio management.  When you combine the ranking, trend and migration shown by the external data with your existing portfolio management routine, you gain a powerful insight into the outliers of your portfolio. With these new dimensions, you can answer actionable questions, as yet unidentified by just internal data, for the portfolio by account:

• Opportunities: Which are improving? Carry less risk than previously perceived? Which should be targeted for growth and more competitive structuring?
• Early detection: Which are deteriorating? Should a representative visit? Should upcoming renewals be forewarned of increased rate or tightening terms?
• Mitigation: Which special asset / litigation issues are deteriorating again? Should credit line availability be reduced, future delivery commitments held?

Commercial lenders can choose among many sources of independent account-level risk assessment with widely varying characteristics. Those characteristics need to be carefully understood by Portfolio Managers.  For example, the source and limitations of the underlying data (is it predictive for you?) and the definition of terms like default (is it comparable to yours?). Many commercially available risk assessments are based on credit scores and provide a historically derived Probability of Default (PD). However, such solutions involve a serious intellectual exercise before they can be used as a portfolio management tool. How so? Consider a customer with a theoretical credit score of 700 that has a PD of 1%. Now, say you booked that customer at the beginning of 2007 and he has paid you as agreed ever since.  In the absence of any other derogatory information, how would you determine the PD at the end of 2007?  At the end 2008?  Today?

If you answered, “Higher, Higher, and I’m not sure” then you’re certainly in good company.  Intuitively, the predicted default risk should change in response to economic conditions, as well as the customer’s own situation. Score-based historic PD’s can only rank order your customers by past experience for entities with similar characteristics.  They cannot predict the default rate for the portfolio; at least, not until you calibrate them for the current economic environment. If you are willing to attempt that using a quantified and consistent methodology and can back test it against your actual history, then you are all set.  However, even then, you would be losing some of the benefit of having a completely independent and objective risk assessment to use as a risk forecasting gauge.

In response to demand for that calibration, PayNet has developed a forward-looking PD using our proprietary data consisting of millions of loans and hundreds of thousands of defaults.  We employed the necessary statistical modeling techniques to take inputs from macro-economic statistics to generate an account-level PD that is not only calibrated to the most recent economic releases, but also forecasts the default rate for the next eight quarters. Our service, PayNet AbsolutePD® is a unique solution for small business exposures (less than $2.5 million) and was developed in conjunction with a team led by Dr. Darrell Duffie, Dean Witter Distinguished Professor of Finance at the Stanford University Graduate School of Business.

You can deploy a forward-looking PD into business strategy planning, forecasting and account management with greater confidence (and less work) than alternative methods.  Its application reaches into capital-allocation decisions and provides decision support for provisions and reserve adequacy analysis.  With it you can answer segment level questions:

• Risk-based pricing:    Which region, branch, industry requires a pricing adjustment now, in order, to maintain forecast profitability?
• Forecasting:               Is there enough evidence in the trend to support a change in forecast provisions?
• Benchmarking:          Which segments are over or under-performing compared with the National or Industry statistics?

Having just experienced the worst recession since the Great Depression, many of your business’ stakeholders (management, investors, funding sources and regulators) are all asking you to address the business concerns that are top-of-mind.  Employing external data sources will provide cost-effective assurance that your portfolio management practice is detecting changes in risk sooner, increasing visibility to your customers’ universe outside your system and improving your focus on the accounts and segments that really call for your attention.

As an aggregator of commercial loan performance data from a closed network of lenders, PayNet provides both segment and customer level data to its member lenders. Our risk management subscribers are using this data and finding sustainable financial benefits in three areas: renewal and underwriting activities; early detection and loss mitigation; and, default forecasting and capital allocation.