From my own experience, I know that the implementation of big data applications in the system landscape of a company can be a great challenge. This applies both as concerns the necessary IT resources and the other departments of the company.
Because in order to derive the right knowledge and measures from the data, the sometimes widespread data stocks must first be pooled, key figures developed and then correctly interpreted. There are often no flexible interfaces or any networking between the individual island systems, or can only be implemented at a cost.
Work in an agile manner and begin with a straightforward project scope, so that you can quickly demonstrate the first measurable results.
The subject area of dunning process optimization or receivable management is therefore a very suitable application case.
The effort pays off
I am convinced that the effort described pays off for an insurance company in every case: In big data and predictive analytics, management will find a tool to help them base decisions on solid foundations and control processes more purposefully.
Big data projects allow you to also implement customer value-oriented, differentiated dunning strategies in a digital environment, with customer dialogue on your website, in your apps and by email. In combination with digital payment methods, this leads to faster response times, quicker payments and a lower abort rates in the digital payment process.
In addition to the topics of dunning strategy and receivable management, we also see specific areas of application for the insurance business, such as payment method management, the identification of cross selling and up selling potential, underwriting, fast claims controlling.