Banking Co-Sourcing Risk Management 
Capgemini
OCTOBER 30, 2024
The role of data and AI in driving sustainability for banks Satish Weber and Tej Vakta Oct 28, 2024 Facebook Linkedin How cutting-edge tech will shape the future of environmental and social impact in banking In finance, the goal is to go green – now more than ever. What follows are key takeaways from the panel.
Infosys Consulting
OCTOBER 19, 2023
An AI-first enterprise provides hyper personalized user experience, encourages humans and machines to co-exist, and exploit data (or the lack of) in an innovative and responsible ecosystem. LinkedIn The post A short guide to AI-first wealth management appeared first on Infosys Consulting - One hub. He is also a certified FRM.
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TMS
FEBRUARY 28, 2023
Berlin’s greatest advantages are the reasonable rent prices and a wide range of co-working spaces that attracts many entrepreneurs and individual workers. Their success is almost certainly guaranteed by the professional logistic, development, and management teams they are working with. All this in only 5 years!
Capgemini
JANUARY 22, 2025
Banking, asset management, and insurance companies are facing increasing financial risks due to climate change. Understanding climate shifts has become essential to assessing their financial impacts, and the physical risk on banking and insurance portfolios.
Capgemini
JUNE 26, 2024
In addition, proactive threat mitigation, robust risk management frameworks, and diligent third-party risk management are essential to DORA compliance. DORA explicitly refers to ICT risk and sets rules on ICT risk-management, incident reporting, operational resilience testing and ICT third-party risk monitoring.
Capgemini
NOVEMBER 18, 2024
As banks and insurance companies navigate an increasingly competitive and complex business landscape, harnessing the power of Gen AI can unlock significant business value. Governance and trust: Creating robust governance frameworks helps organizations maximize AI’s potential while mitigating risks.
Capgemini
JUNE 24, 2024
It’s integral to a satisfactory AML program, but rife with errors, delays, improper decisioning, inadequate recordkeeping, and outsized costs, and increasingly difficult for financial institutions to manage properly. As with any AI system, a huge consideration is comprehensive, quality data, given that the models rely so heavily on them.
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