AI is making inroads into the financial industry through multiple channels. The investment industry has seen a rise in machine-learning models in front-office functions such as quant trading, electronic market making, and producing research. Retail banks are increasingly seeing machine-learning algorithms deployed for credit scoring and consumer lending, while insurers use machine learning for granular risk pricing. And pretty much every type of financial institution can use ML in customer analytics and fraud detection.
Deploying AI across business functions brings benefits ranging from prosaic to game changing, which in turn also depend on the overall digital and data maturity of the organization. Aida Mehonic explores some of the most common applications of AI in finance and shares the typical challenges of data transformation and AI adoption that financial institutions face.
Dr Aida Mehonic is a Programme Manager for AI at The Alan Turing Institute. Previously, she was a Principal at ASI Data Science where she has led the delivery of data science projects for investment funds, multinationals, government and NGOs.
She holds a PhD in Theoretical Physics. Her research into the physics of tumour formation was published in Nature and she won a Bronze Medal at the International Physics Olympiad. After her PhD, she worked in quantitative roles in financial markets, most recently as a Quant Strategist at J.P. Morgan Investment Bank covering credit markets in the No.1 ranked research team in Europe according to Institutional Investor.
She’s a regular speaker at conferences and round tables, including O’Reilly’s A.I. Conference, Strata Data, Newsweek’s AI in Capital Markets, Battle of the Quants and others.
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