Les banques du futur travaillent de manière numérique et automatisée. Au-delà de mieux satisfaire les clients, les banques bénéficient d’avantages en termes de coûts grâce à des processus automatisés. Cela s’applique en particulier aux activités de routine dans le secteur du prêt à la consommation. Mais lorsqu’il s’agit de processus complexes, qui comprennent les prêts aux entreprises et le financement de projets, le degré d’automatisation est considérablement moins élevé. Toutefois, en décomposant un contrat complexe et en identifiant les fonctions nécessaires individuelles, les mêmes processus peuvent alors être automatisés que pour les prêts de détail.
Fini, les taux d’intérêt négatifs – par contre, une perspective de taux d’épargne plus élevés … mais les débiteurs font face à des charges plus lourdes. Ceux qui ne peuvent plus se permettre le rêve de posséder une propre maison posent un problème pour les banques. Les solutions basées sur l’intelligence artificielle aident à identifier les probabilités de défaut et à avertir à l’avance de dommages majeurs.
Artificial intelligence, or AI for short, is currently on everyone’s lips. We associate buzzwords such as machine learning, neural networks and self-learning algorithms with a modern trend technology that we already encounter all too often in everyday life: Be it voice assistants like Alexa, Siri, Cortana & Co, personalised advertising while surfing the internet, traffic jam reports from Google Maps or sense-based translation tools like DeepL.
But what about the use of AI solutions in the banking environment?
Although someone once said that the balance sheet of a company is like the three body shapes of a woman – one shape she has, another one she shows, and yet another one she wants to have – we have succeeded in developing a software that makes it possible to generate reliable forecasts of the bank balance sheet for future booking days. In accordance with the FERNBACH procedure, both balance sheet truth and balance sheet clarity are maintained in the forecasted balance sheets.
The EBA guidelines on loan origination and monitoring (EBA/ GL/2020/06) introduce requirements for the credit assessment of borrowers. The objective of these guidelines is to ensure that high-quality loans are granted in order to reduce the likelihood of new loans becoming non-performing in the future. To this end, the guidelines define detailed governance arrangements for the granting and monitoring of credit facilities throughout the credit life cycle. The EBA guidelines on loan origination and monitoring are divided into a total of eight chapters.
So what’s your modernisation strategy – a ‘big bang’ project to implement a new software package or do you keep on improving the systems already in operation?
We can recommend a third alternative. Our project method ‘Continuous Renewal’ reduces project risks and achieves success quicker with more focus on customers. We supply a provider-independent, state-of-the-art component system.
The European Banking Authority (EBA) has set out guidelines for integrating measures for processing problem loans into banking procedures. These focus on concessions to borrowers with regard to repayment methods when borrowers start to show signs of financial difficulties.
The "Guidance to banks on non-performing loans" (NPL) published by the European Central Bank provides for an operational model for NPLs based on specialised NPL workout units (WUs) which are to take over activities in all stages of non-performing loans.