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The Evolution of Enterprise Data for ESG
Until recently, ESG data was regarded as a peripheral concern among data managers. Today, it is a vital part of an institution’s operations, needed by front- and back-office teams, putting huge new responsibilities on chief data and sustainability officers. Incorporating this new generation of data into institutions’ infrastructures, however, has been a challenge. Many on-prem…
Solving the Enterprise Data Challenge for Institutional Investors
Institutional investors are evolving under pressure from a range of emergent externalities that are prompting many to assume new roles and processes, some of which have traditionally been the preserve of asset managers. Enabled by data-led technology, they are making more direct investments and managing their portfolios in-house. At the same time, they are streamlining…
How Banks Harness the Value of Cloud for Scale and Agility
Financial services are recognizing the benefits that data-led processes can bring and are undergoing digital transformations that are putting data at the heart of their operations and decisions-making processes. They are seizing on data as an asset whose value can be unlocked through technology to increase revenue and reduce costs across the entire enterprise. With…
The Business Case for Master Data Management Transition Within Financial Institutions
Master data management ensures the creation of a “single source of truth” of information for banks and financial institutions that not only provides that the data remains intact but also makes it available and useable across the entire enterprise. Once the information foundation of golden data is established with MDM, the data can be used…
Achieving a 360-degree View of Risk with Data-Driven Insights and Strategies
Technology and data have reached a level of sophistication that is enabling financial institutions to calculate and manage the risks to which their activities and investments are exposed. Leveraging the two, however, is a challenge; if the data isn’t properly managed, the technology that identifies patterns and insights may be unable to operate optimally. One…
Driving Data Adoption Throughout an Organization via Self-Service
Financial institutions and corporations across the board are seeing value in the data sets they generate through their business activities. But harnessing this data to provide valuable insights for internal business teams can be a challenge. One approach is to adopt a self-serve data delivery model that empowers consumers and ensures they get access to…
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…
Next-Generation Network and Vendor Management: mitigating operational risk in relationship networks
The complex network of relationships that financial institutions have built with multiple partners has created a huge operational burden, resulting in significant operational risk as well as financial and compliance risk. Dating back decades, many of these relationships are now managed as a separate business function, costing organisations millions of dollars. Add in regulatory obligations,…
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 –…
Data integrity: Your key to confidence in a complex regulatory environment
Despite the recent challenges of the Covid pandemic, regulators have made it clear that they will not accept reporting errors, particularly those that are related to data. Enforcement is more stringent, scrutiny is more in-depth, and the pressure is on to ensure – and demonstrate – that the data underpinning your reporting and activities is…