Driven by the exponential growth of data and the impact of the pandemic, enterprises have rapidly adopted Artificial Intelligence (AI) as a strategic tool to gain a competitive edge and enhance their business models. Recognizing its potential, they seek to leverage AI to reduce dependency on human workforce and unlock new revenue streams while cutting costs. As a result, enterprises are striving to develop improved AI tools and technologies. However, implementing AI has its own set of challenges such as availability of high-quality curated data and responsible AI implementation. In this report, we discuss the importance of high-quality curated data in the success of enterprises’ AI initiatives. We explore different aspects of preparing high-quality data such as data annotation, using synthetic data when real data is insufficient, incorporating a human-in-the-loop approach, and ensuring data inclusivity and mitigation of biases. Additionally, the report examines emerging trends in AI data services that enterprise should consider before making implementation decisions. Scope  All industries and geographies Contents In this report, we examine: AI / Machine Learning (ML) life cycle Features and benefits of data annotation and labeling Importance of AI-assisted data annotation and synthetic data Human workforces’ role in the AI life cycle Data annotation and labeling services ecosystem Emerging trends in AI data services Membership(s) Artificial Intelligence (AI) Sourcing and Vendor Management

Financial services firms are facing strong headwinds in managing their financial risks, due to tightening regulations, increasing customer expectations, rising costs, and lack of techno-functional expertise. To tackle these challenges, enterprises are looking to transform their Financial Risk Management (FRM) functions by leveraging FRM platforms, which help firms manage risks related to credit, market, interest rate, and asset liabilities in an integrated manner. These platforms enable enterprises to automate their risk functions and manage their risk data, calculations, visualization, and reporting. In this research, we analyze 10 leading FRM technology platform vendors, focusing on their vision, capabilities, investments, and market impact. Our assessment is based on Everest Group’s proprietary transaction intelligence database, public disclosures, and discussions with enterprises, technology vendors, and service providers. Scope We have assessed the capability maturity and market adoption across all geographies for the following 10 FRM platform vendors: Actico, AxiomSL, Calypso, CSI, Finastra, FIS, Fiserv, Murex, Openlink, and SAS Contents We study the following topics in this report: Background and definition of FRM platforms Market trends and demand themes for FRM platforms in BFS Assessment and summary dashboard of FRM platform vendors Characteristics of FRM platform vendors Profiles of FRM platform vendors Membership(s) Banking & Financial Services (BFS) - IT Services (ITS)