Predictive Algorithms: Building innovative online banking
Two observations motivate this article: The crisis has shown a significant increase in the non-performing loan ratio in retail banking. One of the roots of this development is widespread weak financial literacy of retail customers. Secondly, digitalisation of retail banking is thriving and has become a key success factor. Online banking is increasingly becoming the most important contact point towards the customer within a multi-channel strategy. Nevertheless, a quick market research in the german-speaking countries shows that the evolution of online banking in the past five years only took place in the areas of design and user experience/interface. Thus there is a rising need for new solutions that add functionality.
The idea is to bring these two observations together: Historical transaction data of retail customers can be converted into cash flow forecasts. This is tackling a real-world pain: Many customers find it difficult to make a long-term forecast of their cash flows and to analyse scenarios in this context. Such scenarios can be life moments (e.g. getting promoted or losing a job) or the desire to reach a financial goal (e.g. how to fund a new car or flat). Retail customer data includes enough information to expand predictions to a sufficient long time period. Thus, customers can explore their financial future in interactive graphs and bank products can be presented in the context of life decisions.
The authors are currently transforming this idea into a software as a service product being sold to banks. The service helps the banks to augment their online banking and evolve it to become a unique differentiator.
The aim of this article is to examine the technology as well as the business opportunities of such an idea. There will also be given an outlook on the future role of predictive algorithms in both retail and SME/micro corporate banking.
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