Harmoney and DataRobot Drive Innovation in Australasia’s Personal Loan Market
Company Size
11-200
Region
- Pacific
Country
- Australia
- New Zealand
Product
- DataRobot
Tech Stack
- Machine Learning
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Revenue Growth
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Fraud Detection
Services
- Data Science Services
About The Customer
Harmoney is Australasia’s leading marketplace lender, challenging the dominance of big banks in the personal loan market. The company operates a platform that matches borrowers with lenders without the need for a financial intermediary, aiming to deliver greater efficiency to the market. About 60 percent of Harmoney's staff are engineering and data science professionals, and the company can be viewed as a technology company. The company operates in a highly competitive market, with personal loans comprising about 4 percent of the banks’ balance sheets while generating about 16 percent of net profits.
The Challenge
Harmoney, a marketplace lending platform in Australasia, was facing the challenge of keeping pace with the constant innovation required to stay ahead of big banks. The company's small team of data scientists was tasked with the development and deployment of machine learning models to improve the efficiency of the personal loans market. However, the team was finding it difficult to dedicate sufficient time to predictive analytics due to their other responsibilities. Additionally, the traditional tools they were using for modeling were time-consuming and often led to distractions from the main goal of improving the business.
The Solution
Harmoney turned to DataRobot to automate the development and deployment of machine learning models. DataRobot provided a single integrated environment that supported both model development and model deployment, enabling the data science and IT engineering teams to create effective models and accelerate their deployment into operational systems. The use of machine learning improved the accuracy of credit risk assessments, which in turn increased profitability by reducing defaults. Furthermore, Harmoney was able to reduce the number of questions asked to borrowers during the credit application process, thereby accelerating the decision-making process and reducing the dropout rate.
Operational Impact
Quantitative Benefit
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