We always talk about bank digitisation, but how many customers actually the banks have accurate information? And the information between the banking internal systems has really synchronised with each other? In today’s era, when the digital economy is gradually shifting to data economy, according to experts, it is necessary to consider data as a strategic asset.
Nguyen Minh Ducdirector of Military Joint Stock Commercial Bank (MB) data recognised that many banks did not have a specialised department to manage data and had not implemented a Data Management Framework. The improvement of data quality was scattered as a component of projects and often only served the project’s objectives; data collection activities had not been taken seriously, leading to a lack of data for intensive analysis.
According to him, improving data quality should first start from the head of the organisation. It is important to be aware of the role and value of data for business operations, thereby building a habit of using data in business management decision-making activities; or change the organisational model. The second critical factor is human required in the way of organising data management; regulations, processes and technology. The last one is about technology.
Observing the past few years, it can be seen that banks have gradually gained the right and more awareness in technology application, implementing effective data management methods.
For example, the Enterprise Data Warehouse Project (EDW) of Vietnam Joint Stock Commercial Bank of Industry and Trade (Vietinbank) is aimed to help this bank store and process data larger than the old system. VietinBank also implements a Business Intelligence Knowledge system that transforms data into valuable information including reporting and analysis for internal management, risk management and business development.
At the end of March 2018, the bank set up a Data Management Board to deal with data issues in order to optimise the value that data can bring in business development. At the same time, it built the first chief data officer (CDO) and was one of the pioneering banks having this position.
In another case, Vietnam Prosperity Joint Stock Commercial Bank (VPBank) cooperated with IBM to build a large database for studying behaviour, capturing customer selection trends and market trends. Or MB partnered with Infosys, Amigo to deploy a centralised data warehouse project and an administrative reporting tool (Data Warehouse) to help this bank build strong data and technology platforms, meeting information requirements, improving operations management and monitoring.
Tien Phong Joint Stock Commercial Bank (TPBank) leader said that Internet of Things (IoT) opened the era of big data, unstructured data and said a lot of things correctly and in many more angles. So, it was not overestimated when we said that owning data in this period could be considered a “billionaire”. The development of large data analysis solutions on IoT platform in the near future would be interested by many banks in order to develop a more rapid and accurate credit evaluation model.
“Data and analytical tools can be used to add value throughout the entire chain of financial services. As a strategy of contacting customers, managing customer engagement or customer retention through location-based marketing based on credit card transaction data, “said, an expert with the similar view.
However, experts note, the classification and processing of new data is a big challenge. Because the amount of data can reach thousands of items (instead of a few dozen or hundreds of items as traditional data), collected from: shopping habits, website habits, habits on mobile, email or social networks, hobbies, bank information, customer information, etc.
Taking advantage of big data and the analysis ability will optimise sales capabilities. It can be interactive data: such as email or chat content, customer care notes, network access data streams; or customer data and attitudes such as opinions, favourite areas, needs and wants; Descriptive data: attributes, characteristics, self-declaration, demographic characteristics by geography; behavioural data: orders, transactions, payment history and usage history.
Having a basic database base, banks can predict customer behaviour, thereby introducing customer attraction models, campaigns, product or service packages, and methods of managing potential customers or multi-channel campaigns.
“Smart banks personalise relationships with customers through continuous updates, understanding the context and learning from data. Accordingly, they can give common points, relationships as well as an overview through diverse data: customer information, customer experience, transaction history and analysis to build a set of 360-degree document personalising customer information, “said Le Nhan TamIBM Vietnam Technology director.
Not only to identify customer behaviour, Black Inc said that data also played a crucial role in the implementation of Basel II of banks. According to the agency, an organisation must develop and apply tools to collect, store data and monitor overall compliance with each section of Basel II. And it was necessary to optimise the continuous data collection process to serve the inspection according to the regulations of the State management agency.
