Digital Banking & The Wave Of ‘All Things Customer’

Evolving technology means growing demands from customers. They begin expecting tools that are intuitive, seamless, and fast. Incidentally, these tools help banks advance by hitting their ideal customer engagement target. According to a report published by the Financial Brand, customers expect somewhat of a ‘GPS of financial services’. They look for services that can help them navigate their finances and reach their financial goals as efficiently as possible.

By leveraging digital banking engagement tools that use data, analytics, and real-time communication at scale, banks can meet increasing expectations simply by evolving.

Benefits & Use Cases of AI/ML In Digital Banking

Automation that enables seamless, fast, and reliable process

Using Artificial Intelligence, banks can accelerate automation and make their process seamless. One of the best applications of AI in the financial and banking sectors is automation. The scope for AI in the financial industry is tremendous. Banks can simplify and automate every operation previously carried out by humans using AI software. Legacy systems that take up extra room and increase cost, onboarding process that is tedious and time consuming are two common instances where automation wins.

One such UK digital bank, NatWest, collaborated with Tink, a Swedish open platform company, which was used to help develop an actionable newsfeed on their mobile app. They garnered 1.3 million answers in its first two months.

Intelligent tools used for improved customer experience

Chatbots and virtual assistants are tools that have been gaining popularity in recent years. Many banks have already introduced chatbots in their mobile applications due to the advantages it offers with his personalisation feature. AI chatbots in the banking sector can serve clients around the clock and provide precise answers to their questions. It is convenient, time-saving, fast, and reliable due to the degree of personalisation it has for a bot.

Canada’s CIBC bank’s implementation is yet another example of leveraging data to offer an omnichannel experience to power its customer acquisition. They pushed targeted mobile promotions to customers and synchronised data to create models that can update product pages quickly and at scale by using a modular, data-driven approach that enables them to use existing behavioural data to target messages and reuse, and recombine content to fit specific profiles in real-time.

Improves customer engagement

AI banking apps can do wonders. Implementing AI and Machine Learning in banking can help them understand what customers are looking for and offer it to them. One positive aspect of integrating AI/ML is that with its assistance, banks can now be there for their customers even on bank holidays.

AI for portfolio management

Artificial intelligence can be used more effectively for personal financial, wealth, and portfolio management. In addition, The management system for debit and credit cards also dramatically benefits from AI. The handling of credit and debit cards can be automated, which improves security. Artificial intelligence in banking streamlines the card authentication procedure and ensures the safety of all transactions. AI systems, therefore, boost mobile financial services.

AI for risk management and fraud detection

Banking apps with artificial intelligence can reduce fraudulent activities by identifying risks. It is also best suited for risk management. Risk-related activities include verifying documents, approving loans, and assessing financial status. AI and machine learning in banking can handle these with greater accuracy and privacy.

Future Of Customer Engagement With AI/ML

It was observed by Digital Banking Report in their study that some of the key pain points for banks included easy account opening and digital onboarding, the absence of smart personal financial management tools (PFM) and proactively providing advice, and finally, educating the employees with analytics to help new customers.

By integrating AI/ML, banks begin filling the gaps…

“Data is critical to identifying target customers, personalising their experience, and nurturing customer relationships,” says the head of banking of Zurich Cantonal Bank, Switzerland, and we couldn’t agree more. Using data, robust analytics, and AI/ML algorithms, banks can improve their operations across all channels by giving personalised, omnichannel experiences customers look for from their digital interactions.

AI/ML cannot single-handedly drive the banking sector into the future. However, it becomes imperative for banks to integrate AI/ML to search scaling and receive the customer engagement revenue they expect.

Banks are constantly under pressure to adopt new technologies, which is why Clayfin exists to ensure they reach their objectives while meeting their customer’s expectations. We offer services that leverage intelligent technology for transparency and guaranteed scalability. Contact us to learn more.

Jishith Gangadharan

Jishith is a marketing strategist with more than 16 years experience in IT industry. He has extensive experience across various facets of marketing in the industry. Jishith spearheads marketing and communications for Clayfin. Outside office, he enjoys travelling, reading and aquascaping.

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