AI: a promising innovation for the German financial sector
AI is set to transform the financial sector over the next few years. Neural networks are increasingly being used for assessing and processing credit applications; companies are using deep learning to analyse huge quantities of data. This helps to prevent fraud, and allows resource-heavy, repetitive processes and customer services to be automated without any reduction in quality. Banks and insurance companies in the DACH region (Germany, Austria and Switzerland) have recognised the potential of AI in today’s digital age, but have yet to fully exploit it. The majority of executives surveyed (62%) consider AI to be a fairly important or very important innovation that will become even more important in the financial industry over the next five years, but there’s still a considerable discrepancy between the vision and the current situation. At present, just 9% of these executives believe that their company is very well prepared for using AI. Banks and insurers are only just starting to get to grips with potential fields of application for this rapidly advancing technology. As a result, the gap between expectation and implementation is large and is continuing to grow. Many financial institutions have pilot projects for AI, but few have succeeded in transferring these ideas to day-to-day operations. Even companies which already have in-house AI expertise are often unsure about how to address this issue.
“AI is going to be a key competitive factor for financial institutions in the future, but it also offers other applications far beyond process automation.”
The study at a glance
What are the most important fields of application for AI?
At present, most banks and insurers in the DACH region are looking at potential uses of AI from a conventional business perspective: 79% of executives surveyed want to increase digital efficiency in their business processes, nearly three quarters (73%) want to make general cost savings, and 50% expect AI to help their company better comply with regulations. More than half of these executives (55%) are also already using AI in new areas, such as chatbots, automation and predictive marketing. However, many opportunities are being missed: for example, intelligent data analysis could be used to greatly reduce complexity of risk assessment issues and support decision-making in controlling.
What are the biggest obstacles to adopting AI?
In order to get their levels of adoption up to the European average, many financial institutions are currently assessing which new projects are particularly suitable for AI. But in many cases, there’s still a long way to go towards implementation once these projects have been identified and specified: 69% of executives surveyed identified lack of data as an obstacle. Around two thirds (67%) said they were struggling with budget constraints and inadequate financing for AI projects, and 64% of companies simply lack employees with the expertise to answer questions on getting AI established: which area of the business offers a suitable way in for getting AI projects established in business operations? Which department will be responsible for financing the integration process? And should AI projects be anchored in IT, or – considering their strategic importance – should they have their own, independent management structure?
What discrepancies are there within the sector?
The study also shows that using AI in day-to-day business and in established processes – for personalisation or new business models, for example – has so far been given a low priority in financial services. The fact that truly understanding AI systems is frequently difficult or impossible also presents a considerable hurdle in the financial sector. Traditional mathematical applications can be represented by relatively simple algorithms, whereas technologies such as deep neural networks are much more sophisticated and therefore difficult to understand. This creates another problem: the financial services industry is heavily regulated, with companies being required to explain their processes and decisions in detail to regulatory authorities and internal auditors. Because AI is sometimes considered a black box, many companies – not just in banking – are hesitant to use it.
“There are few industries in which companies have access to such a wealth of data as is the case in the financial sector. The key lies in identifying where to find data of sufficient quality, and which use cases look particularly promising on the shop floor.”
AI and the future
As the utilisation and importance of AI increases, ways of working in the financial sector will undergo major change. In the long term, AI will undoubtedly be able to take over many tasks currently performed by humans. This offers great opportunities: companies will be able to reduce workload and give employees more time away from work without sacrificing productivity. New areas of responsibility, both in data management and elsewhere, can also create new areas of business for a company. “However, internal AI expertise is essential to allow companies to exploit the huge potential of AI and use projects efficiently for value creation in the long term,” says Michael Berns. “Finance service providers therefore need to make very large investments in employee know-how in the very near future.”
“Financial institutions in the DACH region are already struggling to catch up with competitors from Asia or the US who have had a head start. Companies in the financial sector should identify promising AI projects now, and rapidly integrate them into their processes.”
Methodology
PwC’s study “How mature is AI adoption in financial services?” is based on a survey of 151 executives in the industry, from banks, insurance companies, and fintech companies. The objective of the study was to create a comprehensive picture of the most important lines and sizes of business in the financial sector; as a result, the study used both qualitative interviews with experts in the sector (46 participants) and a mainly quantitative online survey (105 participants).
