According to K&H Bank, although artificial intelligence (AI) is an important trend in the financial sector, traditional process optimisation and robotic process automation (RPA) currently—and in the near future—play the primary role in banking automation. The bank emphasises that the two technologies are complementary: RPA speeds up repetitive administrative tasks, while AI supports more complex, analysis-intensive processes.
Where AI and RPA sit in the development pipeline
K&H states that AI should generally be implemented later in the process improvement chain. The bank provides a rough breakdown: about 50% of improvement efforts come from initial simplification steps, roughly 40% from RPA, and only the remaining 10% of challenges are addressed with AI-based solutions.
The operational difference between the approaches is significant. Software robots follow predefined, rule-based paths and typically require human intervention only when a process encounters a new decision point. By contrast, AI solutions can collect, organise and analyse information autonomously, and their decisions are not limited to prewritten rules. Nevertheless, in the highly regulated banking environment, continuous human oversight of AI systems remains essential.
Practical uses at K&H
Many software robots already run in the background of financial institutions, handling large volumes of repetitive administrative and back-office work. At K&H Bank nearly one hundred such robots operate, relieving staff workload and speeding up customer service processes.
Automated systems at the bank support creditworthiness assessments, perform entitlement checks and administrative tasks for opening savings accounts, and process large corporate deposit placements. Another robot checks mandatory documentation for corporate clients and, if it finds deficiencies, automatically sends the required notifications—reducing error rates and operational risk.
AI’s role: analytics and risk reduction
While RPA excels at data handling and precise, repetitive execution, AI’s analytical capabilities become important for enhancing security. A notable application is fraud prevention: AI can detect suspicious money movements by analysing digitally processed data.
The system can also enable the bank to alert relatives when fraud is suspected—provided the client previously consented and supplied contact details—which can be particularly helpful in protecting older customers.
Conclusion
K&H’s view is that classic automation remains the dominant, highest-volume element of corporate development and operational efficiency efforts. AI, however, plays a valuable complementary role in complex analyses and risk management, and its deployment should be accompanied by ongoing oversight from banking professionals.
An AI assistant contributed to preparing this article; the final content was edited and verified by a journalist.
