Woori Bank announced on May 2 that it signed a business agreement with Yonsei University for artificial intelligence (AI) research and development.
Yonsei University has held top-level conferences, and has a faculty of excellent researchers who have published numerous papers in the artificial intelligence field.
The prestigious Korean university is leading the world in artificial intelligence technology at home and abroad. In particular, it opened the AI Convergence College, marking a first among major universities in Korea.
The university is also ramping up efforts to nurture artificial intelligence specialists and convergence talent.
This MOU will help the two organizations jointly study artificial intelligence algorithms as part of the application of the latest artificial intelligence technology to the financial industry.
Anther objective will be to discover research tasks and apply them to business models. They plan to develop various collaboration models that are sustainable.
The main contents of this agreement are the advancement of the Knowledge Management System (KMS) based on artificial intelligence, optical character reading (OCR) structure recognition, explainable AI (XAI) and the efficiency of AI classification problem among others and the latest AI-related technologies.
They are planning to maximize their synergies through research and development in order to apply this technology to the financial business.
This agreement is expected to promote mutual growth by serving as an opportunity for Woori Bank to realize innovative ideas about data utilization based on the latest technology and for Yonsei University to utilize financial data that has not been easily utilized for research and development.
Based on the latest AI technology this year, Woori Bank will expand the use of data as assets to the service application area and use it as a basis for building an AI banker system in the future.
“This agreement is Woori Bank’s attempt to utilize the latest artificial intelligence technology under study at the university,” a Woori Bank official said.
“At the same time, it will be a valuable opportunity for Yonsei University to study how the research results are applied in real business environments.”
Meanwhile, Woori Bank is taking the lead in data assetization that allows AI to learn unstructured data that has not been used such as texts, videos and documents.
In 2021, it opened a personalized marketing system that analyzes unstructured data and uses it for marketing.
It took home the Special Big Data Guru Prize by the Korean minister of science and ICT at the 8th Korea Big Data Awards.
Meanwhile, Woori Bank announced on May 9 that it had built the WON MapSy System that enables marketing managers to extract marketing targets based on AI prediction models without the help of professional data analysts.
The WON MapSy System can select optimal marketing targets through a simulation that combines big data models such as customer demand by entering extraction requirements that allow marketers without expertise in data extraction to directly select marketing targets.
The WON MapSy System enables Woori Bank employees to focus more on services which customers want, boost the accuracy of marketing activities and shorten the design time.
In May of last year, Woori Bank announced a customer financial DNA map that integrates its own developed AI prediction models to allow bank employees to understand the characteristics of customers' financial transactions at a glance, and conducted a pilot target marketing activity based on it.
In the past, a lot of time was needed in manual data analysis, such as consultation between data analysts and marketers.
The WON MapSy System expedites the selection of marketing targets and the provision of products and services that customers want.
“By making good use of the WON MapSy System, department managers can quickly select marketing groups directly, which is expected to shorten required time and boost customer satisfaction,” a Woori Bank official said.
“We plan to expand marketing based on AI predictive models at branch offices by supplementing requirements and making a UI/UX improvement.”
