By Bryan Dougherty, Head of Product and Technology, Arcesium
Regulators are not backing down on demands that firms modernize disclosure models and enhance data governance practices. A prime example of this is the $900 million wire transfer mistake made by Citibank in 2020, which resulted in the Office of the Comptroller of the Currency (OCC) slapping the financial institution with a $400 million civil money penalty. Old, defective software was identified as the culprit of this costly error and Citi assured regulators it would improve risk management, data governance, and internal controls. However, just four years later, the institution was once again handed a $135 million dollar fine for their inadequate remediation efforts in addressing data quality management issues.
As financial institutions race to keep up with the pace of digitalization and automation, regulators like the SEC, FINRA, and others are hot on their tails, ensuring their rulemaking remains relevant to today’s rapidly evolving market landscape.
For both buy-side and sell-side firms to stay ahead and remain on the right side of these regulations, they must answer the call to modernize their disclosure models, operate using clean and accurate data, and enact meticulous data governance standards that lead to airtight compliance and reporting. Institutions that move to transform data management processes will be able to manage regulatory risk, enhance operational efficiency, swiftly meet investor demands, and achieve better returns.
The dynamic backdrop of regulatory mandates
The monumental swing in attention by regulatory bodies towards reporting and disclosure methods has been a long time coming. As technology has raced ahead, financial institutions are pushed to capitalize on machine learning, automation, and AI, to build efficiencies and keep up with the competition. However, as markets continue to grow in complexity, spanning across multiple asset classes, venues, and regions, investors today conduct business under a cloud of potential risks. These range from liquidity, compliance hurdles, trade manipulation, and operational vulnerabilities. Regulators view these shifts in the market as a mandate for greater oversight of an industry in flux, seeking to protect investors and, ultimately, the entire financial system.
More oversight means more accountability; more accountability means extensive reporting and disclosure rules. Regulators have made forceful strides with new proposals and intensified enforcement of rules like Regulation Best Interest, the financial recordkeeping and communications law, the revised Form PF, and the recently implemented T+1 rule, which requires trades to be settled one day earlier on the next business day, instead of two days post-trade. Every rule demands heightened accuracy and promptness of financial reporting and disclosure, and both buy-side and sell-side firms need to prepare.
Outdated models unearth the risk of untrustworthy data
To avoid regulatory scrutiny, potential fines, and reputational damage, firms need to address modernizing their disclosure models and tech infrastructure, as was true in the Citibank case.
Firms’ legacy data management platforms and traditional manual spreadsheet processes are unable to properly ingest data from market data vendors, service providers, third-party applications, cloud marketplaces, and internal applications such as accounting, CRM, risk tools, and internal databases. This data fragmentation causes the connection between middle- and back-office operations and front-office aspirations to become disjointed. A firm that relies on bad, inaccurate, and untrustworthy data is flying blind with overly complex workflows, will be slow to incorporate new strategies or asset classes, and will be unable to make informed portfolio decisions. Firms who want to merge into popular private debt vehicles or deploy advanced blended private-public strategies find vexing operational obstacles. They cannot efficiently meet all investor and regulator reporting demands for performance, attribution, and risk. Moreover, disparate datasets lead to costly errors, leaving compliance departments’ reporting and disclosure practices hung out to dry.
Data governance: The backbone of risk control
Updated infrastructure and superior data quality are some of the key steps in meeting regulators and investors’ demands. However, for firms to keep up with evolving rule changes, be audit-ready, and sustain long-term success in meeting reporting and disclosure regulations, they must pursue rigorous data governance and risk management control standards. Citi’s $135 million dollar penalty is a not-so-subtle message from regulators to remediate data quality management and governance. Institutions that formalize clear processes around reporting and disclosure methods that ensure data integrity, accountability, and compliance will be more agile in flagging and rapidly correcting mistakes and in adapting to changing rules in the future.
Automation is the missing piece to efficient and accurate reporting
When faced with increasing regulations, firms can only keep up with expanding reporting requirements if they lean on automation tools for help. Automation can support teams in streamlining data collection and calculation processes, reducing manual errors, and ensuring it’s done efficiently. Teams prioritizing their automation and data processes will be miles ahead in meeting increased reporting demands with greater ease and precision, lessening the chance of costly fines.
