Over the last few decades, banks everywhere have adopted the latest tech innovations to redefine how customers can interact with them. There was the introduction of ATMs (the 1960s) and electronic payments that were card-based (the 1970s). Later, in the 2000s, we witnessed the adoption of online banking 24/7, which was then followed by mobile-based ‘banking on the go’ in the 2010s.
Now in the AI-powered digital age, because of the falling costs for data storage and processing, there is increased access and connectivity for everyone, and more and more advances in AI technologies. These are the technologies that can lead to higher automation and can improve human decision-making in terms of both speed and accuracy, only after they are controlled for risks. According to a study report by McKinsey, AI can potentially unlock $1 trillion of incremental value for banks, annually.
AI technologies that can help boost revenues are – increased personalization of services to customers and employees; uncover opportunities based on the ability to process and generate insights from vast collections of data; lower costs through efficiencies generated by higher automation, reduced error rates, and better resource utilization. AI technologies can dramatically improve banks’ ability to achieve four key outcomes: higher profits, at-scale personalization, rapid innovation cycles, and distinctive omnichannel experiences.
A survey by Narrative Science highlighted the fact that 32% of financial executive participants utilize AI technology, such as voice recognition and predictive analytics. Moreover, an Accenture survey noted that 76% of those who got surveyed believe that most banks will use AI interfaces primarily for customer interaction by 2020. A 2018 report published by the World Economic Forum, in collaboration with Deloitte, confirmed that 76% of Chief Experience Officers in the banking industry agree that AI is a number one priority because it is critical for differentiation.
The deployment of AI is now taking place to help categorize different payments, offer a source of advice,p and a resource for answering queries via chatbots and provide suggestions to customers based on their spending history .
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How are Banks Using AI to Support Customer Interactions?
Because banks are generally still learning to understand their customer needs, the consumers are not realizing their full saving potential. Legacy systems that are still being used by banks can prove to be a struggle to complete transactions beyond money transfers and deposits.
How will AI help in such conditions? It will allow the banks to focus on customers by leveraging the data they already have to gain better insights. Banks can then personalize and enhance the customer journey, making it frictionless by manipulating the data to offer real-time recommendations. People who aren’t tech-savvy will also be able to process their transactions easily and more efficiently via a smooth online experience. Thus, AI can be used for creating a smarter and more personalized experience for the user.
One can even track the customer’s data such as their spending and purchase history so that the bank can give them some information regarding budgeting and saving. Consumers of the bank can avail personalized services, which will help to increase customer satisfaction and retention, creating mutual value for the bank and the customer. Data is collected through various channels via ATMs, web channels, digital wallets, point of sale activity, or mobile devices. A successful AI application in banking would mean to put this data to good use.
This would help with personalization, transforming the services into an individualized and customized one digitally, based on one customer’s unique behavior, preferences, and requirements. This is what would set banks apart in a competitive differentiation – are they improving compliance? increasing customer engagement? or optimizing the overall operational efficiency?
There are various applications of artificial intelligence in banking already, there are a growing number of start-ups in the UK, one of them is called Trussle and Habito who are looking to help the customers find the best mortgage product in the market by the use of machine learning algorithms.
Banks, with the help of AI will be able to develop more products affiliated that have greater customer loyalty and a lifetime value. Consumers will also gain from working with a trusted institution who knows their personal requirements. With the growth of AI-based decision-making tools, relationship managers will better be able to assist their customers with products and services for managing the customer’s personal finances. The managers will also be able to analyze their customer’s banking experience on the existing channels and this will allow banks to determine how effectively their current processes operate. The banks can also model and implement process optimization across all their channels to serve customers more effectively, and provide an enhanced customer experience.
How With the Use of AI, Banks can Combat Money Laundering
Financial Action Task Force’s (FATF) held a recent Private Sector Consultative Forum, where the organization’s President held discussions on the current state of FinTech and RegTech. Participants present in the forum shared their experiences and challenges and highlighted the fact that artificial intelligence and machine learning could assist with more effective monitoring and screening systems.
The solution can easily be implemented to supplement existing transaction monitoring (TM) and know your customer (KYC) systems to provide a focused analysis of a financial institution’s data. The traditional transaction monitoring system, when enhanced with AI and ML, can drastically reduce the number of false-positive alerts, which will decrease the time investigators spend on irrelevant and inaccurate cases with no risks.
The compliance and business teams can also work better to predict where illicit transactions might occur next while mitigating previously undetected anomalous behavior. AI and ML can also help to assist the teams to take notice of suspected anomalous behavior as legitimate transactions. This will be done through the analysis of related customer activity and predictive analysis which will result in less de-risking and improved customer experience.
It is a sign that FATF’s focus on AI and ML technologies to fight against money laundering and terrorist financing is for the fact that global regulators may begin to place greater emphasis on AI-powered solutions as a required tool in every institution’s compliance toolbox.
