With a predicted Compound Annual Growth Rate of 32.6% from 2021 to 2030, the market for AI in banking sector, which was valued at $3.88 billion in 2020, is expected to reach $64.03 billion by 2030. As the application of Artificial Intelligence gains popularity in banking, financial institutions are building on their existing solutions to solve increasingly complex challenges. Many of the changes in banking are a result of AI’s development: the impact of AI is clearly visible in various areas of the sector, from decision-making processes, through forecasting, to customer experience. Here are 3 reasons why AI in banking is set to disrupt financial services for the better.
AI in banking helps with fraud prevention
The application of artificial intelligence in banking can be a great asset with regard to fraud prevention (such as unauthorized transactions, phishing scams and identity theft).
In order to accurately detect fraud, financial institutions must first understand typical customer behavior. For this purpose they can leverage machine learning, analyzing data from past financial and non-financial transactions, and categorizing customers into different profiles. Profiles are helpful as they provide an up-to-date overview of the user’s account and can be leveraged to make predictions on future behavior. The same account could be attributed several profiles based on the recurrent activity of the user, as information gets updated in real time after each transaction. As transactions are made, AI determines whether or not they fit a pattern or whether there is an anomaly to notify to the user.
Thanks to ML in banking it is possible to analyze thousands and thousands of transactions per second, with neural networks taking this capability to the next level by making decisions in real time. The number of flagged transactions is detected in real time and a list of those that require further investigation by a human counterpart is drafted. Since investigating and prosecuting fraud claims can take a lot of time, equipping agents with the right tools is crucial with the goal to increase efficiency.
AI eases the lending process
Determining a customer’s credit score helps lenders understand how eligible they are for certain loans. At the moment, this process is either rules-driven or manual, and is different in all bank branches. That’s why, scoring a customer’s credit helps drive the lender’s business and operations. AI in banking uses multiple data points that analyze customer behaviors, income-tax histories and any other transactions they have made. They can then determine risk scores for each customer, helping the lenders get a clearer picture of who is more likely to repay a loan. The data that AI in banking uses to make this analysis include the behavior of the customers on digital platforms of lenders and affiliates. Artificial Intelligence can process this data and model it to output credit scores for each customer, in real time.
The credit scores allow lenders to approach an existing customer or a potential client and sell them approved loan products. The lender’s messages to their customers and prospects can also be personalized, helping their loan books grow faster. AI in banking can therefore reduce the risk that lenders have to take every time they lend money to their customers. It also standardizes the credit scoring process in all bank branches.
Conversational AI improves customer relationships
Financial institutions also need to make investments in conversational AI to accelerate their digital transformation efforts, and many credit unions appear to be ready to do so. In Cornerstone Advisors’ “What’s Going On in Banking” study, 1 in 4 credit unions said they plan to deploy a chatbot in 2023. Also, as quoted in an article by Forbes, Digital Banking Didn’t Kill Bank Branches – But Chatbots Will: the chatbot experience might have the potential to improve faster than the branch and real life contact center experiences. However, looking past 2023, banks will require more than just “chatbot” deployment.
To respond to the need for frictionless digital service and engagement, financial institutions will need to implement intelligent digital assistants. Whereas chatbots can be defined as rule-based systems which can perform routine tasks with general FAQs, digital assistants will be fully equipped with natural language understanding and will be able to support a wider range of use cases with greater ease of deployment and onboarding, and a higher quality, more sophisticated conversation capability.
What are the pitfalls of AI in banking?
Just as it has benefits, AI in banking can entail a few pitfalls. Emerging technologies can be risky due to their immaturity and the limited time they have been in action, and specifically, the risks of using AI are concerned with the fact that this field is evolving so quickly.
AI bias: one of the main risks in using AI in banking. The application of artificial intelligence in banking can entail AI bias, which is due to how decision-making AI models are developed. When training the machine learning model, humans might bring their own assumptions. To avoid this, once a model is trained, it must be continuously updated to accommodate new factors.
Customer mistrust. In addition to AI bias, financial services companies must be mindful of customer trust when using AI and ML in banking tools. Soon enough, however, Conversational AI could allow an automated system to react to human speech in real time, creating dynamic, lifelike discussions, which will help consumers trust voicebots more and more.
The great promise of AI in banking
Despite its challenges, the application of artificial intelligence in banking is proven to be changing the financial institutions and banking industry for the better:
- AI in banking provides banks with a channel to identify suspicious activity quickly.
- AI in banking allows financial institutions to make better investments and accommodate a broader customer base.
- AI in banking upscales the experience for clients with its increased accessibility and flexibility.
As analyzed above, from enabling frictionless onboarding to preventing payment fraud, the uses of AI in banking are far-reaching. With time, artificial intelligence tools will be responsible for an increasing number of processes in banking institutions and apps. However, the real core of AI in banking capacity is to achieve top-notch personalization. By leveraging AI and ML in banking, banks can transform every interaction with their customers into a meaningful conversations, and turn into their most valuable partners when it comes to their financial education and wellness. How can we help you in this?
