Feature 8. It offers model flexibility
Your BNPL solution should be an accelerator of model deployment and improvement, not a roadblock. Research from Rexer shows that 61% of data scientists expect model deployment delays. Surveys results show that it often takes weeks or months for models to go live, with many never making it through the deployment process. 6 There are a number of solution attributes that can help make deployment easier. For example, recoding a model instantly adds weeks to your timeline, while model agnostic solutions eliminate these recoding delays and help you push models out faster. It also means that your team can work in the modeling language of their choice, not the language your technology requires. Machine learning model retraining can also be a sticking point. Whether based on model drift parameters or a predetermined schedule, retraining models can be a time-consuming process. Solutions that feature ML Ops capabilities that allow you to retrain models in real time can make an incredible difference to your decisioning performance! If you’re one of the many businesses that are struggling with data science talent shortages and could benefit from support with model creation, you should look at solutions that offer prebuilt or custom- built models to alleviate the impact those shortages have on your organization. It can be a great way to accelerate your time to market or make a strategic shift in your risk strategy.
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