Building a golden thread of data
Financial firms should join regulators in their quest for better, more robust reporting. And this is not merely to follow rules, but also to drive alpha. However, driving alpha is no easy task, especially when the investing community is experiencing a state of data explosion and the rise in popularity of opaque and multi-asset-class strategies. The upsurge of less data-transparent private markets continues with an AUM totaling $13.1 trillion, a 20% yearly growth since 2018. Multi-asset strategies have led to asset-class convergence, with its correlation of asset classes causing significant data complexity. The post-trade operations teams who continue to be tasked with manually pulling this raw data from spreadsheets to validate P&L calculation, monitor timestamps, verify trade allocations, and handle transaction reporting are at a disadvantage, burning time and making unavoidable errors.
It’s vital for firms to operate from a single golden thread of investment lifecycle data that is validated and organized. Data management technology should speak directly to the unique needs of modern trading, enabling firms to pull precise information from disparate datasets, as well as the aggregation of holdings, performance, cash flows, risk analytics, and reporting data. Firms must have modernized systems in place that possess the ability to comprehend and interpret data across different asset classes and jurisdictions. These tools provide a crystal-clear picture to their analysts, investors, and regulators alike.
A triple competitive edge: modernization, quality, and governance
In fiscal year 2025, the SEC says it will continue to encourage investor testing on both existing and proposed disclosures to retail investors; and it will advocate for innovative, and more investor focused approaches to disclosure. The procrastination runway in financial services for digital transformation has run out. The SEC stated in its 2025 exam priorities that it will review RIAs and RICs’ compliance programs, including reviewing their consistency of portfolio management practices and disclosures, and issues associated with market volatility.
To have any hope of building secure risk controls, fully compliant reporting, and innovative disclosures, both buy-side and sell-side firms will have no choice but to move away from outdated manual processes and upgrade outdated and inefficient tech infrastructure. Data governance approaches that ensure financial data is standardized, interoperable, and effectively managed to meet evolving regulatory expectations is a prerequisite to growing AUM and staying competitive, now and down the road. The integration of advanced data management systems that operate on clean, organized, and accurate investment lifecycle data is no longer a cost center; it’s a profit center, and a lucrative one. The benefits are extensive, from providing the ability to make informed decisions and facilitate operations to launching new lines of business, as well as meeting regulatory compliance requisites and strengthening investor confidence – ultimately, lining up the path for both short- and long-term growth.
The Era of Data Modernization, Quality and Governance is Here
By Bryan Dougherty, Head of Product and Technology, Arcesium
Regulators are not backing down on demands that firms modernize disclosure models and enhance data governance practices. A prime example of this is the $900 million wire transfer mistake made by Citibank in 2020, which resulted in the Office of the Comptroller of the Currency (OCC) slapping the financial institution with a $400 million civil money penalty. Old, defective software was identified as the culprit of this costly error and Citi assured regulators it would improve risk management, data governance, and internal controls. However, just four years later, the institution was once again handed a $135 million dollar fine for their inadequate remediation efforts in addressing data quality management issues.
As financial institutions race to keep up with the pace of digitalization and automation, regulators like the SEC, FINRA, and others are hot on their tails, ensuring their rulemaking remains relevant to today’s rapidly evolving market landscape.
For both buy-side and sell-side firms to stay ahead and remain on the right side of these regulations, they must answer the call to modernize their disclosure models, operate using clean and accurate data, and enact meticulous data governance standards that lead to airtight compliance and reporting. Institutions that move to transform data management processes will be able to manage regulatory risk, enhance operational efficiency, swiftly meet investor demands, and achieve better returns.
The dynamic backdrop of regulatory mandates
The monumental swing in attention by regulatory bodies towards reporting and disclosure methods has been a long time coming. As technology has raced ahead, financial institutions are pushed to capitalize on machine learning, automation, and AI, to build efficiencies and keep up with the competition. However, as markets continue to grow in complexity, spanning across multiple asset classes, venues, and regions, investors today conduct business under a cloud of potential risks. These range from liquidity, compliance hurdles, trade manipulation, and operational vulnerabilities. Regulators view these shifts in the market as a mandate for greater oversight of an industry in flux, seeking to protect investors and, ultimately, the entire financial system.
More oversight means more accountability; more accountability means extensive reporting and disclosure rules. Regulators have made forceful strides with new proposals and intensified enforcement of rules like Regulation Best Interest, the financial recordkeeping and communications law, the revised Form PF, and the recently implemented T+1 rule, which requires trades to be settled one day earlier on the next business day, instead of two days post-trade. Every rule demands heightened accuracy and promptness of financial reporting and disclosure, and both buy-side and sell-side firms need to prepare.
