Finnova Analytics: A strategic field of action
Finnova has identified data analytics as one of the strategically important topics for the future of the Finnova Banking Software and relies on the systematic development of a uniform and cross-module analytical framework right across all specialist areas of a modern bank.
The focus is on both efficient fulfilment of existing and future regulatory and compliance requirements and optimisation of economic key figures, profit maximisation and cost reduction.
Advanced analytical approaches
In this development, Finnova relies on the use of unique and highly precise new-generation algorithms, which go far beyond the popular open source programming environments and ensure optimised and high-quality data processing and modelling results. The complexity of the analytical tasks and challenges is not reduced here, but rather the handling of the sophisticated methods and approaches by means of fully automated processes.
Interdisciplinary banking topics
Fraud detection and prevention, sales optimisation, client profiling and segmentation, dynamic stress tests and simulations, as well as processing of structured and unstructured data are only a few examples of the analytical tasks that are the focus of these developments.
Finnova hot topics
Following several successful internal proofs of concept in the areas of sales optimisation as well as processing and modelling of external data, Finnova is currently increasingly concentrating on the further development of the areas of fraud detection and prevention as well as analytical CRM. T
he focus is on topics such as real-time analysis of internal and external data across clients and counterparties, their relationships, behaviour and transactions, as well as identification of new, as yet unknown suspicious or promising behaviour patterns.
As a result, the originally compliance-based term ‘Know your Customer’ gains a completely new meaning and develops in the direction of a comprehensive ‘Understand your Customer’ approach. Both the protection of clients and the integrity of the bank, and the optimisation of sales campaigns, client satisfaction and maintaining the client base are at the heart of this.
With the expansion of the Finnova Analytical Framework, the possibilities for data analytics based on structured and unstructured data, as well as the knowledge gained from these, will be extended
in future, and will contribute to a uniform approach to analytical tasks and their solutions.
Deep learning & big data: Digitalisation of decision-making processes
"The Finnova Analytical Framework combines the latest technologies such as deep learning and big data analytics with solid banking know-how. The solution has the potential for general digitalisation of decision-making processes in banks. It is a first step towards automation of expert knowledge."
Thomas Zerndt, Head of the Competence Centre 'Sourcing in the Financial Industry' (CC Sourcing)
Photo: Rainer Alt, Information Systems Institute, University of Leipzig, and Nikolai Tsenov, Product Manager Compliance, Finnova AG, at the award ceremony