One of the biggest obstacles financial services organizations face when it comes to predictive analytics is model deployment delays. In fact, some companies report that their team only deploys a very small percentage of models they build. But why? Many businesses find that their risk team and their risk decisioning technology, whether a platform or in-house built solution, speak different languages. For example, your risk team may prefer to create risk models in Python, but your technology only supports Excel, which means that models need to get ‘translated’ before they can be put into production. This model recoding process can be extremely time consuming, even for small changes. So, your risk team can’t respond quickly to market threats or opportunities. To solve this problem, lenders should choose technology that is model agnostic, meaning models can be uploaded in any language. This eliminates delays and empowers your risk team to deploy their own models so they can quickly implement changes when they’re needed. In an ideal situation your risk and dev teams should work in tandem. When knowledge gaps prevent this from happening it’s like expecting your team to perform at full power when only one cylinder is firing. Your risk and dev teams have an immense amount of knowledge between them, but while there may be some overlap there are huge areas that are specialized know-how. To gain increased efficiency and put their joint knowledge to use you need decisioning technology that can be understood by both teams. Low-code platforms, where a deep understanding of IT development isn’t required, provide an interface that both teams can use. With a visual interface it’s easy to understand exactly your workflows, integrations, and risk analytics processes. It lets your head of risk sit next to your chief technology officer and work side-by-side on a project.
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