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white paper
Loss Forecasting Methodologies
Approaches to Successful Risk Management
March 2009
»» Executive Summary
The ability to accurately forecast risk can have tremendous benefits to an organization.
As new customers are booked, as accounts age and as the economic environment
changes, the expected losses on a portfolio can vary greatly. Recent regulatory
changes combined with increased pressure to accurately predict future earnings have
pushed risk management to the forefront of business strategy. Understanding the
different components with which to forecast losses, and knowing when and how to
use each element of information, allows for greater control of portfolio management,
and results in better and more profitable account management strategies. This paper
discusses the most common approaches to loss forecasting, and highlights the
benefits and drawbacks to each.
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Loss Forecasting Methodologies
»» Forecasting Techniques
There are three common contributing performance patterns used in the analysis and forecasting of
expected losses in the consumer finance industry:
• Net flow rates
• Vintage loss curves
• Score distributions
These evaluation patterns all have strengths and weaknesses when it comes to a standalone forecast
of future performance, and they rely on different data and operating environments. Following is a
description of each technique of evaluation and an explanation of its strengths and weaknesses.
Net Flow Rate
A net flow rate is a forecast in which the cash flow from one level of delinquency (lower) to another
(higher) is applied to the current portfolio outstanding mix. This technique follows the movement of
cash outstandings from a payment status of current through all the delinquency buckets to chargeoff. Net flow rates from bucket to bucket are calculated by dividing each month’s delinquency bucket
by the delinquency bucket from the prior month. The net flow is really an aggregation of all the flows
into a bucket regardless of where they come from.
Once historical net flow rates by bucket have been calculated, their patterns over time are examined
and future flow rates are estimated. This technique is widely used to forecast the next 12 to 24
months losses at the portfolio level. It is also widely used to forecast delinquent accounts by bucket
for use as input for collections capacity plans.
This methodology has been proven to be highly accurate over the immediate next six months for
revolving portfolios and longer for closed-end portfolios. At the portfolio level, accuracy farther out
over time may be diminished by a reliance on forecasts of the total outstandings. The net flow rate
technique is very accurate at forecasting static pools, especially of closed-end loans, over their entire
lives. This is because pool liquidation is much easier to estimate than the size of the entire portfolio by
month, which is influenced by new business.
The best use of this methodology occurs when the portfolio is segmented into vintages and possibly
other sub-segments, each of which is separately forecasted, then rolled up to the portfolio level.
In addition to producing the most accurate monthly forecasts, the net flow methodology provides
the highest degree of detail and offers insights and diagnostics. By breaking losses down into
component flows, portfolio management can easily see where change is originating and the pace of
its impact:
• Current to bucket 1 is solely dependent on asset quality and, to a lesser extent, the economy.
This observed flow can be distorted by payment processing breakdown, but this is rare and can
easily be accounted for. The current to bucket 1 flow shows a lifetime curve pattern, usually rising
gradually then leveling off for a few years and finally turning down somewhat.
• Bucket 1 to bucket 2 is dependent on the quality and efficacy of collections work, and to some
extent, the economy. If risk segmentation tools are used to treat accounts differently in bucket 1,
the net flow rate may change from previous levels, but this change is usually compensated for in
the next bucket’s flow rate.
© 2011 Fair Isaac Corporation. All rights reserved.
page 2
Loss Forecasting Methodologies
• Net flow rates in all later buckets are dependent on collections efficiency, and to some extent, the
economy. They tend to be flat over their lives, unless there is an artificial buildup in the last bucket
(120+, foreclosure inventory, REO) caused by processing workflow instability or loss recognition
policies. The net flow rates in earlier buckets typically show seasonal patterns.
Vintage Loss Curves
This methodology tracks losses by month over the life of portfolio segments, usually vintages or
cohorts, and assumes that more recent segments will track along the same path. When older vintages
have run their course, timing curves can be developed to show what percentage of lifetime losses
have occurred after each month. Losses from recent vintages are then forecast by dividing actual
losses to date by the percentage of lifetime losses expected to have occurred by that date.
This methodology is straightforward and usually produces reasonable forecasts of closed-end
portfolio segments. It is susceptible, however, to every influence that could cause a timing shift
or an overall change in the level of losses. Examples of such influences are collections staffing,
the economy, structural changes such as centralization or decentralization of collections, and loss
recognition policies.
This technique works best if the denominator for calculating the percentage is the sum of all initial
loan balances, not a declining receivable. It has been very inaccurate for revolving portfolios because
the changing nature of these products (balance transfers, teaser rates) has caused a deviation from
former patterns. This methodology is not useful for forecasting losses by month and does not provide
any insights or diagnostics.
