There are various factors that have contributed to the growing use of AI and machine learning in financial sector. Read on to know more about it…
There are various factors that have contributed to the growing use of Artificial Intelligence (AI) and Machine Learning (ML) in financial sector. Some of them are faster processor speeds, lower hardware costs and easy access to computing on the cloud. In essence, this means an increase in the availability of infrastructure both to analyze data as well to extract insights and develop modeling capabilities. The other factors are availability of Artificial Intelligence and Machine Learning tools. Yet another factor is the proliferation of data in digital formats from multiple sources such as online search trends, viewership patterns and social media that contain financial information about markets and consumers.
Some of the scenarios in which AI/machine learning could become valuable in the context of financial services is to automate routine processes like application/case intake and answering customer calls using chatbots. Other opportunities for improvement include risk management analysis, productivity improvements, and enhanced decision-making and solve significantly complex problems. Most financial institutions also find it increasingly necessary to keep up with competitor adoption of AI and ML as well as to increase valuation as a Fintech startup.
The rise of AI in the financial industry proves how quickly it’s changing the business landscape even in traditionally conservative areas. Here are just some of the most popular examples of AI in financial services.
It’s difficult to overestimate the impact of Artificial Intelligence 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.
Artificial Intelligence in finance is a powerful ally when it comes to 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.
For several years, Artificial Intelligence has been very successful in battling financial fraud and the future is looking brighter every year, as Machine Learning is catching up with the White collar and cyber criminals. AI is especially effective at preventing credit card fraud, which has been growing exponentially in recent years due to the increase of e-commerce and online transactions. Fraud detection systems analyze clients’ behavior, location, and buying habits and trigger a security mechanism when something seems out of order and contradicts the established spending pattern.
Banks employ Artificial Intelligence to reveal and prevent various types of financial crimes and money laundering. Machines recognize suspicious activity and help to cut the costs of investigating the alleged money-laundering schemes. One Case study reported a 20% reduction in the investigative workload.
Robo-advisors are increasingly providing automated investment advice as well as offer brokerage and investing services. They also provide services around other investments like retail investors. Some of them even provide zero commission based trading. This allows customers of any income-level access to management of their personal finances. Robo-advisors use Machine Learning algorithms to determine and manage Exchange-Traded Funds (ETFs). This helps customers with a portfolio of diversified, low-risk investments according to their needs.