Marlette Funding, Best Egg Loans is using machine learning (ML) to transform business processes across the organization in revolutionary ways. To make sure they produce a best-in-class fraud detection model for their first foray into ML (and best-in-class data projects in general when working with other parts of the business), the six person team at Marlette Funding:
- Considers return on investment (ROI). Before taking on a data project, the team considers first and foremost the potential business impact of the project. In the case of fraud detection, they calculated that if the model were to catch even one instance of fraud, they could save a personal loan lender an average of $15,000. But they also considered indirect benefits, like the fact that a more sophisticated model would speed the process of getting a loan for customers by minimizing the number of cases that are not fraud.
- Gathers all available data. The key to an innovative data science project is to put as much data in to create the model. In the case of fraud detection project, that means creating a massive dataset to work with using not only internal data, but externally available datasets from credit bureaus, fraud detection vendors, and more.
- Tests/ benchmarks against current strategy. It is necessary to compare developed models with the current solution because if the performance is not better than the one of the existing system it will cause more unnecessary work in monitoring.
- Deploys to production. Once tested and benchmarked, they are put in production, where they can actually have a real impact on the business. The fraud detection model at Marlette Funding is currently deployed and generating cost savings for the lenders.
Data analysts at the core of the organizational structure
Most of the business units at Marlette Funding have their own analysts who look at data and for opportunities for more advanced analytics. From there, they can approach the central data team to collaborate on projects together. This allows the technical skills of the data team to be enhanced by the business knowledge of the analysts and other experts in business units for more optimal project results.