Score Distributions
This methodology uses the percentage of actual losses by score interval, either from a completed
portfolio segment or over some time period, as a baseline from which to forecast losses from other
portfolio segments. It is reasonably accurate for forecasting short lifetime losses (24-36 months) or
future periods of one to three years. The effective time period depends on the observation period
used in developing the score. The long period is needed because many losses will come from current
and bucket 1 accounts, where the scores are most accurate.
Score distributions are not very useful in forecasting what will charge-off from later delinquency
buckets at the beginning of a year. In fact, some observed historical percentage is usually used to
augment the score-based forecast.
This is the best technique for estimating the losses in brand new segments with minimal seasoning
and for very small segments for which net flow rates may be unstable due to low sample sizes. It
is susceptible to the same influences as the other techniques, and as with vintage loss curves, it
provides no diagnostics or insights.
Multidirectional Flow Rates
Although not included above, a mention should be made of attempts to forecast using a
delinquency movement matrix in place of net flow rates. Instead of just using net flows, matrices
of flows in all directions are multiplied from month to month to take into account movement in
every direction. Different organizations have tested this methodology and found it to be impractical
in most cases. This method demands very large numbers of accounts in each segment in order to
provide robust and unbiased estimators of each transition probability between buckets for each
month on file.
© 2011 Fair Isaac Corporation. All rights reserved.
page 3
Loss Forecasting Methodologies
We do recommend that Delinquency Movement Matrices be produced to use as a diagnostic tool for
determining the cause of change in net flow rates. One reason for instability may be differing goals
and incentives for collections. We often see a shift from attempting to collect all past-due money
during the year to just collecting enough to hold an account from charging-off at the end of the year.
This can be picked up as seasonality in the net flow method.
FICO’s Integrated Loss Forecasting Methodology
Over time, FICO has developed a methodology that incorporates the best aspects of all three
techniques. Our method uses the net flow rate from current to bucket 1 by segment and by time
in life as the primary driver of the forecast. This flow rate varies significantly by risk level and shows
a predictable time in life pattern. The remaining bucket-to-bucket flows are aggregated across
segments to complete the forecast. Rigorous analysis is performed to determine the best segments,
as the mix of this component is a key driver of overall portfolio performance. Segments frequently
utilize factors like the origination score for early life on books and behavior scores for more seasoned
accounts, along with other product factors.
During our analysis, net flow rates in all buckets are examined carefully and explanations of pattern
deviations are sought from the client. Future business objectives and potential strategic changes
are also discussed for incorporation. If flow rates are extraordinarily high, for example, we discuss
what action is appropriate to correct them and when it should apply so it can be considered in the
forecast. Economic characteristics are not incorporated into our Loss Forecasting Methodology.
If you are interested in the impact of economics on losses, refer to FICO’s Portfolios Stress Testing
methodologies.
»» Conclusion
Clearly, there are several elements to forecasting losses. The right approach is dependent on
organizational need, portfolio characteristics and available data. As portfolios evolve, the best method
for managing risk may change as well.
FICO Professional Services business consultants are uniquely qualified to increase the accuracy of
your loss forecast. We offer customized action plans so you can systematically achieve steadier, more
profitable risk forecasting. We measurably increase accuracy by blending the best of these patterns
into a system designed for your portfolio. Because loan loss management is a dynamic activity, the
system is adaptable to the evolving portfolio. Since the availability of data is unique for each client,
FICO consultants can work with a variety of client portfolios. Our analytic consultants find the hidden
opportunities in data by utilizing proprietary data modeling tools, then structuring a system that is
customized for you.
© 2011 Fair Isaac Corporation. All rights reserved.
page 4
Loss Forecasting Methodologies
about FICO
FICO (NYSE:FICO) delivers superior predictive analytics solutions that drive smarter decisions. The
company’s groundbreaking use of mathematics to predict consumer behavior has transformed entire
industries and revolutionized the way risk is managed and products are marketed. FICO’s innovative solutions include the FICO® Score—the standard measure of consumer credit risk in the United
States—along with industry-leading solutions for managing credit accounts, identifying and minimizing the impact of fraud, and customizing consumer offers with pinpoint accuracy. Most of the world’s
top banks, as well as leading insurers, retailers, pharmaceutical companies and government agencies,
rely on FICO solutions to accelerate growth, control risk, boost profits and meet regulatory and competitive demands. FICO also helps millions of individuals manage their personal credit health through
www.myFICO.com. Learn more at www.fico.com. FICO: Make every decision count™.
For more information US toll-free
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FICO and “Make every decision count” are trademarks or registered trademarks of Fair Isaac Corporation, in the United States and in other countries. Other product and company names herein may be trademarks of their respective owners. © 2005-2011 Fair Isaac Corporation. All rights reserved.
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