Multi-Stage Lateness Forecasting Model with Treatment Factors
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Solution Overview
Problem
Current lateness forecasting methods in financial institutions, such as roll-rate-based forecasting, are limited in predicting lateness across multiple periods and do not account for treatment effects, making it difficult to differentiate between accounts receiving treatments and those that do not.
Innovation Solution
A system and method that uses a multi-stage, multi-period forecasting model to predict population migration across classifications, incorporating treatment factors to forecast changes in lateness stages over time, allowing for improved lateness forecasting and treatment analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If roll-rate-based forecasting is used, then forecasting can be performed with simple methods, but the forecasting is limited to one period or single account tracking and does not account for treatment effects
Solution Approach 1:
The patent segments the forecasting model into multiple stages (current, past due 30-60 days, past due 60-90 days, past due 90+ days) and multiple periods, allowing simultaneous tracking of population distribution across different lateness stages over time. This segmentation enables the model to handle both single-account tracking and portfolio-level forecasting while incorporating treatment effects at each stage.
Solution Approach 2:
The patent adds a treatment dimension to the traditional roll-rate forecasting model by introducing treatment factors that modify migration rates between lateness stages. This dimensional addition allows the model to differentiate between treated and untreated accounts, enabling forecasting that accounts for treatment effects while maintaining the simplicity of roll-rate-based methodology.
2Measurement precision
If multi-stage, one period approach is used, then lateness profile can be predicted for next period, but cannot track accounts across multiple periods or account for treatments
Solution Approach 1:
The patent divides the forecasting problem into multiple time periods (T0, T1, T2, T3) and multiple stages (current, past due 30-60 days, past due 60-90 days, past due 90+ days), creating a comprehensive multi-stage multi-period framework that tracks account migration over extended time horizons while maintaining precise measurement of lateness profile at each stage.
Solution Approach 2:
The patent calculates migration factors in advance based on historical data and treatment effects, then applies these pre-calculated factors to forecast future population distribution. This preliminary calculation of migration factors enables accurate multi-period forecasting without requiring complex real-time computations, thus extending the forecasting time horizon while maintaining precision.
3Device complexity
If simple tracking methods are used, then model complexity is low, but cannot differentiate between treated and untreated accounts
Solution Approach 1:
The patent applies different migration factors to treated and untreated accounts, allowing the model to capture treatment effects at specific stages. By assigning distinct migration rates to treated accounts (who receive incentives to improve lateness) versus untreated accounts, the model differentiates between the two groups while adding only minimal complexity through the introduction of treatment indicators.
Solution Approach 2:
The patent introduces treatment factors as intermediary variables that modify the migration rates between lateness stages. These treatment factors act as mediators that capture the effect of treatment programs on account migration, allowing the model to differentiate between treated and untreated accounts without requiring complex interactions or additional state variables.
Data Source
AI summary
In general, embodiments of the invention relate to systems, methods, and computer program products for predicting population migration and analyzing migration-affecting programs. For example, an apparatus is provided having a memory device with population information and a plurality of migration factors stored therein. The population information includes information about population distribution across a plurality of classifications. Each of the plurality of migration factors corresponds to a particular classification of the plurality of classifications and indicates how population members of the particular classification migrate to other classifications over a particular time period. The apparatus also includes a processor communicably coupled to the memory device and configured to use the migration factors and the population information to forecast changes in the population distribution across each of the plurality of classifications over multiple time periods. In one embodiment, the systems, methods, and computer program products are configured to predict lateness characteristics of a lending portfolio.