Artificial Intelligence & Machine Learning
76 white papers and resources
Artificial Intelligence & Machine Learning
EDM client case study: Managing SRI-ESG data
This case study reveals how Mirabaud Asset Management is using IHS Markit’s Enterprise Data Management (EDM) platform to establish a strong foundation for the its long-term SRI-ESG (socially-responsible / environmental, social and governance investing) strategy. This includes empowering Mirabaud’s…
Data Management Trends That Can Mean The Difference Between Success and Failure
This paper explores some of the most important changes in AI and data management. Discover what these trends mean to your organization and how you can leverage them to your benefit.
AI & Risk Management: enabling factors, use cases and future challenges
This white paper examines the roots of the combination between AI and Risk Management, the main application fields nowadays and the future challenges.
Digital & data for the new normal
In this session, Paul Lewis, Global Chief Technology Officer at Hitachi discussed the top concerns and shifted priorities for CIOs and Technology leaders: how can they lead the IT transformation under the new normal? How to elevate data to be a value creator, which still be able to accommodate…
Building Artificial Intelligence in Credit Risk: A Commercial Lending Perspective
This whitepaper, drawing on recent academic evidence and business insights, provides a contemporary look at what AI and ML adoption could mean for commercial lending and credit risk assessments while also proposing different approaches to AI and ML adoption tailored to each step of the commercial…
Exploring How AI Can Help Address Contract Challenges During the LIBOR Transition
This whitepaper looks at why financial institutions should consider acting now to develop a solution that supports the use of appropriate contract language in preparation for the permanent discontinuation of LIBOR.
The Digitalization of Sell-Side Risk Management
This white paper describes a series of trends in risk management being shaped by the recent wave of volatility, onslaught of regulatory requirements, ever-increasing data, and market structure changes impacting sell-side institutions. Among them, the focus on risk system upgrades and requirements…
Centralized Data Hubs: The Key to Cracking the Data Quality Conundrum
This survey report focuses on the extent to which financial services firms suffer from suboptimal data, the degree to which they acknowledge their shortcomings and the steps they have taken or are considering to remedy the situation. It also sheds light on the specific business processes most…
The end of the batch process - how streaming technology will change the world of risk
In risk management, the need for speed has never been more pressing than during the current crisis. Batch processing has been deeply embedded in the banking industry for decades now. From processing end of day batch processes in core systems (e.g. calculating interest) to sending data downstream…
Next steps for MRM in South‑east Asia
Financial institutions across South‑east Asia face challenges assessing and measuring non-financial risks (NFRs) inherent in their business models, and are therefore concerned about regulatory scrutiny, transparency and the use of models within their businesses. SAS explores how financial…