– Commencing in January 2022, Arabesque Asset Management Singapore Pte. Ltd. (Arabesque) will be embarking on an AI project that focuses on financial knowledge graphs, understanding data bias with application to transfer learning, and general machine learning approaches for financial analysis and modelling.
– The project will be used to enhance Arabesque’s existing AI business activities such as identifying alpha opportunities.
– An engineering and research unit dedicated to the advancement of AI and automation in asset management will be established as part of the project.
– Arabesque is hiring AI engineers and researchers in Singapore that will help establish Singapore as a leading centre for AI in finance.
SINGAPORE, Nov. 12, 2021 /PRNewswire/ — Arabesque today announced that it will embark on an AI project which focuses on financial knowledge graphs, understanding data bias with application to transfer learning, and general machine learning approaches for financial analysis and modelling. This will enhance Arabesque’s existing AI business activities such as identifying alpha opportunities.
View of downtown district and Marina bay skyline with purple sunrise in Singapore
Commencing in January 2022, the project will run for two years and support Arabesque in developing capabilities in cutting-edge areas of AI. The work undertaken by the AI research unit is expected to enhance Arabesque’s existing business activities such as developing data engineering capabilities, improving the accuracy at which alpha opportunities are identified for its investment strategies, and utilising new unstructured sources of data as inputs into its financial models.
The project is supported under the Financial Sector Technology & Innovation – Artificial Intelligence & Data Analytics (FSTI – AIDA) scheme, which aims to strengthen the AIDA ecosystem in the Singapore financial sector. The FSTI – AIDA scheme is funded by the Financial Sector Development Fund, administered by the Monetary Authority of Singapore.
The project will involve the establishment of an AI engineering and research unit based in Singapore, which will be dedicated to the advancement of AI and automation in asset management. Arabesque will be hiring a team of AI engineers and researchers in Singapore as part of the new unit.
The team will be led by Arabesque’s Dr Qasim Nasar-Ullah, a co-founder of Arabesque’s AI business, who will take on a new role at the firm’s Singapore office. All work undertaken by the AI unit will be used to enhance Arabesque’s existing AI capabilities, and will be utilised to help establish Singapore as a world-leading centre for AI in asset management.
Dr Yasin Rosowsky, CEO of Arabesque AI, said:
“Artificial intelligence will play a vital role in financial services over the next decade and will help accelerate the shift towards more sustainable capital markets. We are honoured to have been awarded the FSTI – AIDA grant and look forward to working on developments that will advance the innovative application of AI in asset management.
We are excited at the prospect of building an AI engineering and research team in Singapore to deliver cutting-edge AI solutions for the fintech ecosystem of Singapore and beyond.”
Headquartered in London, Arabesque Asset Management is part of the Arabesque Group, which aims to advance sustainable finance through investment solutions, market-leading data assets, AI, and financial technology expertise.
About the Arabesque Group
The Arabesque Group is comprised of three businesses, Arabesque Asset Management, Arabesque S-Ray, and Arabesque AI, that work together to advance sustainable finance through investment solutions, AI and financial technology expertise. Established independently in 2013, Arabesque’s mission is to enable clients and other stakeholders to implement sustainability in their investments and financial decision-making. Arabesque counts many of the world’s leading banks, asset managers, asset owners and custodians as clients. arabesque.com
Photo – https://mma.prnasia.com/media2/1686388/Arabesque.jpg?p=medium600
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