The finance and banking sector is transforming itself thanks to the accelerated application of artificial intelligence (AI) in many tasks. However, the widespread application of AI in banking still faces many specific challenges…

Various AI applications have been making their mark in the banking and finance industry. Typically, intelligent chatbots and virtual assistants are capable of understanding and resolving customer queries, providing appropriate financial services, automating tasks, detecting fraud, assessing credit and providing automated customer support solutions, according to Dr. Vo Thi Hong Diem, lecturer at RMIT University. 

The integration of AI into the financial industry has grown significantly in recent years. McKinsey’s 2019 Global AI Survey found that nearly 60% of respondents in the financial services sector said they have integrated at least one type of AI technology to perform rule-based operational tasks or detect cybersecurity risks.

A 2020 World Economic Forum survey also found that 85% of financial institutions were incorporating AI in banking into their operations at the time of the survey, while 77% of senior executives predicted AI would be a high or very high priority for their businesses within the next two years.

AI IS USED IN MANY TASKS

Dr. Diem commented that following the global trend, major banks in Vietnam have been investing in research and application of AI technology in their banking operations. For example, TPBank has integrated facial recognition technology into its LiveBank automatic banking channel, enhancing security and convenience for customers. VietinBank uses FaceID recognition kiosks to identify customers and forward their requests to consultants, while also acting as a powerful assistant.

Other banks such as VietABank, Nam A Bank, VPBank, Techcombank, VIB and ACB have used AI for various functions, including chatbots to support and interact with customers, asset management, security, fraud prevention and analysis of ATM withdrawal behavior during peak seasons.

“Incorporating AI technology into the banking sector not only optimizes operating costs but also enhances customer support and enables efficient process automation. AI has proven its superiority in revolutionizing data management, understanding customer behavior and fostering sustainable customer relationships,” said Dr. Diem.

According to Dr. Diem, it is important to note that most Vietnamese banks use traditional rule-based AI, which excels at processing routine requests and supporting simple financial transactions. This type of AI can only automate pre-programmed tasks, and is often trained specifically for fixed and specific tasks, so it is less adaptable to new situations or tasks. “Meanwhile, generative AI can be trained on many types of data and adapt to different situations and changes. However, the application of generative AI in the banking sector is still limited,” Dr. Diem commented.

Read more: 5 IT Outsourcing trends businesses need to pay special attention to

THE POTENTIAL OF AI CREATORS 

Generative AI in banking is a new generation of technology that has the potential to take automation to the next level by empowering computers to generate new content and ideas, rather than simply processing and analyzing data. Dr. Diem said that the significant difference between traditional AI and generative AI in banking is the ability to learn and adapt. “Generative AI can process past data, learn from that data, and make intelligent decisions based on this knowledge, while traditional AI is limited to performing pre-designed tasks,” Dr. Diem assessed. 

Additionally, generative AI in banking can continuously retrain, update, and adjust its predictions, diagnoses, and decisions in response to new input data. This adaptability is in line with the growing demand for financial services that are personalized to customer needs. Furthermore, generative AI can access the information needed to perform complex tasks involving customer information and complete simple or complex automated payments as an autonomous AI agent without human supervision.

According to Dr. Diem, the widespread integration of generative AI into the banking sector in Vietnam is currently facing a number of challenges.

Firstly, Vietnam lacks a solid AI in banking development ecosystem and appropriate support policies, so it is still in the early stages of AI compared to some other Asian countries. “The high cost of advanced AI and machine learning applications as well as the scarcity of skilled labor are hindering progress in this field. Currently, the supply of AI human resources in Vietnam only meets 10% of the recruitment needs of the domestic market,” Dr. Diem shared. 

Additionally, generative AI in banking requires large amounts of high-quality data. This is a significant obstacle because the completeness, consistency, and accuracy of the data affect the reliability and transparency of the AI ​​model. Strict data security and privacy regulations also limit the amount of data that can be used to train generative AI models, leaving these models vulnerable to cyberattacks and unable to reach their full potential. Inaccuracies and even biases in training data can be amplified by generative AI models, leading to suboptimal results.

Multi-layered infrastructures pose another challenge for generative AI, as this type of AI in banking relies heavily on databases. “However, access to banking data and security information is often restricted, making it impossible for AI to perform simple or complex payment tasks involving customer information and security information,” said Dr. Diem.

Read more: Forecast of prominent cybersecurity trends in 2024

HOW TO DEEPLY INTEGRATE AI IN THE BANKING INDUSTRY?

According to Dr. Diem, to integrate AI in banking more deeply in the future, developing big and high-quality data is a necessary task for the banking industry.

There is a need to continue researching and developing unified AI in banking infrastructure solutions to facilitate complex tasks related to customer information, security, and seamless financial transactions. This will allow AI to access critical information and perform automated processes without constant supervision. “Ideally, AI in banking can be integrated with other disruptive digital technologies such as blockchain, which provides a highly secure database for data transmission and storage, ensuring both security and transparency, and facilitating the construction of an interbank database,” Dr. Diem said.

Furthermore, Vietnam’s AI in banking development ecosystem and supporting policies still need to expand significantly to catch up with other countries in Asia. “Strategic investments in technology infrastructure, resources and talent (including data scientists and machine learning experts) are crucial for banks to remain competitive and ready for emerging trends,” Dr. Diem shared. 

‍