In the second of a three-part blog series, we will examine how regulator-driven reporting standards and data integration initiatives are attempting to propel reporting, accounting and auditing into the digital age, and what this means for your data management strategy.
Of the coming wave of regulatory initiatives that we foreshadowed in part one of this series, perhaps none of them are more urgent than reporting requirements that institutions face in just the coming year. On January 1, 2020, every institution in the European Union (EU) will be required to use eXtensible Business Reporting Language (XBRL) for filing reports, essentially mandating they be machine-readable.
Meeting these new standards is important, but if you treat reporting as a stand-alone issue, you will miss the greater opportunity of reducing operational costs and risks. The benefits of adopting standards like XBRL go beyond the accurate transmission of electronically formatted reports. The underlying taxonomy, or structured classification of the financial information, can lead to better data governance and analysis when it is harmonized with all other regulatory data sets.
Reporting software can help address the immediate need, but it does not necessarily further your digital regulation strategy. Creation of reports with the help of reporting software still involves inefficient, cumbersome manual organization and analysis of voluminous financial information.
By focusing only on regulatory reporting applications, financial firms will continue to struggle in meeting constantly evolving compliance mandates. Thousands of firms, including many large institutions, are still being cited for non-compliant reporting. The resulting financial penalties continue to grow.
Your goal should not just be about response to reporting requests, but rather the ability to meet emerging requirements easily and cost-effectively.
To do this, financial firms should focus data governance strategy on optimizing performance in two main areas:
At a recent RegTech Summit, these two areas were highlighted by financial firms as the biggest challenges and opportunities for RegTech adoption (see images below). When asked about the biggest barriers to RegTech adoption, participants identified “integration with legacy systems” as the largest impediment. As for the biggest drivers of RegTech adoption, “improving data quality” was picked the most.
Image 1 – Poll question: What are the biggest barriers to RegTech adoption at your firm?
Image 2 – Poll question: What are the biggest drivers for RegTech initiatives at your firm?
On-demand reporting is achievable only if you are using a data platform that can integrate and harmonize data, both structured and unstructured, from anywhere in your technology infrastructure. Using a multi-model approach, a data hub platform provides maximum flexibility to integrate, store and quickly access all of your regulatory data and metadata. The data integration hub remains in constant sync with source systems. Curated, non-curated and source data are immediately available for compliance audits.
Your reporting solution should flow from that data architecture, ensuring agility and flexibility, supporting all business functions and leveraging the same integrated data sets. Rapid querying across all data is also critical for meeting always-changing reporting demands. This includes the ability to access historical records of changes made to your data, preserving data integrity without arduous extract, transform and load (ETL) processes that consume valuable time.
As a financial organization dealing with increasingly complex regulatory demands, you’re likely in a constant battle to ensure that your data is complete, accurate and available. The quest for quality data is the quest for effective management of compliance and risk. Worldwide, this “dirty” data problem can cost institutions up to 25% of total revenue. If you are consistently struggling to prepare data, align data sources and put the right data into the hands of compliance officers, fines from regulators are the tip of the compliance cost iceberg.
In addition, data quality could bring an end to the high cost of compliance. The promise and possibility of regulatory data straight-through processing (RegSTP) could slash compliance overhead by automating the interactions between firms and regulators. Such a system in the future would rely on integrated, harmonized data.
To improve data quality, companies need to harmonize data across different siloed sources and harness metadata for data provenance and lineage. A key component of this process is using natural language searching, data modeling and machine learning to identify patterns and anomalies. Cleaning up your data is an investment worth making. An organization that adheres to strong data governance and data-cleansing practices can reduce the cost of compliance, reduce the risk of incurring fines from regulators and even experience a positive impact on revenue.
The need for speed will keep increasing, so your RegTech solution should meet the regulatory reporting requirements of the day and be capable of adaptive growth in both scope and scale.
Seeking to improve speed and flexibility of access to large quantities of regulatory data from financial institutions, the Bank of England partnered with NTT Data Corporation on a solution aimed at doing just that. With a MarkLogic Data Hub at its core, internal and external users can quickly import, store, analyze and visualize data, even in the complex demands of XBRL.
Many organizations are discovering the power of the data hub as a cost-effective method for putting previously siloed data at user’s fingertips—paving the way for always-on digital regulation compliance.
In the next and final post of this three-part blog series, we will lay out the path to digital regulation, discuss its strategic implications and explain the promise of the data hub—a feasible solution to an end-state where machine-executable data integration (RegSTP) is required to improve compliance effectiveness and efficiency across the regulatory ecosystem.
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