Credit card issuers may not think of choosing a credit scoring model as a strategic decision. Perhaps because, historically, they’ve had few choices or because conventional scoring models were just that — conventional. But today’s advanced models, such as VantageScore 4.0, are strategic tools, with big potential impact on portfolio P&L.
Credit scoring advancements in data, analytics, segmentation, governance, and consumer interaction can help issuers optimize portfolio performance. As borrower behaviors change to reflect demographic and economic shifts, card issuers can leverage these innovations to improve the bottom line.
VantageScore, a credit scoring model that advances multiple portfolio goals, has raised the bar for credit scoring models in the card industry. Its advantages include:
• Applicant expansion reaching 98 percent of the addressable market
• Significant improvement to accept-to-turndown ratio
• Easier implementation and interactive consumer education
• More transparency for validations and model governance
Credit scores touch a variety of functions in the credit card life cycle, and VantageScore offers compelling differentiators:
Precise forecasting of default risk is the chief purpose of any credit scoring model, and VantageScore offers superior predictive accuracy, optimized for maximum analytical insight into the middle 40 percent of the consumer population. VantageScore analysts call this segment, which comprises the most strategically important consumers, the lender “decisioning zone.”
By maximizing the universe of consumers who can be scored reliably, VantageScore gives card issuers flexibility in terms of prospecting for customers, evaluating applicants, and managing existing portfolios. The VantageScore 4.0 model allows lenders to accurately assess approximately 40 million more consumers than conventional models.
The only scoring model that operates identically on data from all three major credit reporting companies (CRCs) — Equifax, Experian, and TransUnion — VantageScore employs patented technology to minimize variations in scores across CRCs. That means less loss of precision in applications that use scores from more than one CRC.