How do you evaluate the digital transformation process of the Vietnamese banking and finance sector?
The digital transformation for the banking and finance sector is explained in 3 steps: digital conversion of processes (Business processes), digital conversion of communication channels (Channels) and finally digital conversion of data platforms (Data foundation). Vietnam’s banking industry has adopted process transition. Most of the leading banks and mid-tier banks have successfully and effectively completed automated transaction system.
Next step in the process will be the challenge of customer communication channels such as mobile banking, internet banking, etc. Banks often have problems with personnel in managing new systems, such as former staff who have worked for a long time. That’s why many FinTech companies (startups using technology in the financial sector) were born and determined to follow the digital path right from its inception.
After overcoming those obstacles, banks as well as FinTech units will continue to compete at the stage of database exploitation. How banks will exploit and trade from their database, this is still a question.
What make you think so?
Digital Economy is moving towards to Data Economy. Banks that exploit database more effectively will have more competitive advantage. In terms of UI/UX and banking applications, FinTech companies have more competive advantage than traditional banks. However, when it comes to Data Economy, this will be an unfinished battle. The reason is banks have been doing business and loan servicing for such long time and have huge database to exploit.
How does AI play a role in personalizing financial services?
The provision of highly personalized services is actually not new, it has been taking place in banking industry for a long time through credit scoring, customer behavior grading to classify services that are suitable for customers. However, in the scenario of the industrial revolution 4.0 such as machine learning, big data and most recently AI (artificial intelligence) that created a data explosion, we also have a lot of ways to personalize product data for banking, instead of merely relying on transaction history or customer records like before.
In the past, products were supplied with 30 variables, now up to 300 variables. Since then, the idea of using AI (artificial intelligence) to classify products for a group of customers that is suitable for each individual.
Can you give a specific example about how AI personalize financial services?
First of all, we should clearly understand what AI is. AI is defined as an industry of computer science related to automation of intelligent behaviors. If AI is a large domain, then under that domain there will be machine learning, under which will be deep learning. Machine learning is like how we teach a baby to see, listen, read, etc. When the child grows up, he or she will learn more advanced subjects such as Math, Foreign Language and supervised learning. It is the same in banking industry. With tools that applying machine learning such as contact cencer, chatbot,…builds up appropriate predictive modeling.
For example, in the life cycle of a loan product, banks will classify customers and identify potential customers then turn those leads into borrowers. This classifying tool can apply AI to assist. The problem of predicting whether customers are excited about the given service, clicking on the ads, will AI support the evaluation of this potential customer? Customers open accounts and card but will they use it? AI use prediction modeling to see their characteristic to predict if they use or not.
AI also assist in early warning, about who have high rate of delay credit card payment, based on an analysis of the frequency and delays of customers, as well as who can recover the debt.
In the life cycle of credit products, there are many points that AI can support banks to make more accurate decisions based on machine analysis data, helping banks minimize costs and prevent risks.
How does CMC SISG develop AI solutions for financial customers in Vietnam?
When Big Data (big data) was born, there were many dimensions of data generated, at each point of contact with customers, they could develop in real time. For example, an individual customer who just completes an electronic transaction then the bank can send a warning message about the expiration of the credit limit.
After all, banks must be a financial consultors to their clients. Grasping this requirement of financial institutions, CMC SISG has developed a set of financial analysis and risk assessment solutions called “Real-time decision management”, enabling financial – banking units to make real-time decisions, especially in the context of the current data-driven economy. We have implemented this solution for a finance company in Ho Chi Minh City, marking the milestone that Vietnamese financial businesses have begun to take risks to enter the comsumption industry and the “game” about exploiting this data.
Data solutions of CMC SISG were born with design-mindedness (Design-Centric) right from the origin because CMC SISG wants to design solutions that best suit the particular human aspect and Vietnam’s economy, not automatically applies all product standards. CMC SISG is always up to date with the latest data processing and analysis methods implemented by the research team, consulting research documents from leading academic forums of artificial intelligence and data science. Since then, we have been maintained one step ahead in planning technical and research investments.
We are system integration consultant, in which the professional and applied consulting aspect is always put on par with technical competencies. CMC SISG is implementing and constantly searching for multi-technology, multi-component projects with high challenges, cooperating with leading technology partners and business consultants to always update “best practices” of the world as well as guaranteeing the best investment for our customers.