Collaboration of SAS, E.Sun Bank and National Taiwan University on Machine Learning provides an innovative financial customer experience 

Improved accuracy and greater predictive value for the future

SAS, the leader in business analytics and services, and E.Sun Bank (E.Sun), together with the College of Electrical Engineering and Computer Science of The National Taiwan University (EECS), have jointly announced a new collaboration program using machine learning algorithms enabling E.Sun to provide more personalized banking services for customers through self-learning predictive analysis. Machine learning provides an ideal interactive platform to identify the real and potential needs of customers using detailed data analysis, enhancing financial inclusion for the industry. It is expected E.Sun will launch this new program in late 2017 providing a brand new customer experience for the financial industry in Taiwan.

Mr. Joseph Huang, President of E.Sun FHC, said, “With the growth of the Internet and advances in technology, more banking services are turning to digital services delivered through different online platforms. Thus, in order to provide a more customized journey in the digital arena, big data analytics is critical to understanding and meeting customer needs. However, with the current overwhelming volume of data, it is challenging to deal with data analysis by human effort alone. We therefore decided to collaborate with academic experts to find machine learning deployment methodology so as to provide faster and more accurate customer data analysis. At the same time, we invited SAS, which has been devoted to machine learning (algorithm or application) for over 30 years, to help us realize scenario-based financial services with agile and customized insights that meet the diverse needs of different customers through digital channels.” 

Mr. Chen Ming-syan, the Dean of EECS, also pointed out the uniqueness of this collaboration: “This is the first time we have worked with the FSI to connect with real-time financial data, test for different scenarios and further transfer the required skills even though we had outstanding technology in machine learning skills. Students can gain wider exposure in financial applications through this program, which also helps in nurturing interdisciplinary scientific talent, marking this as a highlight program for this year.”

Mr. Mike Chen, General Manager of SAS Taiwan, said, “In today’s customer-oriented era, the biggest challenge in the financial sector is to understand customer needs. A powerful analytic platform is required to analyze customer behavior and preferences as well as optimize product development and minimize operating costs for a company. SAS is providing specific support to E.Sun and ECCS with integrated Open Source and open system Visual Data Mining and Machine Learning (VDMML) for greater flexibility during the research of this program.”

A cooperation committee has been set up for this program, led by Prof. Chen Hsin-his, Associate Dean of EECS, and Prof. Roger Jang, Associate Chairman of NTU’s Department of Computer Science & Information Engineering, for research and development, supported by SAS with its machine learning analysis platform. At the same time, E.Sun provides big data and designated scenarios for testing. The technology will be introduced by E.Sun in late 2017.

Aiming to deliver an innovative customer experience with a key focus on “in-depth analytics of customer needs” and “providing the best customer interaction”, the program leverages on a machine learning algorithm to figure out customers’ pain points and give them the most satisfactory banking services. Imagine a customer who is planning to visit Japan and is now checking the best exchange rate on the E.Sun website and seeking which branch can offer foreign currency exchange. Additionally, he may also need an airport pick-up service and want to know which kind of credit cards provide him the best discount during travelling. By using data analytics, the bank can understand customers’ needs in advance, and immediately provide them with customized notifications, such as updating them with the best timing for currency exchange when the Japanese Yen might be depreciating and the location of the nearest ATM for foreign currency withdrawal before their trip, and informing them about an exemption of service charge for a E.Sun dual currency credit card, thus providing them with a truly personalized digital banking service.

These days, an emphasis on innovative customer experience is a key focus for all finance institutions.  However, with the gap between the expectation and the maturity of data management, analytics and operating processes, these institutions find it hard to explore the value of data in the corresponding context, resulting in a misallocation of resources.

The SAS consultant team added, “The proactive attitude and vision of E.Sun on fintech innovation with their well-formed and independent team of data scientists established over 10 years contributes a key success factor to this collaboration among three parties. Equally important is the E.Sun’s commitment to building the foundation and culture of a data analytic environment by collaborating with different parties and business operations in driving progress in the field of artificial intelligence, which paves the road for fintech transformation in Taiwan.”

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Editorial contacts:

SAS Taiwan Mike Chen, E.Sun Joseph Huang and NTU Ming-Syan Chen
SAS Taiwan, Esun NTU Introduce Machine Learning

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