Every bank, in some shape or form, has been impacted by machine learning and artificial intelligence. And certainly, everyone within the banking sector has suffered severe disruption as a result of the current COVID-19 pandemic. What’s interesting to note is that while AI has steadily been entering back, middle and front offices of many banks all over the world, what the current pandemic seemed to have highlighted is the urgency by which banking institutions need to put their AI strategy in place.
It’s probably common for many banking customers to have interacted with a customer service chatbot but what may not be as apparent is that many financial institutions have probably used complex machine learning to detect money launderers or sifted through mountains of data to ward off fraud and other anomalies before they wreak havoc, which is much more difficult to control.
In an age where social distancing will become a norm, and work-from-home could potentially be the default mode of working, machines will be increasingly replied upon, not only to deliver accuracy and efficiency, but contribute to our safety as well. Here are some areas where banks can ‘delegate’ some tasks through AI.
Customer Experience and Front Office Support
Client’s customer support expectations are always evolving. While many Millennials will not visit a brick and mortar bank branch for anything, their expectations of its digital representative is a different story. Artificial intelligence has brought on a lot of these changes. With AI-enabled chatbots and voice assistants, information sought by customers can now be dispensed faster, cheaper and more accurately. We’re also seeing AI helping biometric authorisation as well as ‘physical’ help for those who fancy an occasional bank branch visit through an AI-enabled robotic assistant.
In an age where social distancing will become a norm, and work-from-home could potentially be the default mode of working, machines will be increasingly replied upon, not only to deliver accuracy and efficiency, but contribute to our safety as well.
AML and KYC Compliance
AI can help detect fraud, identify patterns and “find the needle in the haystack”, in a manner of speaking. It can help in: transaction monitoring (identify non-obvious connections between individual transaction chains); entity resolution (creates a single unified view of a customer across local and international databases); identifying ultimate ownership (extract information to identify who has ownership or management stake); and media monitoring (categorise news articles and generate a match relevance score).
Risk Management and Lending
AI has the capability to objectively assess information and come up with equitable credit underwriting. By achieving a credible credit scoring and lending efficiently, AI can help banks assess and manage risks and how they build and interpret contracts. This is good news as there is a tremendous upside in this proposition in terms of new business opportunities and promoting people to do higher value work engagements.
Outstanding Machine Learning Initiatives
As they say, it is not a matter of if, but a matter of when and how. Many notable banking institutions are already in the thick of implementing machine learning initiatives as part of their overall artificial intelligence strategies. From stories about automation and how it would augment human capabilities, to fintech collaborations, to how banks are now using machine learning to provide frictionless, 24/7 customer interactions, there are plenty of case studies to learn from. Here are a few of them.
Taipei Fubon Bank
Taipei Fubon Bank has 4.11 million regular account holders and around 390,000 wealth management customers. Every year, some 1% of regular deposit customers get “promoted” to wealth management status through various methods and gain access to the services afforded to wealth management customers. To come up with a more efficient and scientific method to identify the potential value of customers and boost their activity with the bank (leading to higher status), Taipei Fubon Bank resorted to machine learning technology to predict customer value.
Because the value of customers has been underestimated, many customers were not cultivated by the wealth management team and only had access to the services and experiences made available to regular account holders, focused primarily on online services such as online banking and mobile banking. They did not receive personalised wealth management services or investment recommendations.
After the value of customers was identified more accurately, the customers who were promoted to wealth management status received priority treatment in offline (branches) and online channels. Offline, they were offered exclusive financial planning services supported by dedicated financial consultants, received invitations to VIP events, and receive gifts when visiting a branch. Online, they were sent regular newsletters with the latest investing, product and market information. They also received personalised product recommendations, wealth management perks and special offers. These multitude of online and offline activities and benefits forged a powerful omnichannel VIP experience for customers.
When it comes to enhancing customer engagement, relying only on traditional, structured customer data, from demographics to transaction history, has its limits. Unstructured conversation data from call centers reveal opportunistic life transitions, such as starting a family and buying a house are defining moments of the human experience, that would complete the whole personalised customer journey.
Beginning as a cross-business-unit collaboration, Taishin Bank developed and applied speech-to-text and text-mining technologies across business units – from call center to business intelligence development, digital banking, payment services, and information technology. They leveraged speech-to-text and text-mining technologies to create a hybrid data source, composed of static and dynamic, structured and unstructured data. Collectively, the bank increased work efficiency at the customer call center through the automation of call type labeling. They’ve also enhanced customer experience of Taishin’s web and mobile applications by resolving trending issues in customer calls. Furthermore, the monetisation of hybrid data is increasing due to its life storytelling marketing strategies by proactively reaching out to each customer at the point of their life transitions.
Bank of Ayudhya Public Company Limited (Krungsri Bank)
“Analytics-Based Decision Platform” is an initiative of Krungsri Bank that brings in the most efficient approach for providing the best recommendation tailored for each Krungsri customer. Leveraging on machine learning algorithm, the platform produces smart prediction and consistent recommendation for interacting with each individual customer across all touchpoints. With the efficiency of this data-driven platform, Krungsri has developed a positive customer experience that ultimately wins the customers’ long-term engagement.
As behaviours and lifestyles of each customer will change over time, they need a bank that is capable of learning and adapting to their needs. This platform was built to continuously learn from all customers and produce insights required to meet and go beyond customers’ expectations.