Collections Forecasting Methods and Models

By: Michael Moore, Senior Account Executive, Sales

Portfolio forecasting methodologies are under constant revision. Let’s review four common retail lending models.

Cohort Averages – Accounts are segmented into many small cohorts based upon a range of criteria. Recent historical averages are computed for each segment and taken as a prediction of future performance.

Pros:

  • Extremely simple.
  • Does not require much historical data.

Cons:

  • Potentially inaccurate. Should be used for monitoring only.
  • Does not provide insight into what drives a portfolio.
  • Can’t incorporate any scenarios or outside factors, since no historical dynamics have been learned.

Roll Rates – The classic roll-rate model is a structural model of the net rate at which accounts roll through delinquency buckets.  Predictions are made by computing a moving average of historical roll rates.

Pros:

  • Simple to implement.
  • Easy to manipulate.

Cons:

  • Slow to adapt to changes in origination’s quality and economic conditions. Best in a steady-state portfolio.
  • Usually adjusted intuitively to account for future changes in the environment.

Markov Models – Roll-rate models are a simplified subset of the broader class of Markov models. State transitions are modeled by transition probabilities.  Probabilities are usually computed as a moving average on the historical data.

Pros:

  • May be used to model revenue and delinquency.
  • A logical extension of roll-rate models.

Cons:

  • More difficult with line-of-credit products.
  • Needs additional models to adjust weights for life cycles, seasonality, management actions, or economics.
  • Markov models have seen some limited use, but they are not generally flexible enough to be used across the range of portfolio forecasting problems.

Vintage Models – Vintage models start by estimating rates as a function of months-on-books by computing average curves from the historical data. Vintage models are often used as the starting point in hybrid approaches which may be a good approach for a client based on specific economic and cultural conditions in the clients demographic footprint.  In addition, vintage forecasting may be able to identify changes in lending policy and changing dealer relationships over long time horizons.

Pros:

  • Immediate boost in accuracy because life cycles are essential elements and comparatively easy to estimate.
  • Apply to many variables: roll rates, utilization, attrition, etc.

Cons:

  • Must be adjusted for the quality of each vintage.
  • Require intuitive adjustment for economic impacts.
  • Many groups find vintage models technically difficult to implement in full detail with good accuracy.
  • Macroeconomic impacts are usually the confounding factor.