The combination of rising interest rates and rising home values in many regions is making home equity loans and lines of credit (HELOCs) even more attractive options for borrowers, which means that clients will be looking to banks to help them find the products and offers that best fit their needs. Using the end-to-end Nomis Platform, banks implement a customer-centric approach to pricing that increases originations, reduce attrition, and stimulate utilization by anticipating customer needs and delivering context-centric offers that are the right fit for your client.
Nomis pricing science helps banks optimize their go-to and promotional rates and estimate their impact on profitability and volume based on multiple data sources, including ICON LendersBenchmark data. Predictive analytics can identify potential attrition and opportunities for increased utilization so that banks can deliver targeted, relevant, and timely offers.
Relationship pricing, promotional offers, and other pricing rules often require extra steps before optimized rates can be introduced to the market. These execution and operationalization challenges limit banks' ability to respond quickly in a competitive market. Nomis applies bank-specific rules to optimized rate sheets to streamline time-to-market and assure consistency and control. Banks can leverage Nomis' customer behavioral segmentation to ensure that customers receive the right offers at the right time at the right price.
Consultative banking relationships are increasingly important in an environment in which interest rate changes affect consumers' important residential lending decisions and banks face pressure to remain relevant. The Nomis Platform delivers customer intelligence to all customer touch points — branch, web, call center — to span multiple stand-alone systems. This context-centric approach supports win-win customer engagements in every channel (and across channels) that increase client satisfaction by delivering customer-centric offers when and where clients need them. By collecting data about front-line decision making and using it to refine analytical models, the system becomes even more accurate over time.