As the application of Artificial Intelligence gains popularity in banking, financial institutions are building on their existing solutions to solve increasingly complex challenges. The impact of AI is clearly visible in various areas of the banking sector, from decision-making processes, through forecasting, to customer experience.
With a predicted Compound Annual Growth Rate of 32.6% from 2021 to 2030, the market for the AI in banking sector, valued at $3.88 billion in 2020, is expected to reach $64.03 billion by 2030. Here are 3 reasons why AI in banking is set to disrupt financial services for the better.
1. AI in banking helps with fraud prevention
The application of AI in banking is a great asset when it comes to fraud prevention – from unauthorized transactions, to phishing scams, through to identity theft.
To accurately detect fraud, financial institutions must first thoroughly 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, which 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. Since investigating and prosecuting fraud claims can take a lot of time, equipping agents with the right tools is crucial with the goal of increasing efficiency in banking processes.
2. 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 it is different in all bank branches. AI in banking uses multiple data points that analyze customer behaviors, income-tax histories and any other transactions they have made, helping lenders get a clearer picture of who is more likely to repay a loan. To make this analysis, AI in banking uses customer behavior on digital platforms of lenders and affiliates, processing this data and modelling it to output credit scores for each customer, in real time.
Credit scores allow lenders to approach an existing customer or a potential client and sell them approved loan products. AI in banking can standardize the credit scoring process in all banks, and reduce the risk that lenders have to take every time they lend money to their customers.
3. 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 mentioned in an article by Forbes, the chatbot experience might have the potential to improve faster than the branch and real life contact center experiences.
Many argue that, 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.
Are there any pitfalls of AI in banking?
Just as it has benefits, AI in banking can entail a few pitfalls. Emerging technologies can in certain cases 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: 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 in 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 players 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 bots more and more.
Strands Data Analytics for financial institutions
With 15 years of experience in AI & ML in banking services, Strands has been at the forefront of data-driven personalization for years.
With our advanced Data Analytics, raw transactional data gets enriched and transformed into clear and intuitive overviews for the end users. Banking data or open finance data is constantly cleansed, normalized and attributed crucial information, and user behavior is constantly outlined to provide the best possible experience. With a future view of their financial situation based on predictive models, your customers will feel supported in their next best action at all times.
If you represent a financial institution and want to provide your customers with full control over their finances and turn into the best financial advisor for them, find out more about how our Data Analytics can help you:
