Regulatory compliance is one of the most important priorities of financial services and insurance organizations. It has become increasingly complex to navigate the multi-dimensional operating environment that spans the regions in which they work. Amidst a wave of technology changes including the proliferation of cloud solutions and the explosion of data volumes, organizations must rethink how they use data to meet their regulatory reporting requirements. This includes reducing the operational burden without inhibiting business growth or the customer experience.
In this blog post, we will cover some of the challenges financial services and insurance companies face, and how Cloudera provides a complete platform with data services that simplify and accelerate regulatory reporting for financial institutions.
Key Challenges in Financial Services
Over the last few decades, there has been a dramatic change in both the regulatory landscape and the technologies used for regulatory reporting. This landscape is dynamic and constantly evolving, while the data required to satisfy auditors is siloed and difficult to access and integrate. Because of this, compliance teams face several key challenges.
New regulatory obligations
Innovations in business models and financial products are two driving forces behind the growing complexity of regulatory reporting requirements. For example, banking regulators globally have introduced a wave of new regulatory requirements in response to the explosive demand for digital assets among retail and institutional investors.
Constant regulatory change
It is not just the new regulations that introduce change, however. Regulators consistently revise existing regulations and request more information, greater reporting granularity, and increased reporting frequency. As an example, the latest regulatory framework for the management of market risk, the Fundamental Review of the Trading Book (FRTB), requires more historical data, more complex regulatory measures, and more granular reporting than previous methodologies.
Scarcity of regulatory compliance talent
As the Wall Street Journal suggests, hiring good people in compliance departments is getting harder, as younger professionals gravitate towards front-office jobs. That introduces several limitations to financial services and insurance organizations that need to scale their regulatory reporting capabilities with even scarcer talent.
Data accuracy and competition challenges
The rise of software solutions that compose the regulatory reporting stack has led to many data silos and Extract, Transform, & Load (ETL) pipelines used to move data between data warehouses and data lakes, source systems, and proprietary tools. All of that inefficiency has contributed to data accuracy issues among systems, as it is virtually impossible to keep different data sources in sync and resolve discrepancies in data and metadata among tools and environments.
Performance and scalability limitations
The technologies initially used to build regulatory reporting solutions, such as relational databases and proprietary regulatory reporting solutions, lack distributed processing capabilities for growing volumes of data and computational needs.
High technology costs
The legacy, appliance-based platforms that many financial institutions use to deliver regulatory reporting solutions cannot scale in a cost-efficient manner to meet growing business needs due to the high-capital outlays required for purchasing proprietary hardware. As a result of these challenges, financial institutions must re-evaluate their data analytics architecture based on a new set of criteria that meets the needs of compliance teams while preserving business agility and delivering a unified customer experience across channels.
Figure 1: Complexities of Traditional Architectures
Requirements for a Modern Regulatory Reporting Platform
To meet dynamic and evolving regulatory requirements in a cost-efficient manner, organizations need a modern data architecture that delivers five major capabilities:
A unified approach for structured and unstructured data: A modern data architecture must adopt a unified data management approach to break down silos across data warehouses, data lakes, and other databases and analytical solutions.
A scalable data movement and processing model: A modern data platform should scale to meet existing and future storage and processing requirements for regulatory reporting related to moving data between systems and calculating regulatory measures.
A flexible and elastic deployment model: Regulatory processing happens at specified periods of time and it involves data generated predominantly by on-premises systems. Because of this, a modern data platform should offer a hybrid deployment model, using elastic cloud resources to perform complex regulatory calculations at the end of the reporting period.
End-to-end security and governance: Regulatory reporting involves sensitive information (e.g., over-the-counter trades between financial services institutions) that needs to be safeguarded from unauthorized access. A modern data platform should offer fine-grained security and governance to control access for different stakeholders who participate in the regulatory reporting process.
Automation with AI and ML: A modern data platform should provide the capability to meet reporting requirements and build automation into the reporting process, with AIOps tools to provide governance, observability, and repeatability over the model development process, and model explainability.
Cloudera Delivers a Modern Regulatory Reporting Platform
Cloudera delivers the only true hybrid platform for regulatory reporting, offering features such as:
Open Table Format with Apache Iceberg
Apache Iceberg lies at the heart of Cloudera as the common table format for both structured and semistructured data, offering interoperability with Cloudera services such as Data Engineering and Data Warehousing and proprietary tools such as Snowflake. In addition, it streamlines changing and enriching complex data models for regulatory reporting, making it easy to adapt to new regulatory requirements and perform complex computations for risk models. Additionally, Iceberg enables auditability of historical data by giving data analysts a mechanism to reproduce a previous state of the data model to assess the impact of regulations or market scenarios.
Cloudera Data Engineering
With Apache Spark as its processing engine, Cloudera Data Engineering enables large- scale, compute-intensive data transformations, automated data validation, and data normalization and standardization. Beyond that, it offers several tools to streamline data operations such as deep analysis, which offers a visual interface to identify and resolve performance and Apache Airflow to schedule and manage the lifecycle of complex data engineering jobs.
Hybrid Cloud Platform
Cloudera’s platform enables organizations to leverage cloud resources in addition to their on-premises environments to execute regulatory analytics in the cloud when on-premises capacity is not sufficient. This model allows executing heavy computational tasks for regulatory reporting at the end of the reporting period by providing cloud resources in the cloud for those transient workloads. Additionally, hybrid cloud enhances resilience by offering an additional hosting environment to seamlessly run analytics applications in case of failure in an on-premises-only or cloud-only deployment, thus addressing Digital Operational Resilience Act (DORA) requirements.
Cloudera Data Flow
Cloudera Data Flow provides data integration and movement between source systems, Cloudera services, and other analytics solutions. It offers a universal data movement solution with a no-code flow designer enabled by Apache NiFi to unlock data silos from many different technologies and data sources. It can also simplify complex architectures by replacing point-to-point integrations between internal databases and regulatory systems with an enterprise message bus based on Apache Kafka.
Cloudera AI
Cloudera AI is a platform for end-to-end AI and ML model development, providing the tools required to build, train, and deploy models on-premises and in the clouds. Cloudera AI enables secure and governed AI and ML workflows with full observability over the development process. Cloudera AI supports the automation of regulatory reporting workflows to reduce SLAs and deliver consistent and accurate results.
Comprehensive Data Governance
Cloudera Shared Data Experience (SDX) provides fine-grained security for data assets across analytics services to ensure proper data access for all different roles in the organization. It also offers traceability capabilities through end-to-end data lineage to support the auditability of risk assets and processes, addressing a key requirement of regulations such as BCBS93.
Get Started with the Cloudera Open Data Lakehouse Today
Amidst a global wave of regulatory transformation, organizations need to adopt an open, hybrid data strategy using Cloudera as the foundational data management and processing platform to bring together internal and external data for regulatory processing. With over 80% of the largest global banks, four of the top five stock exchanges, and eight out of the top ten wealth management firms, Cloudera has the track record and expertise to help organizations to meet their regulatory reporting needs, regardless of scale or complexity. Learn more about how the Cloudera Data Platform can help you.
