About Finnova

Finnova is a leading provider of banking software
in the Swiss financial centre. Finnovas' solutions are used by more than 100 banks. Finnova supports banks and outsourcing providers in achieving digital transformation and open banking.

Finnova is recognised for their powerful innovative banking solutions – in development, in operation and in advisory. Together with their partners, Finnova supports over a 100 customers from the financial services sector in being profitable and competitive.

Through Finnova's Open Platform that enables open ecosystems, banks are able to be more innovative, agile, and better satisfy their clients' needs. Finnova supports their customers' individual digitalisation strategies, both with their own products and with a broad-based partner network.

Starting position and challenge

Banks have to meet complex regulatory requirements. Rule-based systems can only provide limited support. Without the use of powerful, precise analytics, these requirements are managed with high risks and costs.

Solution

The solution was to create Finnova Analytical Framework (FAF). It is a business solution based on Prospero’s software platform for predictive analytics, called DetectX.
It is embedded in the Finnova banking software and responds to all requirements from all suites and modules.

“Prospero’s insights save time, resources, costs, and reduce the probability of operational risks and losses.”

Peter Wolf
BA Data Analytisc & Compliance
Finnova AG Bankware

98%

of false alerts were reduced with FAF

Areas of application that DetectX® offers within FAF

DetectX® can be used to map the entire range of applications of machine intelligence for optimising the business processes of a bank in a uniform platform.

Examples are:

anti-money laundering
fraud prevention
risk management
robo-advisory and
potential-oriented sales management

Banks get a 360° view of their customers

360°

and their risks and opportunities

Functionality

DetectX® is a powerful predictive analytics software that offers analytical approaches such as supervised learning, unsupervised learning, and rules based on expert knowledge.

Supervised learning uses a target variable on which the model is built. A representative sample is drawn for this purpose and separated into train and test datasets. Then the optimised model is applied to the universe of data in the productive environment.

Within unsupervised learning, the entire data is taken and used in the analysis and modelling process in an unbiased way. In this way, unknown and suspicious behavioural patterns can be revealed. The combined use of both methods enables high-quality accurate and stable models.

Expert knowledge is no longer used in isolation, but rather in combination with the other two methods. Profiling serves as a preliminary processing stage based on static and dynamic customer data. Aggregation of data takes place from the three areas of expertise.

During model optimisation the unique algorithms of DetectX® are applied. The feedback loop from the aggregation into the profiling and into the supervised and unsupervised learning is particularly important; this guarantees constant self-improvement and evolutionary learning of the system.

The smart core of the Analytics Framework

The heart of FAF is DetectX®, Analytical Engine, which validates, optimises and calibrates models continuously in the background. Regardless of whether the models are for fraud detection, transaction analysis, link analysis, robo-advisory or Analytical CRM, the engine ensures maximum precision and stability of the modeling process.

Depending on the task, data from different sources is loaded for processing, checked for completeness and distortion, transformed, supplemented and enriched. In a complex and unique optimisation process, millions of combinations are calculated and the relevant factors are continuously determined with their weightings. All operations run in an automated process so that the intervention by the experts is reduced to a minimum. To be able to handle all analytical tasks of a bank, the model creation and the model application are carried out in parallel in an unlimited scalable process.