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Creating an Enterprise-Wide Data Fabric to Underpin Digital Transformation in Capital Markets
As they seek to adopt a data-driven approach to their business operations across the enterprise, capital markets firms need to put in place a common data fabric that embeds their single view of the truth, and to underpin analytics, reporting and regulatory processes. But legacy data systems often are not fit for purpose; often fragmented…
B2B Data Marketplaces and Beyond
Financial institutions and corporations often generate huge quantities of data as a by product of their core activities. This data – details of historical transactions, customer interactions and metadata for referring to instruments, counterparties or entities – can provide valuable insights for industry participants, and form the basis of a meaningful data sales business. But…
Sanctions Screening for Indirect Investments – The Buy Side’s New Compliance Challenge
The global political climate over the past few years has sparked a jump in the use of sanctions to attempt to influence the behaviour of players in the geopolitical landscape. While sell-side firms are familiar with sanctions and have long been required to monitor the securities they trade, own or recommend to clients to ensure…
Practical Data Strategies for meeting ESG Obligations in Financial Services
The ESG investing landscape is poised to become more defined, as competing definitions, standards and regulatory initiatives start to converge. The impact of ESG will be felt far and wide across the financial services community, which will face practical challenges in developing and implementing an ESG strategy that is both effective and avoids box-ticking –…
Embracing Automation and Collaboration Tools to Inject Reference Data into the Trade Lifecycle
Digital transformation in the financial services sector has raised many questions around data, including the cost and volume of reference data required by each financial institution. Firms need flexible access to the reference data required to ensure workflows can proceed without interruption. ‘One-size-fits-all’ bulk data licensing models are increasingly less fit for purpose. Emerging solutions…
Can ‘Observational Learning’ Help Improve Data Quality?
Data quality is a pre-requisite for financial institutions seeking to automate their operations. But given the huge volumes of transaction data, often in a wide array of formats, it is very difficult to achieve. Can newer AI-based techniques such as observational learning actually help or are they just hype? This white paper explores: What observational…
How to leverage the LIBOR transition to improve your data management game
The final goodbye to Libor has been described as the Y2K moment for data managers in financial services – and with the phase-out slated for the end of 2021, firms need to act now to ensure a smooth transition. The replacement of Libor with alternative benchmarks presents an immense challenge for financial institutions, especially given…
Adopting AI for Superior Reconciliations
Firms’ reconciliation and exceptions management processes are manually intensive, expensive and prone to error. With rising compliance costs and greater competition narrowing margins in financial services, firms are looking to streamline their reconciliations processes through automation, giving them the opportunity to reduce the number of exceptions they manage and the time it takes to deal…
Data as the Catalyst for Innovation in Asset and Wealth Management
Fund managers and wealth management firms are being squeezed between downward pressures on sources of revenues and upward pressures on costs. Firms are facing a migration to passive investment funds, with some research suggesting a one-third drop in active management fees by 2023. Meanwhile, the ongoing regulatory onslaught is adding to costs. Under pressure to…
Applying Emerging Technologies to Real-World Business Challenge in Financial Services
Today’s world of technology is evolving at lightning speed for financial services firms. Terms like artificial intelligence (AI), machine learning (ML) and distributed ledger technology (DLT) bandied about, technology conversations seem to be all about hype. The reality is that financial services firms need to understand the impact that these new technologies could have and…