Everyone uses the same methods to underwrite. They either use logistic regression, decision trees, or a combination of both. Logistic regression can only handle 10 – 15 variables; additionally, all variables must be present (and correct). Decision trees require it be possible to divide all applicants into mutually exclusive categories.
Neither of these constraints really make sense. And when either is violated, the decision is incorrect. This isn’t surprising -- everyone knows these models are imperfect. However, they are not only imperfect, they yield incorrect answers, especially for the underbanked.
ZestFinance’s big data approach avoids these pitfalls.
ZestFinance uses machine learning techniques and large-scale data analysis to consume vast amounts of data and make more accurate credit decisions. When you analyze trends across thousands of signals, your ability to underwrite massively improves.
The ZestFinance decisioning infrastructure runs dozens of individual underwriting models in parallel and returns underwriting decisions in moments. Here’s how it works, at a high level:
The math matters in big data. If you get the math wrong, the data is useless or completely misleading. We get it right. ZestFinance has combined the best data modelers in the world with the best credit analysts in the field to build a big data model that dramatically improves underwriting quality.
To learn more about how our underwriting model can dramatically improve your credit scoring, get in touch with our big data experts at firstname.lastname@example.org.