Black Book's ValuEngine batch processing tool now offers Enhanced Vehicle Matching, a more precise VIN-level valuation tool powered by machine learning and natural language processing.
Approximately 30% of VINs do not decode to a single trim level. Furthermore, adjusting values for vehicle equipment often requires manual interaction. Now, Black Book valuations are continually becoming even more precise by increasing the frequency of unique trim identification along with any applicable add/deducts.
The first foray into a more precise VIN-level valuation enhancement was introduced through History Adjusted Valuations. Today, Enhanced Vehicle Matching in ValuEngine leverages machine learning and big data whereby Black Book’s data science team has created a process to match 17-digit VINs to a single trim and any applicable add/deducts. The process analyzes millions of records daily from numerous data sources including build data received from OEMs.
“Especially in today’s market, with razor-thin margins and price fluctuations occurring frequently, ValuEngine helps automotive industry professionals and lenders precisely value collateral,” said Jared Kalfus, executive vice president, Revenue for Black Book. “Enhanced Vehicle Matching helps to decrease multiple trim scenarios while increasing the valuation accuracy.”
ValuEngine is a collateral valuation tool allowing automotive industry professionals, lenders, OEMs, and dealers the ability to value their entire portfolio through a secure, on-demand, self-service platform.
With ValuEngine, automotive industry professionals can react quickly to market changes by valuing a portfolio’s historical, current, and projected residual values on any collateral, down to the specific trim level.
Anil Goyal, Executive Vice President, Operations for Black Book said, “We’ve invested heavily in our big data architecture which enables our machine learning processes to identify additional VIN-specific data. By utilizing ValuEngine, users can improve loss forecasting while also identifying delinquencies for a more precise collections strategy, aiding in risk analysis.”
