Machine learning is one of those buzzwords not everyone understands. Often people confuse machine learning with automation or with continuous learning and envision self-aware robots taking over the world. In reality, machine learning is a method or series of mathematical techniques that uses computers to organize data, identify patterns, and make predictions. In other words, when designed properly, machine learning is an approach that enables faster and more accurate decision-making.
For example, to understand the value of using machine learning in credit risk assessment, an auto finance company that partners with independent dealers to provide financing to non-prime consumers asked us at Enova Decisions to develop a credit model using machine learning techniques. Using the same data that the company had access to, our model was 5x more effective at predicting defaults than the company’s existing model. This machine learning model accounted for ~$1 million losses that could have been prevented!
The applications of machine learning are vast and it’s easy to feel overwhelmed by the possibilities. That’s why we recommend starting with operational decisions: complex but routine decisions made by employees on a frequent basis. In the context of consumer finance, operational decisions include verifying identity, detecting fraud, assessing credit risk, producing a loan offer, and managing payments/collections. Incremental improvements to any of those decisions will greatly increase your bottom line.
Now that you have a decision in mind, it’s time to talk about infrastructure. This is where many businesses fall short. The tools and technologies necessary to build a machine learning model are very different than those necessary to run a machine learning model in a live environment. In other words, even if you already have a team of data scientists, you may not have the infrastructure to implement machine learning and improve decision-making in real-time.
The time and cost required to build the right infrastructure is not feasible for many businesses and why we recommend partnering with a reputable cloud-based decision management vendor. Just make sure the vendor you choose can manage all your data connections, deploy machine learning models with high availability, and handle ongoing IT maintenance.
Learn more in our machine learning white paper.
Contact us today to learn how Enova Decisions can help your business get up-and-running faster with machine learning-driven decisions.