Finance

Automation in the Financial Services Industry

Automation in the Financial Services Industry:

According to a survey conducted by the Global Association of Risk Professionals (GARP) and SAS, it was concluded that four out of five respondents (81%) agreed that AI technologies are already benefiting their institutions. More than 50% of people said that they are using AI systems for optimization and forecasting. The survey drew more than 2,000 responses from the financial services industry, including investment banking/securities and wealth/asset management. 

   

 

 “AI helps risk and marketing teams to more effectively pool quantities of credit data and develop products more tailored to customers’ needs. Also, AI enables them to use all the data, making results more precise, and so helping to reduce risk.”, says Mahdi Amri, national services leader – Canada for Omnia AI, Deloitte’s AI practice. 

 

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Automation, productivity, and data insights topped the list of anticipated benefits from AI by survey respondents over the next three years. “Machine Learning is all about getting data. We must have good-quality data and large amounts of it to train these models.”, says Katherine Taylor, SAS senior data scientist. She added, “The topic of AI and its core methodologies is being taught in schools and the hype for AI can overshadow its practical benefits.”

 

On the other hand, Amri says, “The business must first understand what the model is saying, and when it is convinced that the model is good and improves upon what is already being used, then companies can seek approval from the regulators to use the models.” The survey also concluded that more than 75% of respondents are concerned about the transparency and interpretability of their firms’ AI models. 

 

Amri lists three main AI building blocks: vast quantities of data, including alternative and unstructured data; large-scale data processing and storage capability, increasingly practical today with cloud technology; and rigorous algorithms for analytical and predictive accuracy. He further adds, “Banks’ logistic regression models are not dead yet, and they are proven and easy to understand. But machine learning and other AI techniques can enhance those models.”

 

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As financial firms are adjusting to the new technology, and if explaining it to the regulators is a tough task, companies may use AI to create “challenger models.” These models “are not replacing production models,” Taylor says, “but they can provide a lot of insight into improving those existing models.”

 

The survey concludes by indicating that AI technology is here to stay and can become an important tool while conducting Automation in the financial Services industry, particularly toward the advancement of risk management functions such as risk monitoring,  modeling, and analytics. 

 

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Written by: Priyank Singal

About the Author: I am a current MS in Global Finance student at Fordham University in New York City. Being a CFA Level II Candidate and a Computer Engineer, I am a fintech enthusiast who loves playing around with large amounts of datasets. I have a varied set of interests from running marathons to cooking to reading books.

 

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