Outdated models unearth the risk of untrustworthy data
To avoid regulatory scrutiny, potential fines, and reputational damage, firms need to address modernizing their disclosure models and tech infrastructure, as was true in the Citibank case.
Firms’ legacy data management platforms and traditional manual spreadsheet processes are unable to properly ingest data from market data vendors, service providers, third-party applications, cloud marketplaces, and internal applications such as accounting, CRM, risk tools, and internal databases. This data fragmentation causes the connection between middle- and back-office operations and front-office aspirations to become disjointed. A firm that relies on bad, inaccurate, and untrustworthy data is flying blind with overly complex workflows, will be slow to incorporate new strategies or asset classes, and will be unable to make informed portfolio decisions. Firms who want to merge into popular private debt vehicles or deploy advanced blended private-public strategies find vexing operational obstacles. They cannot efficiently meet all investor and regulator reporting demands for performance, attribution, and risk. Moreover, disparate datasets lead to costly errors, leaving compliance departments’ reporting and disclosure practices hung out to dry.
Data governance: The backbone of risk control
Updated infrastructure and superior data quality are some of the key steps in meeting regulators and investors’ demands. However, for firms to keep up with evolving rule changes, be audit-ready, and sustain long-term success in meeting reporting and disclosure regulations, they must pursue rigorous data governance and risk management control standards. Citi’s $135 million dollar penalty is a not-so-subtle message from regulators to remediate data quality management and governance. Institutions that formalize clear processes around reporting and disclosure methods that ensure data integrity, accountability, and compliance will be more agile in flagging and rapidly correcting mistakes and in adapting to changing rules in the future.
Automation is the missing piece to efficient and accurate reporting
When faced with increasing regulations, firms can only keep up with expanding reporting requirements if they lean on automation tools for help. Automation can support teams in streamlining data collection and calculation processes, reducing manual errors, and ensuring it’s done efficiently. Teams prioritizing their automation and data processes will be miles ahead in meeting increased reporting demands with greater ease and precision, lessening the chance of costly fines.
Building a golden thread of data
Financial firms should join regulators in their quest for better, more robust reporting. And this is not merely to follow rules, but also to drive alpha. However, driving alpha is no easy task, especially when the investing community is experiencing a state of data explosion and the rise in popularity of opaque and multi-asset-class strategies. The upsurge of less data-transparent private markets continues with an AUM totaling $13.1 trillion, a 20% yearly growth since 2018. Multi-asset strategies have led to asset-class convergence, with its correlation of asset classes causing significant data complexity. The post-trade operations teams who continue to be tasked with manually pulling this raw data from spreadsheets to validate P&L calculation, monitor timestamps, verify trade allocations, and handle transaction reporting are at a disadvantage, burning time and making unavoidable errors.
It’s vital for firms to operate from a single golden thread of investment lifecycle data that is validated and organized. Data management technology should speak directly to the unique needs of modern trading, enabling firms to pull precise information from disparate datasets, as well as the aggregation of holdings, performance, cash flows, risk analytics, and reporting data. Firms must have modernized systems in place that possess the ability to comprehend and interpret data across different asset classes and jurisdictions. These tools provide a crystal-clear picture to their analysts, investors, and regulators alike.
A triple competitive edge: modernization, quality, and governance
In fiscal year 2025, the SEC says it will continue to encourage investor testing on both existing and proposed disclosures to retail investors; and it will advocate for innovative, and more investor focused approaches to disclosure. The procrastination runway in financial services for digital transformation has run out. The SEC stated in its 2025 exam priorities that it will review RIAs and RICs’ compliance programs, including reviewing their consistency of portfolio management practices and disclosures, and issues associated with market volatility.
To have any hope of building secure risk controls, fully compliant reporting, and innovative disclosures, both buy-side and sell-side firms will have no choice but to move away from outdated manual processes and upgrade outdated and inefficient tech infrastructure. Data governance approaches that ensure financial data is standardized, interoperable, and effectively managed to meet evolving regulatory expectations is a prerequisite to growing AUM and staying competitive, now and down the road. The integration of advanced data management systems that operate on clean, organized, and accurate investment lifecycle data is no longer a cost center; it’s a profit center, and a lucrative one. The benefits are extensive, from providing the ability to make informed decisions and facilitate operations to launching new lines of business, as well as meeting regulatory compliance requisites and strengthening investor confidence – ultimately, lining up the path for both short- and long-term growth.