Bank management: Change is achievable

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Teaser

The professional demands on bank management have increased significantly in recent years. In addition to ever new external requirements, the internal provision of steering information and the rapid implementation of steering impulses to cope with dynamic crises and successfully shape transformations also pose an enormous challenge. With „suboptimal“ support from existing IT systems and data quality problems, bank controlling, instead of becoming a driver of business development, threatens to become a competitive disadvantage. Added to this is the cost pressure that bank control functions also have to face. In this situation, it is worth taking a look at new technologies and new project approaches for the realization of more efficient bank management, which have not existed in this form in the last two decades – during which the problems of today’s IT landscape have grown.

The challenge for bank management

Despite all the uncertainty surrounding the further course of the COVID 19 pandemic, one thing is certain: the problem loan wave is yet to come. Particularly in the SME sector, business models will be at existential risk. While expanding volume was an option during the low-interest phase in order to generate income despite low margins, this will hardly be the case in the foreseeable future in view of rising risk positions and risk provisioning. This will make the general conditions for bank management functions even more difficult. Increased pressure to reduce costs, growing competition from near-, non- and new-banks, the need to invest in digital transformation, and rising regulatory requirements are already major challenges for bank management and are further intensified by current developments. The areas of bank management are now particularly challenged; they must reduce „production costs“ but deliver more in return: faster, but always comprehensible and reliable analyses and reports, as well as evaluations of options for action via scenario analyses with key figures from all disciplines of bank management, to name just a few points.

The status quo of bank management brings with it various challenges in meeting these demands. Data delivery and preparation is not standardized for the various pillars of bank management, corrections are made in isolation, and methods are implemented multiple times. This regularly leads to time-consuming, sometimes manual processes of reconciliation between the key figures of the finance, risk and reporting areas. These problems are exacerbated by a fragmented system landscape. Overall, this means that the IT landscape for bank management cannot meet expectations in terms of the „user experience“ and quality of the results. In addition, there are major efforts to meet compliance requirements such as the traceability of key figures and calculations of externally defined stress scenarios. These challenges, which have accumulated over decades, are frequently encountered above all because a fundamental modernization and redesign of bank management processes and IT is seen as difficult to master and incalculably expensive. Therefore, not only new elements of the target world are important for evaluating the opportunity of modernizing bank management, but also new methods for successfully carrying out the project.

The opportunity for modernization

The crucial question, then, is: what new means are available today that are much better than in the past two decades at solving these bank management problems? These are essentially three points: Standardization, technology and agile project approach.

Standardization

A major expense and complexity driver in bank management are internal models and procedures, e.g. for complex models for credit or market risk or economic capital models. A large number of non-harmonized reports and ratios can also be counted in this area. After decades of developing and maintaining internal methods, there is now a trend toward greater standardization. This includes, on the one hand, internal management consistently aligned with regulatory defined key figures and, on the other hand, the willingness to align processes and methods with the quasi-standard of established software providers. In addition to the need to respond to the current challenges mentioned above, this development is driven both by supervision and by internal lessons learned. Specific methods for internal management have not been able to convincingly demonstrate their benefits for individual institutions and the banking sector as a whole. Where this evidence can be provided, e.g., for residual value risk assessments in leasing, such models will certainly continue. On the other hand, it makes sense to manage with standard ratios when they are the scarce variable – like regulatory capital. In addition, in recent years the supervisory authorities have drastically restricted the possibility of using specific internal models to reduce the required capital backing.

Technology

Today, technological developments provide quite significant new opportunities for bank management. In data management, classic data warehouse concepts can be consistently combined with new data architectures of data lakes. One aspect of this is the stronger decoupling from the source systems through the delivery of the complete data of these systems to the bank management. The transformation of the actually required data to the central data model then takes place within the IT of the bank control. This means that the scope of the data modeling can be expanded if further data is required within the IT systems of bank management alone, which significantly reduces the duration and complexity of the implementation. In addition, exploratory analyses on the entire data sets are possible, e.g., to identify unusual patterns that may indicate data quality problems or even compliance issues. Furthermore, Artificial Intelligence methods can be used in bank management for analyses, preparation of reports and also management of data quality, in addition to the usual analytical tools. Fields of application for Artificial Intelligence are, for example, forecast models, the automated evaluation of internal and external text sources or the realization of Intelligent Workflows, in which employees receive exactly those tasks that actually require their expertise. Today, „as a service“ models are already established for the provision of business applications and other software components. This means that specialized services can be used for operation and maintenance of the application landscape, which can be flexibly adapted to actual requirements. The trend described above toward using the standard functionality of the software also has a positive effect here. The fewer proprietary additions and modifications to the software components used are included in the IT landscape of bank management, the more cost-effectively and smoothly operation and maintenance can be realized.

An agile project approach

Fundamental changes in bank management, from business applications and data management to processes, are considered extremely high-risk and expensive projects, which means that overall bank management is often seen as „too complex to change.“ At this point, first of all, the technological capabilities mentioned above are an important part of the solution. Cloud technology allows the target architecture to be made available quickly and without investment in additional hardware at the start of the project. Productive operation can then later take place both externally and behind the company’s own firewall („hybrid cloud“). This flexibility allows a much faster project approach that can be managed at short notice. Another point is the project methodology itself. The agile approach to projects already established in many banks today is very well suited to managing the risks of a bank management transformation. In the scaled application of agile methods for a large project, „punctures“ (in the context of „program increments“) are an essential element. In the punctures, domain-oriented use cases from data delivery to report are realized at an early stage, starting with a small data width and reduced functional scope. This means that the entire chain from business requirements to implementation and integration is completed in just a few weeks. This scaled agile approach thus ideally supports the rapid identification of problems in the project and early corrections. The procedure for testing and ultimately accepting a new bank control system is also developed and trained step by step.

Conclusion

The modernization of bank management with its methods and processes, its application landscape and data supply is necessary for banks to survive in the dynamic and challenging market environment. Today, however, such a transformation is also possible in a leaner way and with much greater certainty of success. A greater willingness to standardize and the use of modern technologies and project methodologies are key success factors. The goal of efficient and high-performance overall bank management, which becomes a competitive advantage, with a central, uniform data budget and supported by modern technologies such as Hybrid Cloud and Artificial Intelligence, can be achieved in this way.

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