Financial institutions are currently struggling with mounting regulatory demands in various areas of risk management, which require detailed reporting. For example, the European Central Bank's Risk Data Aggregation and Risk Reporting (ECB RDARR) emphasises the need for a streamlined and efficient reporting mechanism amid evolving regulatory landscapes. The ECB considers RDARR as "the worst-rated sub-category of internal governance; in the 2022 SREP cycle, the ECB has observed an increasing number of outstanding supervisory measures in this area, most of them triggered by on-site inspections''.
Meanwhile, business users seek real-time data analysis for their risk management. These challenges echo in various risk management domains, spanning market risk, credit risk, liquidity risk, and climate risk.
In the current economic landscape, global financial entities are forced to establish a comprehensive risk data framework, adapting reporting chains to align with operational efficiency and cost-effectiveness. The difficulties lie in accommodating organisational complexities, fitting in with legacy IT systems, and ensuring information reliability while adhering to increasingly granular regulatory requirements. As well as enabling production teams to effortlessly and independently handle vast amounts of data without technical constraints, steering clear of performance degradation.
Let’s explore how a strong and scalable data platform that combines data management and real-time analytics is the only way to fulfil demanding regulatory requirements like RDARR.
From data lakes to insights: The critical data management challenge
Extracting datasets from data lakes or diverse IT systems, often accumulating substantial volumes of data, is the primary challenge in data manipulation. Without a data management strategy and a scalable environment, storage costs and unpredictable queries can escalate significantly. Overcoming the inflexibility of legacy systems is crucial for an efficient extraction process, ensuring users can access relevant data swiftly without compromising adaptability or quality. Data preparation is intricate and demands a control framework to monitor input flows and address data quality issues early on. Local entities with diverse formats highlight the importance of pre-processing data for time savings.
Manual adjustments to correct or enrich data within the dataset are a daily concern. This consumes a significant portion of reporting teams' production time and carries operational risks. A big data solution proves itself valuable by enabling tracking, securing data changes, and providing an audit trail for the reporting process. In market risk, correcting metrics or inputting just-traded deal metrics manually is common, necessitating a system that logs all changes for transparency and accountability. This comprehensive approach ensures an efficient, adaptable, and quality-focused data manipulation process in the dynamic landscape of financial institutions.
Metrics in motion: Bringing transparency to risk analytics
In the complex realm of financial data management, calculation is key and involves key metrics, such as ES on FRTB, LCR-NSFR on liquidity, and NII on IRRBB. Often, these calculations occur outside the big data platform, potentially disrupting production and yielding insufficient aggregated metrics for in-depth investigation. To ensure consistency and precision, calculations should originate from the official engine or an embedded tool capable of dynamic metric simulation, facilitating immediate comprehension of data transformations, corrections, or stress scenarios. This flexibility proves crucial in market risk adjustments, FRTB desk regrouping, liquidity classification alterations, and IRRBB mortgage/deposit projection simulations.
A comprehensive, open solution, not only allows smooth calculations but also grants visibility into the process, avoiding the opacity of aggregated calculations—a critical aspect of transparency.
Moving to the analysis and certification stage, where multiple analyses must be performed and reported metrics validated before submission, banks often face challenges due to a lack of standardisation across teams. A scalable data platform with unlimited historical data empowers AI algorithms to alert analysts to unusual metric behaviour, offering clear explanations and streamlining the certification process. It enhances efficiency, data and report quality, and inter-reporting reconciliation. Ownership of the certification is maintained through a single data platform, tracking the validation process via a universal workflow accessible to all stakeholders.
The final leg of the risk data journey involves restitution, where the results of analyses or metrics are presented through dashboards for internal or external stakeholders. Traditionally, this process involves exporting data from various databases, which poses operational challenges and consumes non-value-added time. Integrating dashboarding directly into a single data platform enhances efficiency and flexibility, ensuring a seamless and value-driven restitution process. In essence, this comprehensive approach to financial data management tackles challenges at every stage, fostering transparency, efficiency, and quality in a dynamic landscape.
Opensee’s identity has been to build such a scalable data platform that combines data management and real-time analytics, dedicated to financial institutions. We believe a user-friendly big data solution coupled with business analytics is the only way for financial institutions to bring the most agile support to users, carry out advanced analysis, remove any tasks with low value, and in the end get closer to the RDARR ECB’s requirements.
