With the increasing complexity of reporting requirements from regulators around the globe, the cost and resource burden of regulatory reporting has soared in recent years. Struggling to keep up with the constant pace of change, financial firms must find ways to meet expanding data requirements more efficiently and accurately while strategically evolving their data architecture to improve performance and drive growth.
Many financial services organizations are still wasting a significant amount of time and skilled resources preparing regulatory reports. Without an automated system that performs data quality checks and eliminates data silos, banks can’t be confident that their regulatory submissions are accurate without spending countless hours reviewing the reports. Accessing data at the desired level of granularity is another challenge because different systems capture data at different levels—for instance, loan systems capture data at the account and transaction level, loan origination systems capture data at the enquiry level, and credit card systems capture data at the card and transaction level. Analyzing data at a consistent level of granularity allows financial institutions to gain a 360-degree understanding of their operations, customers, and markets. It enables them to view data in context and identify relationships, patterns, and trends that might be missed if the data was aggregated or disaggregated in an inconsistent manner.
To address these issues, financial services organizations are redefining their approach to risk calculation, regulatory reporting, and compliance as a holistic process and seeking end-to-end automation and governance—from data capture and analysis to reporting, including the final mile submission to regulators.
The following architecture demonstrates how we can combine Oracle components and capabilities, including advanced analytics, AI, and machine learning, to create a comprehensive data platform for regulatory reporting and risk calculation that facilitates data integration, data quality, standardization, processing, lineage, and agility. The data platform provides financial institutions with a robust foundation to help them meet regulatory requirements, create timely and accurate reports, and perform effective risk calculations.
There are three main ways to inject data into an architecture to enable financial services organizations to streamline risk calculation and regulatory reporting processes while improving accuracy.
Data persistence and processing is built on three (optionally four) components.
The ability to analyze, learn, and predict is built on three technologies.
Oracle Data Platform can help financial services organizations keep up with the swiftly changing risk management and regulatory reporting landscape, manage the increasing complexity of reporting requirements from regulators worldwide, and ensure they have access to data at the correct level of granularity. Oracle’s solution provides an integrated environment and framework for managing risk data that reduces the valuable time and resources organizations must dedicate to preparing regulatory reports. With an automated solution that applies quality rules and eliminates data silos, organizations can have confidence in their regulatory submissions and better understand, manage, and minimize risk.
Learn how to better meet customer demands with a 360-degree view of your customers using a sophisticated data platform.
Learn how to manage financial services operations more efficiently using a data platform that helps improve performance with machine learning.
Learn how Oracle Data Platform for financial services can help you reduce risk and improve fraud detection and compliance in this use case.
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