Technology trends and automation in the financial sector
By Arnold Kinzel
Consultant | Advisory
April 27, 2020 | Frankfurt, Germany
Arguably, no other sector has been influenced and transformed more by innovations in technology than financial services. The fast-paced nature of the sector requires finance companies to evaluate and adapt opportunities quickly. Competitive pressure is not only restricted to the established finance firms but has expanded into the tech-sector where computer scientists develop low-cost alternatives for the consumer. Therefore, it is imperative for financial services to absorb technology and build up their IT know-how for customer-faced services as well as internal operations.
In fact, a PwC study (2020) has found that 28% of the Banking & Payments industry and 22% of the Insurance, Asset & Wealth Management industry is at risk in 2020. This pressure results primarily from FinTech companies entering the market and offering low-cost substitution products for consumers. However, well established companies have an advantage in resources and should be able to stay ahead if they allocate funding towards internal innovation including the automation of their process infrastructure and the implementation of Artificial Intelligence, among others. We have researched the major technology trends for retail banking, insurances, and the capital markets. The findings are based on a survey and the resulting statistics conducted by Accenture and Oxford Economics (2018).
It was observed that technology investments in retail banks were flowing into blockchain and cloud-based technologies. The latter which should bring about measurable business value as well as improve internal operations. 47% of retail banks are investing heavily in those two technologies respectively but only 13% believe to be investing in these areas in the very near future. Rather, retail banks shift their funding from cloud and blockchain towards more cutting-edge technologies such as Artificial Intelligence (internal efficiency and customer-facing processes). Although retail banks believe to be developing further their internal blockchain applications, they are focusing on AI implementation the most.
Similarly, insurances will focus more on Artificial Intelligence implementations, agile development, and blockchain technologies within the next couple years. In addition, they have also recognized the need develop their operational effectiveness and acknowledged the need of change management capabilities to drive changes in general, and, more specifically, technology. Indeed, the greatest obstacles faced by insurances are concerned with systems integration, bad IT collaboration and a lack of change management expertise.
Research suggests that capital markets are much quicker in adapting to innovation than their peers in the financial sector. Investment patterns are more evenly distributed indicating early entries into new technologies and further strong investment into more advanced innovations like Artificial Intelligence for exploiting internal efficiency chances. Capital markets have anticipated the benefits of Artificial Intelligence in client-facing processes earlier and are now ready to implement internal AI solutions for operational efficiency. This is consistent with the fact; the data analytics will play a more important role in these organizations. Data analytics is the science of using raw data inputs to reach conclusions and drive evidence-based decisions. It is therefore inseparable from AI technologies which should be part of any corporate data analytics strategy – for internal operations as well as external customer processes.
Process Automation in finance
In all three categories above, agile development and AI play major roles in firm’s innovation strategies. It has also been examined that, in the insurance business, IT systems integration and compatibility is a pressing issue. Process automation can assist companies in moving valuable time resources away from repetitive tasks and towards business values encouraging agile project management. Furthermore, many automation technologies, such as Robotic Process Automation (RPA), Digital Process Automation (DPA), or Intelligent Process Automation (IPA), do not require manipulation of the existing IT landscape but are able to navigate between different systems to execute their intended purpose. From this, it is clear that automation can serve as an enabler to solve some of the more challenging tasks faced by the finance profession while bearing relatively low investment costs.
The graph above demonstrated how the before-mentioned technologies are already being implemented by financial firms. Investments in IPA are expected to increase constantly over the next years while RPA and AI operations technologies are even predicted to experience exponential investment volumes until 2023.
Automation use case in finance
In an ever evolving and more competitive environment, RPA can support banking in becoming more customer centric. Today’s customers demand 24/7 services and fast execution preferably online and intuitive. Simple requests such as changes in credit limits can easily be executed by virtual robots. For example, the customer can submit an online form with his required data and signature via an online portal. The robot will, under pre-defined rules and guides, evaluate the form and whether the customer can extent his limit or not. This requires a combination of RPA to retrieve and transmit data from and into different systems and OCR technology to extract information from a document (e.g. PDF). The evaluation and decision of credit limit expansion does not need manual interference anymore.
Accenture. (2018). digital innovation in financial services. Von www.oxfordeconomics.com: https://www.oxfordeconomics.com/digital-innovation-in-financial-services
PwC. (2020). Financial Services Technology 2020 and Beyond: Embracing disruption.