“Artificial intelligence will have a more profound impact on humanity than fire, electricity and the internet”. A bold statement from Sundar Pichai, Head of Google.
Beyond the AI hype, banks and financial institutions are increasing their efforts to investigate how AI can help reshape their processes and organisational structures to boost efficiency and enhance customer service levels. Globally, AI in the banking industry is expected to keep growing. It is projected to reach USD 64.03 billion by 2030, growing at a CAGR of 32.6% from 2021 to 2030. While in the APAC region, The International Data Corp (IDC) expects businesses (excluding Japan) to collectively spend USD 32 billion on AI in 2025. IDC also mentioned that the banking industry is expected to drive the bulk of the AI spending as banks in the APAC region increasingly leverage the technology for augmented threat intelligence and fraud analysis applications.
According to Gartner, the top three business drivers for AI in the banking industry are to improve customer service/experience, reduce costs, and improve risk management. Let’s take a look at how some banks or financial institutions in APAC are using AI to their advantage.
Trigger early warnings or “digital nudges” to potential late payers
Non-performing loans (NPLs) are on the rise. NPL is a sum of borrowed money whose scheduled payments have not been made for more than 90 days pass without the borrower paying the agreed instalments or interest. For example, credit card debts, car loans, or mortgages.
The Commonwealth Bank of Australia (CBA)’s financial wellbeing and behavioural economics teams show how using AI, machine learning, and insights from customer activity to provide well-timed “digital nudges” can help customers save money and repay their credit card debt. With this new feature, CBA prompts those who consistently incur credit card interest in real time to think about how they could best use their annual tax refund along with messages to consider making a credit card repayment. Additionally, providing customers with insights on the benefits of reducing their debt, including savings on interest or avoiding late fees, as well as potentially improving customers’ overall financial well-being significantly.
The results of this new feature showed that CBA’s customers who engaged with the credit card repayment “digital nudge” repaid an average of AUD 541 more in the month following their tax refund. “The trial showed that if the credit card nudge was rolled out to all eligible customers, this would result in more than AUD 4 million in ‘catch-up’ credit card repayments each year.” – CBA’s Chief Data and Analytics Officer, Dr Andrew McMullan.
Thus, AI can be utilised to forecast potential late payers on top of educating customers about their financial well-being. Having this capability will allow banks to gain more revenue and offer new levels of customer servicing.
Reimagine financial customer services and experiences to increase personalisation and efficiency
Speaking about offering new levels, of customer servicing in banking, the way customers prefer to do banking nowadays has changed too. Customers are increasingly preferring personalised financial services on top of being more value-oriented as they have multiple alternatives. In response to this changing customer behaviour, banks and financial institutions should leverage AI to provide a smart online and offline experience that is convenient, timely, and relevant to the customer.
In China, digital humans have vastly gained popularity as it is closely related to the concept of the metaverse, integrating the virtual and real worlds. About 20 digital humans made their debut in China in the Beijing 2022 Olympic and Paralympic Winter Games alone.
Agricultural Bank of China deploys its AI-based digital human receptionist at its branch in Zhongshan, Hangzhou to help the bank’s duty managers ease their workloads by handling customer queries while offering a new banking experience to customers. We also have Bank of Ningbo introducing its first digital employee at their Shanghai branch that can “sense” the behaviours of customers. If she “senses” customers being in a good mood, she becomes more talkative and introduces more of the bank’s wealth management services. However, if she “senses” customers frowning, she becomes more cautious and does nothing more than answer the questions asked. Another example would be Chinese real estate giant Vanke having an AI-based debt collector named Cui Xiaopan who reminds employees to pay the company’s bills on time and collect bills due. Supported by powerful AI algorithms, the resolution rate of the cases Cui Xiaopan handled was as high as 91.44%.
The concept of having digital humans have brought customer service to a whole new level. The convergence of AI tech is bringing about the power of personality into brands and deeper engagement with greater personalisation.
Examples of AI Use Cases for the Banking and Financial Services
Of course, there are many more ways AI can be applied in many areas in banks and financial institutions. According to the Korean Herald, Hana Bank, which is one of the five major banks in Korea aims to expand communication between customers and staff by using AI to look through customer reviews in real-time. An official who is in charge of the programme at Hana Bank said that getting immediate feedback on their financial services is vital to their expansion as the banking industry is constantly changing. The AI is able to handle the staggering volume of reviews faster and draws clearer boundaries between good and bad feedback compared to humans, which can greatly help Hana Bank determine what needs to be done to enhance customer experience.
Here are some use cases of our very own proprietary Juris Mindcraft, an autoML that can enable you to make intelligent business decisions.
Onboarding your customers with greater personalisation:
- Optimise the customer acquisition cost in telemarketing
- Improve the customer conversion and reduce the acquisition cost in sales
- Provide product recommendations, cross-selling, and upselling to customers
- Predict customer churn and Loan-to-Value (LTV)
Enhancing your loan origination process:
- Identify credit default risk when scoring customers
- Utilise alternative credit scoring for the credit-invisible customers
- Predict next best action
- Credit limit recommendation
Increasing your revenue with an efficient collection process:
- Utilise self-curing prediction strategy to approach delinquent customers
- Utilise self-curing customers prediction strategy to optimise the early collection stage
- Predict next best action
- Optimise the collection strategy by targeting the right customer with the right treatment at the right time
With the right people, the right tools, and the right technology, AI can be easily implemented with banks and financial institutions greatly benefiting from it. Yet, according to Fintech News, APAC’s banks and financial institutions are struggling to draw critical insights from their data in a timely and effective manner. Understanding the major pain points of the banking and financial industry in implementing AI, JurisTech built Juris Mindcraft, an automated Machine Learning (autoML) and artificial intelligence (AI) platform that uses advanced machine learning (ML) techniques to build powerful AI models. An effortless AI that enables banks and financial institutions to make intelligent business decisions and gain insights to solve real-world problems.
Speak to us if you would like to know more about how you can utilise AI and ML to scale your business infinitely at email@example.com.
JurisTech (Juris Technologies) is a leading Malaysian-based fintech company, specialising in enterprise-class software solutions for banks, financial institutions, and telecommunications companies in Malaysia, Southeast Asia, and beyond.