The Growing Impact of AI in Finance
If there is a technology thats paying dividends, its AI in finance. Artificial intelligence has given the world of banking and the financial industry as a whole a way to meet the demands of customers who want smarter, more convenient, safer ways to access, spend, save and invest their money. According to Insiders Intelligent report 80% of financial institutions are highly aware of the potential benefits presented by Artificial Intelligence.
In his paper Tom Lin stated that Artificial Intelligence is an existential component of modern finance. The progress and promise realized and presented by artificial intelligence in finance has been far remarkable. It has the ability to make finance cheaper, faster, larger, more accessible, more profitable, and more efficient in many ways.
AI is reordering the Financial Services landscape, navigating an entire industry back to its customers. Fintech is forecast to achieve a compound annual growth rate (CAGR) of 25% through 2022, reaching a market value of $309B. The broader financial services market expected to reach $26.5T by 2022, achieving a 6% CAGR.
AI and machine learning are the catalysts that every organization in Financial Services is either adopting or evaluating to break down silos, automate processes and remove barriers between themselves and their customers. In short, AI deliver valuable new data and insights about customers and their needs that traditional Financial Services firms could not see before.
AI is helping the financial industry to streamline and optimize processes ranging from credit decisions to quantitative trading and financial risk management.
Implications of AI on Finance
Risk management
It’s difficult to overestimate the impact of AI in financial services when it comes to risk management. Enormous processing power allows vast amounts of data to be handled in a short time, and cognitive computing helps to manage both structured and unstructured data, a task that would take far too much time for a human to do. Algorithms analyze the history of risk cases and identify early signs of potential future issues. These models can also make sure that more reliable information is obtained for use in future models.
Analyzing real-time activities in any given market or environment; the accurate predictions and detailed forecasts it provides are based on multiple variables and vital to business planning.
Page Acknowledgments: Tom Lin, Business Insider, Towardsdatascience, Forbes