비정형 데이터 분석을 통한 금융소비자 유형화 및 그에 따른 금융상품 추천 방법

• 영문명: Financial Instruments Recommendation based on Classification Financial Consumer by Text Mining Techniques
• 발행기관: 한국IT서비스학회
• 저자명: 이재웅(Jae woong Lee) 김영식(Young Sik Kim) 권오병(Oh byung Kwon)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제15권 제4호, 1~24쪽, 전체 24쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2016.12.30

영문 초록

With the innovation of information technology, non-face-to-face robo advisor with high accessibility and convenience is spreading. The current robot advisor recommends appropriate investment products after understanding the investment propensity based on the structured data entered directly or indirectly by individuals. However, it is an inconvenient and obtrusive way for financial consumers to inquire or input their own subjective propensity to invest. Hence, this study proposes a way to deduce the propensity to invest in unstructured data that customers voluntarily exposed during consultation or online. Since prediction performance based on unstructured document differs according to the characteristics of text, in this study, classification algorithm optimized for the characteristic of text left by financial consumers is selected by performing prediction performance evaluation of various learning discrimination algorithms and proposed an intelligent method that automatically recommends investment products. User tests were given to MBA students. After showing the recommended investment and list of investment products, satisfaction was asked. Financial consumers satisfaction was measured by dividing them into investment propensity and recommendation goods. The results suggest that the users high satisfaction with investment products recommended by the method proposed in this paper. The results showed that it can be applies to non-face-to-face robo advisor.

목차

1. 서 론

2. 로보 어드바이저

3. 방 법

4. 실 험

5. 시사점 및 결론

References

키워드

Fintech Robo-Advisor Text Mining Decision Tree Random Forest Machine learning