End-of-day (EOD) reporting is the most important part of any banking application maintenance with respect to BAU operations. Banks use automated EOD (or intraday) job plans to generate reports for regulatory monitoring, business users, downstream stream systems etc., and to deliver them to stakeholders for various purposes. Such jobs or reports can be classified into:
‘Report’ as a term has been used for decades to represent data file delivery. This definition in the functional sense, however, simply means published data, as opposed to processed information which can facilitate the decision-making process.
At present, the EOD reports are usually delivered to different stakeholders in formats XLS, CSV, DAT, or TXT as partially processed data.
Fig 1: Sample report format in current setups
A sample of a report in the figure above represents data as requested by the end user. In order to offer the end-user information which is useful in decision-making, however, further processing steps are required for making the process time-inefficient and including the possibility of human error.
In the reminder of this blog, we will look at the possible solutions to this optimization problem. It is important to understand what the end-user use the reported data for and the purpose of the reporting, i.e. what is the end-result the user wants to achieve with this process step?
An institution and its stakeholders can achieve high benefits from integrating data science methodologies into the EOD reporting. Data science methodologies can be used to clean, analyse and visualize the data for better representation of the final summary, providing useful information to the user.
Complete business logic for analysing reports and suggesting feasible solutions automatically, which the end-user would otherwise need to apply manually, can be implemented using Python (or any other programming language) and Data Science methodologies.
With this solution, we can generate an enhanced report, which will include new sections with insightful information, in addition to the data that the end-user has been receiving so far.
The data in the report has been analysed, processed, and summarized in a way that is easy to understand. This representation would enable the end-user to make faster and better-informed decisions based on the delivered facts and figures.
To implement the new report generation by integrating the existing reporting components with data science methodologies, changes to the data extraction, processing and visualization are required. The current report delivery methods to downstream and regulatory bodies, however, do not change. The end-users of the modified reports are internal stakeholders and internal management who mainly use reports for taking business decisions.
The following points should be considered during the implementation:
1. Reduced report processing time
2. Improved report quality
3. Enhanced data quality
Designing end-of-day end-user reports which automate processing of raw data and create visual aids to support internal stakeholder decision-making can dramatically reduce the extent of manual processing and thus decrease errors. Such process-oriented solutions can build on existing structures. They can be developed using design thinking methodology and bring substantial benefits in terms of speed and quality of information for decision making.
Capco has a longstanding history in the financial services industry. We understand what data is needed, where it comes from, who needs it and for what purposes, and we are experts at how to represent data visually. Contact us to find out more about data science methodologies for better, faster and more profitable business decision making.
Andreas Pfeil, Senior Partner
T: +49 69 9760 9106
M: +49 172 165 3932