Think further: Innovation potential of artificial intelligence for banks


The turning point has long since been reached. There is hardly any way around artificial intelligence (AI). The pressure on companies and institutions to develop and utilise potential applications for their industry as quickly as possible, which was initially only slightly noticeable, is increasing constantly. Opportunities must be seized and risks avoided. Banks are also recognising the need for change and are taking the first steps.

Artificial intelligence: part of the future of banking

According to a study conducted by Confinpro AG in collaboration with VÖB-Service GmbH, 65 per cent of the more than 380 experts surveyed from German financial service providers are convinced that AI and banks are inextricably linked. However, if organisations fail to use the latest technologies such as machine learning, 87 per cent believe they will be at an enormous competitive disadvantage.

For many banks, AI is therefore no longer just a distant dream of the future. According to the industry report, two thirds of banks and insurance companies in Germany are already using AI solutions. These range from chatbots in banking applications, which bank customers can use to get in touch, to process automation. However, the participants in the study do not believe that the next stage of evolution can be achieved alone. 72 per cent see technology partners as support for the further integration of AI applications. Especially when it comes to overcoming obstacles together.

Overcoming obstacles

Banks are facing more and more challenges, especially at the beginning. Shying away from this and postponing the topic of implementation to the future is not a sensible alternative in view of the general AI upgrade. It is much more helpful to recognise the initial difficulties and actively look for solutions. The following points are typical stumbling blocks:

  • – Missing data or poor data quality
  • – Little expertise in relation to artificial intelligence
  • – Uncertainties regarding regulatory requirements (including data protection)
  • – Outdated IT infrastructure
  • – Non-existent target definition and unclear use cases
  • – Insufficient involvement of all specialist departments/employees

If banks are aware of their individual challenges and look for solutions, ideally before the project begins, they can keep an eye on the key success factors for AI projects. With sufficient data, data quality, IT expertise and knowledge of their own goals and applicable regulations, project success is within reach.

Opportunities recognised: AI models for the banking sector

But in which direction can banks think? In view of the many possible applications of AI, it is crucial to assess which processes can be efficiently automated, where meaningful human-machine interaction is conceivable and where employees must remain an indispensable point of contact. Three scenarios show the benefits.

Improve customer service: The idea behind this is well known. If you know your customers, you can give them customised recommendations. This is where AI algorithms come in, generating suggestions for bank advisors on funds, shares or bonds for their customers. AI can also be used to automate the risk assessment of portfolios in order to ensure security and maximise customer satisfaction.

Finanzkriminalität reduzieren: Verdächtige Transaktionen lassen sich mithilfe von KI-Modellen aufdecken. Dafür analysieren Anwendungen beispielsweise Transaktionen hinsichtlich verschiedener Kriterien wie Betrag, Währung, Zielland und Transaktionstyp. Fallen Abweichungen auf, meldet die KI diese an einen Kundenberater, der die Informationen noch einmal manuell prüfen und bei naheliegendem Verdacht an die Finanzkriminalitätsabteilung weiterleiten kann. Somit hilft KI im Kampf gegen Geldwäsche und Co.

Reduce financial crime: Suspicious transactions can be detected with the help of AI models. For example, applications analyse transactions according to various criteria such as amount, currency, destination country and transaction type. If deviations are detected, the AI reports them to a customer advisor, who can manually check the information again and forward it to the financial crime department if there is any suspicion. AI thus helps in the fight against money laundering and the like.

Future-proof – with AI

Whether in customer service, combating financial crime, fulfilling sustainability obligations or simply optimising internal processes – AI is already being used in a wide range of areas in the banking business. As the future holds even more points of contact and therefore simplifications and opportunities, it is now time for all managers to stay alert to AI and seize potential early on.

Are you already thinking about the potential of AI in your bank? Then we have even more starting points for you!