In the world of banking, with the increasing pressure in managing risk along with growing governance and regulatory requirements, it is mandatory for banks to enhance their services towards more exceptional and better customer service. Banks can leverage the power of AI and ML in banking, along with data science acceleration, to enhance customer’s portfolio offerings.
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.
Let’s take a look at some of the ways in which AI & ML are impacting the banking industry
Improved Decision Making
One of the benefits of machine learning in banking is improved decision making. As compared to traditional methods, artificial intelligence helps banks to calculate credit scores accurately. The main reason ML can do this is that it can provide an objective evaluation without any bias. The huge amount of data collected from the potential borrower assists banks in making better decisions.
Better Risk Management
These technologies reduce risks for both customers and banks through accurate reporting. Predictions can be made based on transaction history after giving credit to customers. Bank employees have more insights into credit risk testing. Early detection of errors and the availability of potential future risks help the banking industry to prepare in advance.
Fraud Prevention
Credit card fraud is a huge problem in the banking industry. Machine learning can significantly lower the number of fraudulent activities. The majority of fraud occurs when customers pay for products, whether online or offline. It prevents this from happening in several ways and one of the most prominent methods is facial recognition. It can be used to confirm that the person using a credit card is its rightful owner.
Improved Customer Experience
With technology changing almost every aspect of life, consumers are looking for better services and eager to get the same from banking institutions. At the same time, banks that can provide more security and a personalized experience would attract more clients. Customers want digital banking products that are easy to use. One way in which ML improves the overall experience and services is by reducing the time it takes to make credit decisions and banking operations. Loan applications which usually take weeks to be made and processed can now be made within days. AI can make an unbiased analysis based on several credit factors.
Internal Operational Solutions
Machine learning has greatly changed internal operations for the better in the banking sector. Automation reduces the time staff spends on redundant tasks. Therefore, resources can be allocated towards improving the overall experience. Robots perform routine tasks with minimal risk of errors. So a bank can provide efficient solutions while automation gives employees the chance to pay more attention to the core functions and tasks.
One of the most important advantages is internal operational solutions. Robots can go through a customer database at record time, thus reducing the need for employees to do this manually.
Marketing and lending solutions
ML and AI collect data and also search for specific patterns that help banks make better marketing predictions. Examples of predictions that ML can make include:
- Changes in currencies
- The best investment ideas
- Credit risks
- The optimum loan agreement for a client
This data assists a bank in deciding where to invest, thus increasing their revenue. It also provides more accurate information on how to attract new clients.
Personalization
Banks can benefit from ML since it helps adopt great management in an organization, enhancing customer satisfaction, and providing more personalized and simplified operations and support. Big data can give their clients and potential consumers a personalized experience in the banking field. ML is all about creating safe yet accessible financial services and data.
Marketing
The ability of AI and Machine Learning models to make accurate predictions based on past behaviour makes them a great marketing tool. From analysing the mobile app usage, web activity, and responses to previous ad campaigns, machine learning algorithms can help to create a robust marketing strategy for banks and finance companies.
Cost Reduction
Machine Learning allows financial organizations to identify weaknesses in processes and organize the work of full-time employees more efficiently. The simplest example is chatbots, which can successfully cope with advising clients on simple and standard issues. Chatbots also don’t require payment for their work! Besides the fact that working with ML allows companies to reduce costs, it is logical that it also helps increase profits due to improved customer service.
Conclusion
As the market continues to demand easier and faster transactions, emerging technologies, such as artificial intelligence and machine learning, will remain crucial for the banking sector.
Innovations based on AI & ML have empowered the banking industry significantly. As a result, banks and financial institutions are now offering better financial services to customers with excellence.
We at AppleTech have the experience and expertise in building portals, mobile and web applications using AI for the banking industry as well as many other domains. Come to us with your idea and watch it develop into a solution.
