With the additional help of artificial intelligence, digital banking is generating vast amounts of data that requires rapid, precise and robust analysis. This, in turn, will provide greater knowledge about customer habits and preferences, help gain and retain customers and have a positive impact on revenue. Furthermore, adopting the latest analytical models offers the chance to obtain richer data, and machine learning is capable of identifying complex, non-linear patterns from large data sets to generate more accurate risk models.

These days, banks generate so much data that they often lack the capabilities to effectively handle and analyse it all. As a result, they turn to companies like GDS Modellica to help them analyse macro-data, carry out predictive analysis and harness the power of other big data tools to help increase collections and protect themselves against fraud quickly and efficiently. For lenders, for example, it is absolutely vital to have a system that can analyse data and better understand customer behaviour and demographic changes.

According to GDS Modellica, “there is currently more customer information available than ever before, even if a new more agile approach is required to use it to make more intelligent decisions (Decisions as a Service). As companies continue to develop cloud-based systems and big data strategies to improve their customer relationships, they will see an increasing need for solutions that offer analytical capabilities, cloud-based apps and a cloud-based development platform for flexible decision management”.