Risk and fraud officers around the world are beginning to realize that their risk models simply aren’t up to scratch.
They’re seeing for themselves that it’s not enough to build a risk model once and then rest on your laurels. The risk landscape is changing, new datasets are emerging — and your machine learning models need to evolve and rise to the challenge, too.
Unless your models deliver real business benefits and answer your pressing questions, what are they for? In this in-depth guide, we reveal the real reasons your machine learning risk models are falling short, what you can do to fix them — and exactly how to tackle the problem.
- The three vital steps to enhancing your risk models
- Where to look for the data you don’t have in-house
- How to streamline your connections to external data