Originally published by A-Team Insights, 24th June 2024.
By Adam Quirke, Business Development Lead, Financial Services, InterSystems UK & Ireland.
With the financial sector’s regulation landscape continuously evolving, compliance officers at financial institutions play a crucial role in maintaining integrity and trust. They maintain compliance despite a growing burden of responsibilities and the increasingly acute problem of legacy technology, which lacks agility, requires significant maintenance efforts, introduces operational inefficiencies and may delay implementing required regulatory changes.
This reliance on outdated systems is becoming a serious threat as regulators become more demanding. Manual processes remain common at a time when new regulation demands more detail and greater frequency of reports. Multiple disparate data sources often struggle to connect and work together to manage the growing mountain of data required for analysis, insight and reporting in this sensitive area.
By leveraging modern data technology, compliance teams can significantly enhance efficiency and avoid continuing to toil away on lengthy and complex processes that eat up unnecessary amounts of their time. A survey of 375 asset management companies around the world commissioned by InterSystems found two-thirds of firms employ between six and nine people just to process data.
Data management challenges have intensified
Asset managers need to think hard about new approaches to data. The survey found that 44% of respondents see improving responses to regulators’ requests as one of their major data management challenges. Some 54% state data errors between disparate sources is their number one driver for investing in data management.
Many firms also struggle to obtain data that is current. Only 38% use data less than a day old for reporting, with processing unstructured information from a wide variety of sources, errors, and manual methods all contributing to the problem.
Firms need to innovate and adopt a smarter architecture
Innovation is now essential for compliance. Data fabric architecture is rising in popularity as it is one of the most effective approaches to deliver accurate, harmonised data for reporting in near real time. As an architectural layer, it simplifies complex data infrastructures without replacing systems, maximising current investments in technology. The data fabric layer sits on top of a firm’s infrastructure, delivering a unified version of data from high-volume internal and external sources without time-consuming manual processes or complicated wrangling. It streamlines compliance work and supplies the kind of timely and trusted data compliance officers need for reporting.
The truth is that without greater automation in compliance processes, and more efficient data management, costs will rise. Recruitment of skilled and expensive staff is becoming necessary to cope successfully with the burdens of compliance. The Thomson Reuters’ 2023 Cost of Compliance Report, which reviewed 1,374 regulators in 190 countries, found a third of respondents with compliance-related responsibilities at financial institutions in the UK, EU, and US expecting compliance teams to grow and the overall costs to increase. Many also see how compliance will steadily have more involvement in cyber resilience, corporate governance and the setting of risk appetite.
Coping with these extra demands is difficult when firms have legacy systems and applications. These create enormous complexities and prevent organisations from accessing and processing the clean standardised data needed for regulatory reporting. Harmonising data and rendering it usable and meaningful can be time-consuming and costly when firms require data spread across spreadsheets, data warehouses, data marts or data lakes.
Significant regulatory changes will demand more reporting
A quick scan of the regulatory horizon shows why such innovation is necessary. The entire financial services world faces a tide of new rules. EMIR 3.0, and Basel 3.1 will introduce more than 80 new data-field requirements. For Basel 3.1, the Prudential Reporting Authority (PRA) plans to introduce 19 new COREP (Common Reporting) templates and revise 12 of those already in use. Last year’s wide-ranging UK Financial Services and Markets Act also introduced significant updates to the regulatory reporting framework (also giving the Bank of England and PRA expanded enforcement powers).
The UK and EU are not alone in developing regulations. The US Securities and Exchange Commission (SEC) has adopted final Private Fund Adviser Rules. These rules include five regulations and are backed up with a large volume of summary materials.
Environmental, social and governance (ESG) obligations are also an increasingly strong factor in regulation. As of May this year, for example, UK Financial Conduct Authority (FCA) regulated firms must review product types and disclosures in the context of ESG to eliminate greenwashing. These changes will increase workloads substantially.
The benefits go beyond compliance
When an asset management firm transforms its data architecture, the gains extend beyond the compliance function. The firm can build machine learning (ML) models to inject greater efficiency into risk management and to streamline back- and middle-office processes. They can also more easily meet bespoke client requirements and provide rapid and detailed insight into performance, fees and charges, changing market conditions and competitors’ activities.
By investing in smart data fabric, which takes the data fabric approach a step further by embedding a wide range of analytics capabilities, asset managers can enable compliance teams to excel at a time when their firm may well be struggling with low margins and sluggish growth.
The compliance function needs agility and new technology just as much as line-of-business teams, which is why asset managers should think seriously about smart data fabric. It will give them the compliance capabilities they need to streamline onerous new regulatory requirements and achieve higher margins and greater internal efficiency.
