3 Things Auto Lenders Should Know About Data
In today’s business environment, auto lenders need data to get ahead. But not just any old, raw data – it has to be standardized data. That’s because only standardized data can provide reliable information about the car-buying experience – like which lender booked the loan or which dealer sold the car – that businesses can use to gleam intelligent insights. Below are three considerations lenders should understand to get the most out of their data.
1. Raw Data is Imperfect
When a consumer buys a vehicle, there are several options to register the automobile including by mail, electronically at the point of sale, or in-person at the Department of Motor Vehicles (DMV). The problem with this process is that the registration information is recorded in multiple channels by different humans – who have different language patterns.
Dr. Rajkumar Bondugula (aka Dr. Raj), a principal data scientist in the Equifax Data Science Lab, explains what can happen at the DMV. “When you go to the DMV and hand over your documents to the clerk for your registration, the clerk captures all your information manually. Inconsistencies such as conventions and typos creep into the data because of this manual process. These inconsistencies will lead to incorrect conclusions when raw data is interrogated rather than standardized data.”
2. AI Provides Structure to Data
The Equifax Data Science Lab recently conducted a lost sales analysis on auto lenders. As a result, it identified 720,000 raw lender names in DMV records. That’s a sharp contrast to internal data that shows there are only about 30,000 auto lenders in the U.S. Why the discrepancy? The answer is: imperfect data. For example, there are more than 400 variations of a leading bank that provides auto loans.
Furthermore, Dr. Raj’s team has developed an Expert System to standardize lender names that feed TradeSight, which is a market intelligence platform that Equifax offers to auto lenders and dealers. An Expert System is a type of AI that uses knowledgebase and inference engines to imitate the factual knowledge and analytical thinking possessed by a human domain expert. The rules in the inference engine are automatically extracted using machine learning techniques.
3. Structured Data Generates Reliable Insights
Finally, Dr. Raj and his team use natural language processing (NLP) to standardize the raw unstructured data – like DMV records – during the data ingestion phase. As a result, data analysts can make more informed insights that help auto lenders build relationships with dealers that have the most potential.
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