What are the examples of AI use in banking? For instance, it can be woven into the eKYC procedures to make them faster and more pleasurable for the customer. Or, generative AI may be implemented into customer service tools to help agents answer tickets more efficiently. It might also be utilized for intelligent product recommendations, tailoring the messages to customer preferences, or improving overall data security. Do you wish to learn more? Then read on.
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The Use of AI and ML in Banking and Finance: Examples
So, without any further ado – let’s look at the examples of how AI is being used in the banking sector.
Customer Onboarding Optimization
The first example can be found in the Polish SGB bank, which uses AI to make customer onboarding simpler. How does it work?
- The customer agrees to the terms and conditions.
- The customer takes a photo of themself.
- The AI system uses biometrics to compare the client’s selfie and ID card – the client is requested to make a few hand gestures to ensure that it’s them.
This is quite important since onboarding is one of the most critical steps on the road to acquiring a customer and people tend to give up on this stage, never completing the process. By using AI, it’s possible to make onboarding (and eKYC) easier, faster and hence more attractive to potential customers.
What is more, it is also possible to use AI and ML in banking onboarding in one more way – to quickly analyze the data from the previous financial institution and hence create a profile of the customer.
Customer Segmentation
Another use of AI and ML in banking can be observed in customer segmentation. It is possible to utilize data collected on each client to segment them, leading to a set of benefits and further actions. But, first things first – what data should you collect?
- Transactions,
- personal data,
- bank contracts,
- activity through various channels,
- products.
With this, you can create general segments but also come up with much more personalized offers for your customers. But first, you need to engineer this data – we’ve written more about it in our article on how to implement AI in your business, which we strongly recommend.
Personalization
When it comes to the use of AI and ML in the banking sector, one of the most prominent (and beneficial) ones is intelligent product recommendations. An AI-driven system may utilize the data mentioned in the previous section to come up with personalized product offers based on customer’s preferences and previously chosen products, along with their current credit score and capabilities. A perfect example of this AI use in banking is our own customer engagement platform – LiveBank.
The possibility to personalize offers is critical. Different generations have varying priorities and preferences when it comes to banking products. If you add individual factors into the mix, you can see that it’s really difficult to make the right choice when offering products to your clients. But AI is capable of spotting patterns in data that humans wouldn’t be able to, and it does so in a matter of seconds, which enables your financial institution to achieve a much higher level of personalization.
While we mentioned mostly product recommendations above, this also applies to content – articles and videos targeted at your customers. Serving customized content is as important as the products, especially for the younger customers who seek guides on investing rather than particular banking solutions[1] (though it is an excellent way to promote your investment solutions!).
Churn Prediction
With customer acquisition costs reaching absurd levels, preventing customer churn becomes one of the major goals for financial institutions. So, it should not be surprising that one of the uses of AI in banking and finance is strictly related to it.
How does it work? First, you use machine learning to train your artificial intelligence model to detect churn – use historical data on those customers who left your institution and juxtapose it with those who didn’t, as AI should be capable of spotting the pattern. Now, you can implement your system to monitor customer behavior and watch out for any indicators of churn.
Following this, you can either set alerts or automatic measures dedicated to approaching a soon-to-churn customer and convincing them to change their mind. This way, a simple AI banking system can help you keep many more of your customers loyal, which translates directly into higher profits.
AI Chatbots and Assistants
We must not forget chatbots and AI assistants – two quite similar yet distinctly different types of artificial intelligence solutions used in banking. Why don’t we start with chatbots?
Chatbots
One of the most well-known examples of this is Erica – Bank of America’s chatbot which launched in March 2019. It’s known for having assisted about 6 million users.
At first, chatbots were simple – their purpose was to answer basic questions, like queries about particular transactions or account balances. Nowadays, this gets a bit more advanced, mainly due to natural language processing (NLP) and AI. Such systems are capable of detecting customer’s intent and generating automatic responses while collecting data on the interaction at the same time. This way, they can streamline business processes and improve customer experience.
The main disadvantage of such chatbots is that they are often most useful only in English. However, at Ailleron, we can proudly say that our generative AI solutions are excellent not only in English but also in local languages, helping you polish the customer experience no matter what country your financial institution is based in.
AI Assistants
Known as AI assistants, AI co-pilots, and under other similar names, they are an example of AI use in banking that you will find among our solutions: we can prepare an AI assistant tailored to your organization’s needs, but also offer it as a part of our LiveBank platform. How do they work?
As the name suggests, they assist customer service agents. They can serve numerous functions, often integrating other AI capabilities, such as:
- Drafting responses to customers and providing sources of information for the agent to verify.
- Paraphrasing the messages to tailor the voice to the customer.
- Proposing intelligent product recommendations in real-time.
- Using an AI prompter to provide agents with data on each customer.
Marketing
Finally, you may use AI for marketing purposes to streamline your customer acquisition and reduce acquisition costs. You can achieve this on two levels:
- Campaign carry-outs – AI may be used to present personalized advertisements and offers to potential customers based on data collected on each of them through various channels, including:
- e-mail,
- banners,
- splash screens,
- push notifications in apps,
- open-web advertisements.
- Contact center lead prioritization – Similarly to churn prediction, you can use ML in banking to teach the AI to recognize the quality of each lead and help you prioritize the most promising ones.
Conclusions
These are just a few of the most popular uses of AI in banking and finance. With the further development of generative AI, we are bound to see this list get even longer with time – after all, it all depends on what kind of customized artificial intelligence model you will choose for your bank. And, since we are on the topic of bespoke AI in banking, check our machine learning services and other solutions that we can provide you with – we will help you implement artificial intelligence in your organization.
You may also read: Trends in digital banking and the future of banking
[1] https://www.bai.org/banking-strategies/what-matters-most-to-each-generation-of-banking-customers/
