Harmoney has selected automated machine learning platform DataRobot to improve the performance of its credit risk assessment process.

Harmoney is a peer-to-peer lending marketplace, acting as an intermediary between borrowers and lenders.

DataRobot provides an automated machine learning platform that puts the power of machine learning into the hands of business users.

DataRobot automates the data science workflow, enabling users to build and deploy accurate predictive models in a fraction of the time of traditional methods.

“With our deployment of DataRobot, we're now using artificial intelligence to reduce the risk for our lenders,” says Harmoney joint-CEO Brad Hagstrom.

“Our marketplace, which has more than 15,000 members and has facilitated more than $700million in loans, will now feature the same credit risk assessment capabilities used by the best banks in the world”.

“The machine learning models that Harmoney has created with DataRobot are trained on data captured from more than 300,000 loan applications. These models have proven to be so accurate in their real-time predictions of credit default that Harmoney has been able to improve profitability for lenders, reduce costs to borrowers, and sharpen the company's competitive position against incumbent lenders in our market,” Hagstrom says.

“With DataRobot, we have decreased the time required to deploy predictive models from 12-16 weeks to minutes,” Hagstrom adds.

In the three years since its inception, Harmoney has introduced the concepts of a shared economy to New Zealand and Australia's financial market.

With the application of machine learning automation and advanced modelling capabilities in existence, Harmoney is strengthening the marketplace with industry-leading credit risk assessment.

“This is just the next step in our process of disrupting both New Zealand and Australian banking industry and the financial institutions that currently dominate it,” says Hagstrom.

“Our Australian business is on a bigger trajectory than the New Zealand business, and we are very excited about the opportunities for lending in both countries with machine learning a contributing factor,” he adds.