MQube has simplified and accelerated the process of onboarding its “Origo” platform through its new “Policy as Code” tool which cuts down the time it takes to onboard the platform from weeks to days.
To coincide with the launch of this new tool, the fintech provider has launched a handbook addressing the challenges lenders face when onboarding new technologies and how AI can be used to enable lenders to adopt new technologies seamlessly.
“Historically, lenders have struggled to adopt new technology due to legacy systems and outdated infrastructure which aren’t compatible with modern technology. Replacing or integrating with newer AI-driven systems can be complex, risky and expensive for regulated lenders,” explains Stuart Cheetham, CEO of MQube.
One of the most laborious parts of the process for lenders in adopting such systems is the integration of a lender’s policy into a new platform, which is a highly detailed, multi-page, indeterminate document representing the lender’s specific risk appetite and business strategy. The reason this process takes so long is because developers must manually translate the lender’s static policy document into a functional set of rules and configurations within the platform. MQube’s new “Policy as Code” tool changes this by using large language models (LLMs) to automate the policy analysis and configuration process, reducing onboarding times from months to a few days.
Cheetham adds: "We are making our mortgage origination technology easier to adopt, less time consuming and costly. Onboarding new software should not be a pain point for lenders but instead something that should be done quickly and seamlessly and the launch of this new tool will enable our lending partners to do just that.”
MQube’s Origo mortgage origination platform speeds up the underwriting process by assessing and automating data and documents using big data and large language models. MQube offers a wide range of AI-driven mortgage technologies including Criteria Genius a sophisticated AI chatbot, which communicates like a human being to provide instant answers to broker questions relating to a lender’s policy. The firm is also developing an architecture that will allow mortgage lenders to trade their mortgage debt on the blockchain.
