DONE: Provenir_SME_Lending_eBook_English_FINAL_061022

Not Just Any Technology, but the Right Technology: Your Risk Solution Checklist Technology is obviously essential to digitizing lending processes, but to really support future business success your chosen tech solution needs to power agility, flexibility, and scalability. For financial services organizations this means that it needs to do the following things: Simplify Data Access Data is the key to making smart decisions. Would you buy a car without knowing its service history, number of kilometers, number of owners, etc.? To pull information into your digital decisioning solution you need to integrate key data sources. But hard coding API integrations makes your risk team reliant on your IT department to build and update integrations as you need them. This slows down your risk team and prevents you from rapidly implementing your risk strategy. Instead, you need a solution that empowers your risk team to quickly create connections to data sources that help you understand the health and potential of an SME, including accounting software, tax information, and bank accounts. For example, risk platforms like Provenir provide integration wizards that make it quick and easy to create new integrations and normalize data so it’s available to your team in a usable form. With no-code technology, business users have access to a drag-and-drop user interface that empowers your risk team to easily map data from the source into your risk analytics tool. With simple integrations your team can also easily explore the use of alternative data (like social media presence, travel data, website info) in the decisioning process, making it easier to expand the approval criteria needed to power your loan decisions. Power Advanced Analytics Your risk analytics platform should empower your risk team to use the latest predictive analytics tools to not just decision loans, but also improve the speed and accuracy of the decisioning process. The right technology lets you quickly deploy advanced analytics tools such as machine learning, artificial intelligence, and other data science techniques.

The right technology lets you quickly deploy advanced analytics tools such as machine learning, artificial intelligence, and other data science techniques. One of the biggest obstacles financial services organizations face when it comes to predictive analytics is model deployment delays. 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. Which means 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. Bridge the Gap Between Risk and Dev Teams 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. No/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 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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