Experts believed that to carry out Big Data successfully, an overall approach was required. In which the strategy included building a team of experts with in-depth knowledge about finance and Big Data, along with identifying, connecting and collecting necessary data; building strict mechanisms and policies in information security and security, and so on.
In the past, the most prominent companies were those that had access to the most data or were able to lock their platforms. But in a digital age, the most influential companies would be those that could synthesize the most data and know how to exploit this data source effectively. Data was an essential asset of many fields, including banks, and it was not natural that the resource was described as ‘a new oil.’ From this source of digital data, it was possible to increase, generate new revenue, and provide unique digital application ecosystems, services and products.
The peculiarity of banking activities was to produce a massive amount of data from structured data such as transaction history, customer records to other unstructured data like customer activities on website, mobile banking, social networks. A ‘seasoned’ amount of data would help banks understand customers better, aiming to improve the customer experience journey. Or regarding credit scoring, artificial intelligence (AI) and Big Data would help banks minimise risks as well as save time processing information to give valuable signals in the operation of yourself. Relevance there was how banks would behave in data mining and management, applying Big Data technology to bring competitiveness and efficiency to the procedure.
In recent times, banks had been more and more aware of technology application, implemented effective data management methods, such as Vietnam Joint Stock Commercial Bank for Industry and Trade (Vietinbank), Vietnam Technological and Commercial Joint-Stock Bank (Techcombank), Vietnam Prosperity Joint-Stock Commercial Bank (VPBank) cooperating with International Business Machines (IBM), Military Commercial Joint Stock Bank (MB) cooperating with Infosys, Amigo. Typically, FPT IS in September 2019 also signed a contract of ‘Supplying system construction and big data analysis’ with Tien Phong Commercial Joint Stock Bank (TPBank). Or more actively, Vietnam International Commercial Joint Stock Bank (VIB) had pioneered the application of technology with Big Data and AI to optimise the credit card approval process, initially successfully applied Online Plus (card approval within 30 minutes if customers met the bank’s criteria, without having to visit branches.)
However, experts believed that customer data sources in commercial banks were currently incomplete. Moreover, there was no method to ensure data consistency through the implementation of a master data management system. Not to mention the lack of tools and organisational ways to build 360-degree images of customers, customer relationship management (CRM) implemented recently at banks also did not have the module of ‘data analytics.’ There was no consistent process for integrating data between CRM and other systems in banks, or the way of organising data was not suitable for business, especially marketing was also challenging issues for banks in data mining.
Therefore, according to the research team of Phan Thanh Duc (Banking Academy), three factors needed commercial banks to pay attention to were as follows. Firstly, it was necessary to have an overall architecture for applying Big Data to customer relationship management activities at commercial banks. Secondly, there were specific methods and techniques for unstructured and semi-structured data collection activities from data sources outside banks, with an integrated, consistent customer data platform and storing internal and external data to provide a 360-degree customer profile. Thirdly, a full legal framework was also essential for owning and using non-bank data sources (from third parties, social networks, etc.)
One of the other challenges for commercial banks was to standardise data, standardise accounting and auditing following international practices. The problem of buying investment banks from different IT partners had not had a standard from the beginning, which would make it more difficult to consolidate data.
Another point also mentioned by the expert was to strengthen the management of network security, interested in building a Disaster Recovery Centre. Each bank must update its information security standards to approach the world’s information security standards gradually, measures to ensure security and safety for the payment system. Also, banks needed to detect struggles, prevent and handle violations of the law in the field of payment methods using high technology.
A representative of Techcombank also shared that an abundant source of data required accompanying an analytical team to make meaningful conclusions. That said, the critical point there was that banks must also pay attention to training factors, proper professional qualifications, high professionalism, meeting the needs of operation management, and mastering of modern technological systems.
In general terms, experts believed that to carry out Big Data successfully, an overall strategy was required, in which building a team of experts with in-depth knowledge about finance and Big Data, along with identifying, connecting and collecting necessary data; and building strict mechanisms and policies in information security.
