FROM THE BACK OFFICE TO REGULATION:
DIVERSE APPLICATION POSSIBILITIES OF RPA IN THE FINANCIAL SECTOR

Process requirements for RPA implementation

When implementing RPA solutions in the banking sector, back-office processes in particular have proven to be suitable starting points in recent years, as these are generally characterized by a high degree of standardization, clearly defined rules, repetitive activities and the processing of large transaction volumes. When introducing RPA, it is generally advisable to start with simple, non-critical processes in order to gain experience and achieve quick wins. Once the basic capabilities and governance structures have been established, more complex and business-critical use cases can be tackled successively. The key prerequisites for a successful RPA initiative include transaction frequency, as processes with high volumes generally promise greater automation potential and a faster ROI. In addition, the processes should be sufficiently mature in terms of standardization and documentation, and the quality and consistency of the processed data should be guaranteed. RPA solutions rely on structured, digital input in order to function reliably. [3]

High RPA relevance in the areas of regulation

RPA is proving to be an extremely effective tool for managing a wide range of processes in the areas of compliance, risk management, reporting and controlling. A key reason for the high relevance lies in the nature of the underlying processes. Many follow clearly defined rules and guidelines that can be easily transferred to the rule-based logic of RPA bots. On the one hand, the processes in these domains follow clearly defined rules and guidelines that can be precisely translated into RPA programs to ensure consistent and error-free execution. On the other hand, RPA systems generate seamless, audit-proof logs of all actions which, in combination with strict access and monitoring controls, can support the fulfillment of regulatory requirements of MaRisk or DORA. [4]

In addition, the 24/7 operation of RPA bots enables continuous monitoring of critical processes and a prompt response to potential risks. These audit trails are of great value for GRC functions (governance, risk controlling and compliance) as they ensure the traceability and verifiability of processes and thus support the fulfillment of compliance and audit requirements. In combination with strict access and authorization controls, RPA solutions help to minimize operational risks in automated processes and support a robust ICS. [5]

Another advantage of RPA in these functional areas is its ability to process large volumes of data quickly and reliably. Many tasks in reporting, risk management or controlling require the aggregation, analysis and preparation of data from different source systems. RPA bots can automatically extract and transform this data and convert it into the required target formats. This significantly speeds up time-critical processes such as regulatory reports or risk reports and improves data quality and error robustness.

Use cases in the regulatory areas

RPA is proving to be an extremely effective tool for managing a large number of (sub-)processes in the areas of compliance, risk management, reporting and controlling at banks. Some specific application examples are presented in more detail below.

The graphic illustrates how RPA links the areas of controlling, risk management, reporting, compliance and IT controlling in bank management. For example, RPA enables the automated creation of management reports in controlling, the monitoring of risk indicators and limits in risk management, the automated transmission of regulatory reports in reporting and the efficient implementation of KYC and AML processes in compliance. [6]

Through automation, banks can manage complex regulatory requirements in these areas more efficiently and at the same time benefit from cost savings, higher process quality and essential employee relief. [7]

Outlook

The use of RPA in the regulatory areas of the banking sector is likely to become even more important in the coming years, driven by the increasing complexity of regulatory requirements and the ongoing digitalization imperative. A key trend here is the increasing convergence of RPA with artificial intelligence (AI), machine learning (ML) and natural language processing (NLP), which 90% of RPA providers will be offering in the form of generative, AI-supported automation by 2025. [8]

In the context of regulation, compliance and risk management, RPA represents a promising approach to increasing the efficiency, quality and agility of processes, making it a strategic enabler for an effective GRC framework in the increasingly complex and dynamic environment of the banking sector.