The pandemic has catapulted every sector into the digital world. In today’s era, if your digital strategies are not up-to-date for your customers, you are not living in 2022. With the pandemic accelerating the digital progress of the brands, 2022 is witnessing the employment of Artificial intelligence, Big Data, and other advanced technologies to cater to the respective audience. The banking sector has also digitized its services for its customers. However, only going online will not benefit in the coming days. The need for AI and Data has increased and banks need to create digital ecosystems for the changing customer requirements. In this article, I will walk you through the post-pandemic changes in the working of the banking sector and how AI in the banking sector can help grow further.
In 2020, when COVID-19 struck the world, we had to shut our physical operations to practice social distancing. Along with physical markets, and ATMs, Banks also had to shut shop. While the consumer goods found themselves in the digital markets of Amazon, Flipkart, Big Bazaar, Grofers, etc., banks too transferred their operations online. Net banking and transactions through apps became the norm. The economy was moving towards becoming cash-free and so the banks moved towards becoming queue-free. The entire business model of the brands shifted towards accommodating digitization.
Today, through their apps, banks allow their customers to not only pay for commodities, but transfer money, make payments, open accounts, create fixed, recurring or other deposits, open DMAT accounts, apply for loans, stop payments, cancel or block cards, and avail many more services. The need for personalisation has increased, thus solely digitization will not be enough.
One step towards adopting AI that has been taken by Indian banks is taking the aid of WhatsApp to enhance their end-to-end customer services. Interacting with the employees and resolving your queries has become as simple as chatting with friends. Bots have started communicating with consumers without creating huge expenses for the banks.
Adopting more technical advancements to their systems can help increase the ROI in the financial sector as well. As per McKinsey&Company, banks can “unlock $1 trillion of increment value” with the use of AI. According to a report by Accenture, with the help of AI tools, “banks can achieve a 2-5X increase in the volume of interactions or transactions with the same headcount”.
Let’s look at different ways in which AI in the banking sector can be applied and make customer interactions better.
How AI make customer interactions better
1. Fraud Detection and Security
With bank applications and websites dealing with payment-related information every day, it becomes important to secure them from fraud. AI can help detect fraudulent activities and alert the bank as well as the customers immediately. This has helped one of the largest banks in Denmark achieve its security goals. Danske Bank has been able to increase its fraud detection ability by 50% and decrease false alarm rates by 60%. The increased security can put the minds of the customers at ease and increase their trust in the name. AI can also help banks solve the issue of cybercrime and threats. These bots can easily detect such activities and alert the banks before the customer is affected.
2. Chatbots and Customer Engagement
With an AI chatbot, it becomes easier for the banks to attend to customer queries 24×7. These chatbots will not only interact with customers but also report individual activities and patterns, thus allowing the bank to build a more personalised experience for their audience.
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3. Customer Lending and Credit
Keeping credit scores and deciding whether an individual is worthy of a loan is an activity that can be performed better by an AI bot. From calculating the score, keeping a check on payments, to initiating warnings to banks for frauds and defaults, AI can organise the entire loaning and lending system, make it safer, and increase its accuracy.
4. Data Analysis
Banking activities and transactions have increased financial data drastically over the past few years. With the digitization of the industry, customer interaction with a bank has grown so much. Proper maintenance and storage of this data can be achieved by the use of AI. The AI-based solutions can not only help systemize and sort data but also analyse to provide reports to the banks, which can in turn help them improve their services.
5. Advising the Customer
Another AI-based system that can be beneficial to banks is the advice from a robot. Based on the activities and financial records of an individual, these robots can issue service, product, investment, or equity advice for the customer. These recommendations will work in favour of the bank, with a customer discovering better investment options and utilizing more services with the bank.
6. Customer Acquisition and Retention
As the customer satisfaction levels will increase, so will the retention rate of a customer. AI bots can also help in acquiring new customers at a faster rate by reaching the right audience as per market analysis and user contact lists.
7. Market trends
Analysing market trends, stocks, and customer activities can be easier with the help of AI. With the AI predicting investment opportunities for the future, the decision-making process will be sped up and help banks, as well as their customers, engage in the stock exchange more safely.
So how can banks transition toward an AI-based system?
The 4-step process can enable banks to create an AI system for their brand and improve customer experience.
Step 1: Developing AI strategy
Find the right team of AI professionals, Data Scientists, and marketers to develop a specific and personalised AI strategy for your bank and the market that it caters to. Conducting research amongst your customers and potential consumers to understand the lack and loopholes will help form a stronger strategy.
While adopting AI, find better methods to integrate it into various departments in the bank so that the functionality of one segment is enhanced by the other and not halted by the inefficiency.
Step 2: Adopting a use case-driven process
Once you have formed a strategy, aligning it with the systems in place is a must; whether the processes need to be modified or recreated to smoothly integrate newer systems.
Before implementation, evaluate your internal systems for gaps to bridge them with feasible options.
Step 3: Deploying
Execution is the next step in adopting AI. Once you are thorough with the strategies theoretically, it is time to test the practicality of your research with the newly acquired AI-ready talent.
Start slow and steady rather than jumping to change the entire network. Test the prototype and form algorithms to start the process. Make use of the collected data and yield results. Once the model is in place, start revising and remodelling as per the new data obtained.
Step 4: Monitoring the progress
Never stop upgrading your processes. Keeping a check on your systems and bots with a set review cycle will help eliminate the gaps in your processes. To ensure the quality, security, and fairness of the model, regular checks are necessary.
If you wish to understand how the AI systems have been working for banks, you can check out the cases of JPMorgan Chase and Capital One. Both companies have adopted AI into their processes, with JPMorgan focussing on customer security and Capital One creating a virtual assistant to enhance customer engagement.
In this changing world scenario, AI in the banking sector has many benefit and on many levels. If a banking brand is unsure of how to proceed with the AI integration process, there is no harm in discussing with the experts and finding what fits for you and your clients.