Finance Centred Automation

This whitepaper collects and consolidates the latest research on technological disruptions, innovation in automation, the different automation techniques, and use cases in the financial sector.


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Technological disruptions in the financial industry
  •    Technology disruptions 2020
  •    Retail bank
  •    Insurance
  •    Capital markets
Automation technologies and impact
  •    Digital Process Automation (DPA)
  •    Robotic Process Automation (RPA)
  •    Artificial Intelligence (AI)
  •    Intelligent Process Automation (IPA)
  •    Impact on the industry
Automation and AI trends in the financial sector
  •    AI / automation trends
Use case distribution in a complexity-payback matrix
Use case examples in finance & accounting
  •    BCH use cases
The automation journey


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Executive summary

28% of Banking & Payments and 22% of Insurance, Asset- & Wealth Management businesses are at risk due to technological disruptions, specifically from FinTech start-ups, which are utilizing technology for optimal cost-efficiency (PwC, 2020). This puts pressure on established financial firms to innovate their businesses in order to stay competitive. Automation for internal operations, as well as client-facing processes will support companies in achieving future competitiveness. Primarily, financial firms are shifting investment volumes from cloud-based and blockchain technologies towards AI-based innovations and the development of their business’ agility, including automating operations (Accenture; Oxford Economics, 2018). Automation is not only reducing costs by 10 to 15%, but is expected to increase revenue by more than $500 billion in 2020 (Bayern, 2018), although only 10% of finance companies have adopted automation. This leaves the industry with enormous potential for the future, which is mirrored in AI and automation trends and the corresponding investment flows. Specifically, RPA demand has been increasing while finance & accounting is one of the most affected sectors (e.g., KPMG, 2015; ISG; Raconteur, 2019). Therefore, RPA and AI operations spending is predicted to increase exponentially until 2023 with IPA (Intelligent Process Automation) increasing steadily as well (HfSResearch, 2020). For prioritization purposes, Capgemini has created a payback – complexity matrix (page 12). It is recommended to automate processes with low complexity and fast payback first before moving on. For a more effective strategy, however, corporations need an individualized automation strategy as part of their executive suite and long-term planning. 


Matthias Tyroller

Matthias Tyroller

Head of AI

Arnold Kinzel

Arnold Kinzel

Senior Consultant

Maximilian Pletschacher

Maximilian Pletschacher

Former CEO