All the details for a plug play model conversion

All the details for a “Plug & Play” model conversion

Date: June 24, 2020

As part of our ongoing efforts to assist lenders with conversions to the VantageScore credit scoring model, VantageScore Solutions has issued a new white paper, Model Conversion 1: Plug & Play.

The first in a series of papers that address model conversion, the paper details a relatively simple, easy-to-apply model implementation strategy. This “plug & play” approach allows lenders to begin experiencing the value of a new credit scoring model such as VantageScore 3.0 relatively quickly. The method also can serve as the first step in a more detailed credit scoring strategy update, one that allows lenders to increase volumes without lowering lending standards, while providing consumers greater access to credit.

The white paper follows a previously posted webinar that shares a similar title.

Model Conversion 1: Plug & Play covers the implications of using a new model, ongoing monitoring, downstream implications, and how to execute a plug & play strategy for model conversion. The paper breaks the strategy down into four options:

1) Arrange by probability of default (PD) using product performance charts: This explains how to coordinate a cut-off strategy between two credit scoring models (i.e., the incumbent model and the new model).

2) Coordination of strategy by probability of default using FDIC PD maps: This describes how banks with more than $10 billion in assets can use “PD maps” as a basis for model conversion. Large banks already generate these maps for their FDIC deposit insurance assessments as a result of a rule implemented by the FDIC in April 2013.

3) Coordination of strategy by probability of default using a simple logistic arrange on a lender portfolio: This method provides a custom method based on a lender’s own portfolio with unique credit and risk dynamics.

4) Coordination of strategy by population volume using FACT Act risk-based pricing tables: This method leverages the risk-based pricing tables required by the Fair and Accurate Credit Transaction Act that classify the U.S. population of consumers with credit files into percentiles based on consumer credit scores.

Download the paper at