The application of new technology requires banks to be fully aware and consistent about the management model, transformation plan, resources and risks to change from traditional to digital business models.
As one of the outstanding technological achievements of industry 4.0, artificial intelligence (AI) is increasingly researched for wide application in many fields including banking and finance. Along with IoT, Blockchain and Big Data, AI is expected to bring significant transformation opportunities for the banking industry, such as the application of IoT and AI with the aim of leveraging interaction points with customers and improving operational efficiency.
General director of Tien Phong Commercial Joint Stock Bank (TPBank), Nguyen Hung said that with IoT, the bank had the ability to collect thousands of unique data for each customer through contact points such as card scanners, data readers and smartphone, helping shape the picture and instantly update customer needs. Combined with the AI solution, the bank could actively access or respond to changes of customer needs automatically with the support of a virtual assistant (Chatbot).
According to experts, banks can also apply AI in managing risk portfolios, customer management, and databases. With self-learning and adaptability, the potential of AI is not limited to applications. Representative of a joint stock commercial bank added that financial institutions could use AI to assess credit quality, prices, insurance contracts and automatic interaction with customers. Investment funds and brokerage agents can use AI to realise higher returns and optimise the execution of transactions. In addition, regulators can use AI technologies to carry out regulatory compliance, surveillance, data quality assessment and fraud detection.
Chatbot is considered as the most identifiable form of AI applied in banking activities. Chatbots are built with AI that can help agents respond to customer questions with precision and speed, or it can delight customers without manpower.
TPBank is the first bank in Vietnam to apply AI via the virtual assistant TAio on facebook since July 2017. VPBank Timo, Viet A Bank, Eximbank, HDBank and Vietcombank have introduced Chatbot to replace part of the customer care staff.
Not only Chatbot, many banks also put AI into various operations of banks such as Vietcombank with money transfer service integrated with AI technology. TPBank introduced and implemented many modern systems such as CRMmanagement system of customer relationship or LOSloan management system, which helps the bank greatly increase the ability to find new customers, digitise documents, and process documents with AI technology.
Vietnam Maritime JointStock Commercial Bank (MSB) is the first bank to apply AI in credit card opening operations. To serve customers in the field of digital banking, VietinBank has implemented the Business Intelligence systemthe system that converts data into valuable information (including reporting and analysis) for internal management activities, risk management and business development.
However, developing AI applications is not simple. According to an expert, only with credit scoring, AI and Big Data will help the bank minimise risks as well as save time processing information to give out valuable signals in its operations. But this also requires banks to take appropriate steps to keep up with the development trend of information technology, bringing the best experience to customers. Besides, to be able to build and maintain an employee team with in-depth knowledge of AI and Big Data is also a challenge.
“Banks can consider buying the external tools or solutions available to assist in automating the estimation, building credit scoring models on a user-only basis, understand the principles and parameters of a model, without necessarily being an expert in data science to develop algorithms from scratch. Then focusing on building internal resources with professional competence is probably a more appropriate step for Vietnamese banks,” he said.
On the other hand, Vietnam financial market data is still not enough to be able to strongly apply AI. Customer profile data and credit relationship history at banks are currently still limited. This makes it difficult to identify good and bad customers according to advanced practices, or with new products and services, the bank will also be short of credit history information of customers to serve the risk appetite classification.
For services that need to be more personalised, it is difficult for the banks to develop effectively unless AI is soon put into the application. The application of new technology also requires banks to be fully aware and consistent about the management model, transformation plan, resources and risks to change from traditional to digital business models.
