Machine Learning in Banking – Opportunities, Risks, Use Cases
Information is the 21st Century gold, and financial institutions are aware of this. Armed with machine learning and artificial intelligence technologies, they have the opportunity to analyze data that originates beyond the bank office. Financial companies collect and store more and more user data in order to revise their strategies, improve user experience, prevent fraud, and mitigate risks. In this article, we will talk about how Artificial Intelligence and Machine Learning are used as well as the benefits and risks of these solutions.
Artificial Intelligence in Banking Statistics
- According to a forecast by the research company Autonomous Next, banks around the world will be able to reduce costs by 22% by 2030 through using artificial intelligence technologies. Savings could reach $1 trillion.
- Financial companies employ 60% of all professionals who have the skills to create AI systems.
- It is expected that face recognition technology will be used in the banking sector to prevent credit card fraud. Face recognition technology will increase its annual revenue growth rate by over 20% in 2020.
Benefits of Machine Learning in Banking
Artificial Intelligence and Machine Learning are able to provide unprecedented levels of automation, either by taking over the tasks of human experts, or by enhancing their performance while assisting them with routine, repetitive tasks. But what are the main benefits of Machine Learning in Banking? This question has many possible answers, and what is even more interesting, the number of answers will continue to expand as the newest technological solutions hit the market. Here is an attempt to highlight the most important ones:
Greater Automation and Improved Productivity
Artificial Intelligence and Machine Learning can easily handle mundane tasks, allowing managers more time to work on more sophisticated challenges than repetitive paperwork. Automation across the entire organization will ultimately lead to greater profits.
Personalized Customer Service
Automated solutions with Big Data capabilities can track and store as much information about the bank’s customers as needed, providing the most precise and personalized customer experience. Optimizing the customer footprint allows banks to leverage analytical capabilities of Artificial Intelligence and Machine Learning to detect even the most subtle tendencies in customer behavior, which helps create a more personalized experience for each individual client.
More precise Risk Assessment
Having an accurate digital footprint of each customer also can help banks reduce uncertainty for managers working with individual clients. The automated system is more accurate than a human in such areas as analysis of loan underwriting, eliminating any possible human bias.
Advanced Fraud Detection and Prevention
This is probably the top benefit of AI/ML for any financial institution because there has historically been, and will continue to be, criminals who are devising methods to commit financial fraud. Fortunately, there are currently a wide range of proven methods and techniques of ML-powered Fraud Detection on the market. We will talk about all of them in greater detail in this article, and you will find out how to make your bank even more secure thanks to these technological innovations!
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