Software and hardware level

Regulatory reporting process has some finance-specific attributes and activities that require targeted solutions. This framework investigates the problem of creating efficient financial data flow for the regulatory report’s generation. In the extension of this framework, it can be any finance-related sub-process that takes input data and with application of specific rules and definitions that are incorporated in a financial-specific software, produces outcome results in the format specified by the business. Results can be used for further data analysis, informing management about the performed operations and overall state of things or for the legal or regulatory purposes

Financial software in this step of framework can be defined as a single program or a set of tools, that receives financial data, applies some transformation and aggregation logic and then provides output in the required format. In case of the process in consideration, the input would be capital, risks and operations data and the outcome should be a generated financial report. Controllers can submit these files to the regulator system.

Such software can provide additional features, while simplifying the job of controllers. It could allow users to construct rules which define how input data would be processed in order to create a template. These rules are automatically applied to the input data, directing each data entry into the correct cells in the reports. It also calls validation or the created reports, executing pre-defined conditions that can be set up by regulatory authority or defined by the users. These checks should ensure consistency of the provided information and act as verification of the data integrity.

After data processing step, this software should provide an option of creating XBRL files. As mentioned in section 2, EBA regulator ruse XBRL format in order to automate bank operations validation against regulatory requirements. For large amounts of data, it is not possible to construct XBRL file by hand, therefore either the selected software system, or a separate solution should be used in this step.

Having all these features under one roof allows users to reduce mundane tasks of moving files from one system to another. However, as all companies have different requirements and input data format, they might still use additional software components for some of these aspects as a cost-effective solution.

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49 4.1.3. Software and hardware level

After establishing a steady workflow therefore reducing man-hours spent on transferring data from excel manually, it is time for the next automation step. This level is dedicated to efficient data storage and processing.

On the previous step diagram, the data that controllers generate, comes either from external (in the context of this process – this could be tools in other divisions, or external providers) sources, or calculated manually by controllers themselves. The downside of this approach is that it is very hard to maintain large sets of data in folders with excel files, calculate formulas by using Marcoses or separate scripts, and store the data versions in some archives, making it practically impossible to detect the original source of the data.

To resolve this problem, we can apply a set of technologies and tools (Figure 12). Firstly, all the data should be stored in a central database that can store all relevant information and allow to update it during the submission. This database may be a standalone server, owned by the company, or provided as a service. The other option is to use the regulatory reporting software, that in some cases, comes a database service, allowing to load the initial data into the system and perform all the operations from there. This approach has the advantage of reducing the amount of development and support needed to setup and maintain a separate database. On the other side, it will increase the dependency on the external software and will make it very effort consuming to switch the software provider if the need arises.

The other improvement is more finance related. In order to calculate all the data, necessary for the reporting, the company needs to onboard some sort of calculation software. This tool can be presented as a set of in-house standalone calculators, created as parts of one product, or also a module in a reporting software. This calculator should support traceability, maintain clear structure in order to be available and easy for an external auditor.

50 Figure 12: Process automation framework step 2

51 4.1.4. Analysis level

After techology was incorporated in the process to the level of almost full process automation, there is still room for improvement. On this level there are various techniques that can be introduced into the workflowю. They can help to improve the quality of calculated data, reduce tech support workload and provide management with planning and future predictions on a high-level. It is not a defined set of tools, but rather a number of opportunities that could generate value when used in the right direction. Depending on the nature of the process and company profile, only one, or a couple of these techniques should be implemented to improve the overall process performance. Here I will present some of such techniques and how they could be applied:

Cloud hosting: All the software that was overviewed in the previous steps can be provided on premise or on cloud basis. The advantage of the cloud is that it allows to expand the resources used by the company seamlessly in case of the process expansion, therefore increasing speed and stability of the software. The downside risks here is the process of sending data outside the company, because the cloud servers could be physically based anywhere in the world. This could cause problems regarding GDPR and client privacy agreements, because it exposes the data to the external provider.

Distributed processing: The amount of data that is being processed while calculating all necessary parameters is huge, and these calculations sometimes have to be executed daily or even every hour, which makes it essential to increase efficiency of computing as much as possible. Distributed computing allows to use several servers to calculate one task, splitter into several parallel processes. It is widely used on cloud servers, as they have access to multiple servers by design.

Machine learning: One serious constraint to applying machine learning in practice is the amount and quality of data that is necessary to perform valid assumptions and create models. Therefore, this step can be started only after implementing a consolidated database, that will store all the data in one place for a significant period of time. The most common application of AI in financial reporting is searching for anomalies patterns of data, to detect fraud and potential risks in everyday operations. MindBridge AI is one example of AI software producer that specializes on regulatory reporting. (31, 32)

4.1.5. Usability level Robotic process automation:

RPA is a powerful tool when it comes to process support. With several automated solutions in place the load on the tech support increases significantly, increasing man-hours spent and response time accordingly. This poses a problem as users facing a slow response to their questions could abandon the installed system and switch to fool-proof manual process instead. Robotic process automation allows

52 to simulate the basic response patterns and even simple tasks allowing to automate the most basic part of support, but reducing workload significantly.

The key feature of RPA is it’s focus on the user interactions. While BPA provides an option of automating business process with different types of components – data streaming, event processing, decision making, RPA allows to provide the users support and information they need in order to interact with the process.