Underwrite.ai - Machine Learning + Big Data for Credit Underwriting

Underwrite.ai applies advances in artificial intelligence derived from genomics and particle physics to provide lenders with nonlinear, dynamic models of credit risk which radically outperform traditional approaches.

Overview

Unlike traditional models of underwriting which focus on only a handful of credit attributes, we analyze thousands of data points from credit bureau sources, to allow us to accurately model credit risk for any consumer. By applying advances in machine learning we are able to radically outperform traditional scorecards in both consumer and small business lending.

By building models on more than simple default, and focusing on such outcomes as profitability and customer lifetime value, we allow you to fully leverage artificial intelligence to increase your lending performance.

Because we are using biologically based machine learning techniques applied to your own portfolio; we model the expertise of your underwriters and the past performance of your loans to return an automated decision in milliseconds. If you have no portfolio data, we have models based upon massive quantities of publicly available datasets covering everything from home mortgage and automobile to peer to peer lending and cash advance.

Your organization can leverage the latest advanced in credit analytics without capital investment or build time.

Case Studies

Online Lender

We began working in May of 2015 with a major online installment lender whose first payment default rate (FPD) was 32.8% and overall defaults were in excess of 60%. By implementing our algorithm as their sole underwriting methodology we were able to reduce their FPD rate month over month.

May: 32.8%
June 22%
July 18%
Aug 15%
Sept 12%
Oct 9%
Nov 8%
Dec 7.3%
Jan 6.5%

More importantly, we are seeing a default rate of only 8.5% for the first three payments. In a sector where FPD rates average 35%, this is remarkable performance.

Dental Financing

Our client provides financing to dental practices for their patients. Their default rate when we started was 17.8%. By focusing on fraud prevention and credit tiering we reduced their default rate to a current 5.4%

Underserved Markets

The use of nonlinear algorithmic modeling allows us to effectively determine lending risk in countries with very limited or non-existent credit bureau utilization. We're working with an M-Pesa lender in Kenya, a small business lender in India, a private placement marketplace in SouthEast Asia, and phone based lenders in Brazil and the Philippines.

Regulatory Compliance

We are leveraging AI and nonlinear modeling to perform automated Fair Lending compliance reviews for a major bank.

The world's most advanced underwriting platform

Genetic Algorithm to determine the best modeling techniques for your specific data, industry, and risk perspective.

Machine Learning Ensembles to create a set of statistical models that can be applied to applications. We'll do all the math.

Big Data - Our cloud based system can easily process terabytes of portfolio data and return a decision in real-time.

REST API - Your IT team can implement our technology is a few hours. Nothing to install and no capital expense.

We use a highly complex process of advanced techniques to deliver an incredibly simple service to lenders. You simply post a small set of inputs via our REST API and get back a decision (approve/decline) as well as a credit tier or interest rate (your choice). It takes a slew of genetic algorithms, neural networks, random forests, support vector machines and a whole bunch of processing power to make it this easy, but we'll handle all that.

Test Drive

Just get in touch with us at info@underwrite.ai.

We'll have you send us an anonymized set of your portfolio data for training and another set for testing. We'll run your data through our platform and send you the decisions on the test data. You can compare our decisions with the ones you generated in house.

If you like our results you can sign up. Can't get much simpler than that.