For a modern bank, management reporting is the basis of strategic planning. Automation of reporting is of particular importance, but it comes with certain risks: data errors, regulatory sanctions, and reputational losses. There are five key rules that will help banks reduce risks, as well as turn automation into a sustainable competitive advantage.
The importance of direction automation
The banking industry is one of the leaders in digitalization, due to the strict requirements of regulators and high competition. The industry is constantly evolving: non-banks are being replaced by AI banks based on a modular, microservice, API-first architecture that allows the rapid launch of new products with a trend towards hyperpersonalization in real time. Achieving such results requires powerful analytical solutions with a high degree of detail, capable of processing information in almost real time.
The basis of such systems is data. Their lack or irrelevance paralyzes decision-making processes. Reporting in T+1 and even T0 mode has become the standard of business life, which displaces manual input and “shadow” Excel models. The comparability of indicators between business lines and organizations within groups is increasing, and the basis for scenario modeling and predictive analysis is being created. With the development of Big Data, ML, and AI, the key success factor is the ability to collect, store, and deliver the necessary data on time through DWH, Data Lake, and Big Data (enhanced by Data Governance processes and solutions).
Thus, trends set the bar high: reporting must be prompt, holistic, and reliable at the same time. However, in practice, the path to this result is often accompanied by typical and costly mistakes.
Typical mistakes and consequences
Multiple integrations. Attempts to combine a large number of systems (most of them, as a rule, are legacy systems) without a single business model lead to heterogeneity and non-normalization of data, different interpretations and omissions. The lack of a culture of master data control complicates the definition of a “golden” source of information.
Insufficient data quality management. The lack of designated data owners, standardized metrics and end-to-end change tracing, as well as structured incident management processes leads to unpredictable processes and uncontrolled information quality.
Neglect of comprehensive testing. Insufficient attention to testing at all stages of the process — from data extraction to report generation — leads to errors in logic and final figures. Parallel calculations, anomaly checks, and user testing are critically important.
Methodological inconsistency. Different approaches to calculating indicators in different reporting systems violate data comparability. For example, inconsistent rules for revenue recognition, asset valuation, and risk assessment reduce the credibility of reporting, as well as the availability level and time of data readyness.
Mistakes lead to serious consequences: financial losses, deterioration of reputation and sanctions from regulators. Striking examples are JPMorgan Chase’s losses of $6 billion due to errors in risk data and a fine of $1.9 billion imposed on HSBC for deficiencies in the AML control system.
Fortunately, the risks can be minimized through appropriate techniques and algorithms.
Five rules for successful automation
Rule 1: Clear goals and measurable effect. The goal of automation should be the business result, not the technology implementation itself. Key success indicators may include: reducing reporting time, reducing the number of manual adjustments, or improving product metrics. This approach allows to get a measurable effect and avoid building up excessive complexity. A project without clear KPIs risks becoming an expensive experiment with no payoff.
Rule 2: Data Governance is the mainstay of reporting automation: it is based not on reports and dashboards, but on managed data. Data Governance, a data management system that includes procedures, policies, standards, roles, data-lineage approaches, a master data management system, and other organizational and technological issues that enable efficient and secure use of data, can help banks do this.
Many banks use RegTech solutions that automate regulatory compliance monitoring and integrate directly into data management systems at the design stage, acting not only as a tool to respond to changes in legislation. This helps in proactive risk management related to compliance with regulatory requirements and helps to build the most effective reporting systems based on convergence of standards.
Rule 3: End-to-end architecture and a shift-left approach to control. The data architecture should ensure full traceability at all stages, from the source to the final report. This allows to identify quickly the origin of any indicators and avoid disputes about the reliability of the figures.
The key principle is the shift-left approach, that is, the transfer of quality control to the earliest stages. Instead of searching for errors in reports, it is necessary to implement checks directly at the places where the data originated. This reduces significantly the cost of improvements (the earlier an error is found, the cheaper it is to fix it) and the impact on management decisions.
To implement this approach, “Data Contracts” are used between suppliers and consumers of data, which fix deadlines and mandatory requirements for the quality of information. This transforms data management from a simple set of recommendations into a deeply integrated business function. And in the context of modern approaches to building systems based on microservice architecture, it is important to build integrations based on the API-first principle, immediately fixing agreements on data contracts with the definition of schemes, SLAs and quality requirements/metrics.
Rule 4: Comprehensive testing with user participation. It is not enough to check the correctness of calculations — it is necessary to test the data path from the original source to the report and the management decision. Testing should be multi-level, automated as possible, and understandable for business users.
The key role belongs to the users: They participate in the formulation of acceptance criteria, the development of scenarios and promptly identify emerging issues. It is important to coordinate with the business not only an acceptance testing plan, but also to create reference datasets and reports for verifying the results.
Rule 5: Phased implementation, taking into account scaling. In order for the value to reach the customer as soon as possible, it is necessary to make deliveries in short iterations. Start with the most important thing for your business — it can be Product P&L, portfolios/delinquency, liquidity, and more. Define the basic data domains (products, customers, operations, etc.), quality monitoring criteria. It is recommended to use an iterative approach with tracking metrics to demonstrate value at each stage and focus on maximizing business impact, but with the most correct and scalable architecture and processes.
Compliance with these rules not only reduces risks, but also creates the foundation for the next stage — the introduction of AI into data management and reporting processes.
Prospects for the coming years
The main trend will be the transition from intelligent automation to controlled AI processes. AI models will automatically recognize, structure, and check data for quality, and humans will connect in difficult cases. AI-based real-time decision-making will become the standard.
AI will play a special role in managing change. The systems will be able to assess automatically the impact of adjustments to methodologies or data structures on reports and business processes, that will help reduce support costs and errors.
Modern approaches will enable us to identify methodological contradictions in the algorithms for calculating indicators. By analyzing formulas, conversion code, and report usage patterns, AI will be able to find discrepancies and offer specific recommendations for improvement — refine descriptions, add adjustments, change the aggregation window, calibrate the model, unify reference books, and introduce mandatory DQ and contract checks. This will shorten the error correction cycle from weeks to days, while significantly reducing the cost of both day-to-day support and changes to methods and storefronts.
Thus, the bank’s sustainability directly depends on the quality of its management reporting. Investing in automation based on the recommendations above is a contribution to the ability to make long-term decisions and the ability to look ahead confidently.
By Tatiana Stashchuk, Director of the Financial Technologies Department of the Digital Economy League