Predictive model and ML delays and decay:
Many companies report that their risk teams only deploy a small percentage of the models they build. Why? Because businesses often find that risk teams and decisioning technology speak different languages. For every model that needs to be translated (i.e. from Excel to Python or vice versa), time (and money) is ticking. Easy machine learning model deploying is essential to enabling your ability to accurately respond to market threats or opportunities before your competition can. And getting the right risk models in place is not a one and done situation. Consumer behavior continues to change and your risk models need to keep up. Can your risk teams easily tell when your models go stale?
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