Tietoevry Banking driving collaborative R&D programs in the Veturi ecosystem project

Financial services are also one of the focus areas of the Business Finland funded Tietoevry Veturi ecosystem project, which is part of the NextGenerationEU recovery program. We are committed to driving three main research topics in the project: real-time bill payment, financial insights, and real-time financial crime prevention. The common nominators are real-time services and extensive utilization of data.

The aim of the ecosystem model for bill payment is to make the invoice and payment flows completely transparent to the companies, whereas now a company normally learns about the status of the invoice on payment due date + 1 time by the bank providing bank statement. The whole invoicing process will be fully real-time, just like in mobile payments already today, with a dashboard showing all the stages from delivering the invoice to the payment.

The ecosystem aims to comprise banks, financing companies together with large billers like retail groups and insurance companies, in addition to fintech companies with new innovations and services. We have a good set of participants interested in the initiative, but there are still some missing parts. In the Veturi sub project we will identify the gaps and define the operating model for roles needed in the value chain. The introduction of real-time payment will have a huge societal impact on the reduction of manual work when debt collection due to human errors, like forgetting to pay, can be avoided.

In the Financial insights sub project, we will be aggregating financial data into a datalake which is utilized for sustainable decision making. At the moment, we are creating POV (proof of value) for credit area solutions, eg. AI-driven portfolio analysis and machine learning profit scoring models. These are done already in a smaller ecosystem and the first use cases are focusing on managing the risks in consumer loans. We will continue the project with company loans processes and expanding later to company liquidity management forecasting.

In this sub project, starting in 2023, we will leverage extensively machine learning, AI and data on financial services for the prevention of fraudulent transactions, anti-money laundering and other automated processes for financial disputes. All the data gathered from sources throughout the fraud prevention value chain is efficiently converted to intelligence with help from our integrated ML functionality and though the analytics dashboard.

We are constantly looking for active participants to our ecosystem, so if you are interested, please contact Sami Uski or Mikko Erkkilä.