A Systematic Literature Review of Financial Product Recommendation Systems
E-finance has brought many challenges while promoting the process of financial inclusion, thus raising users’ requirements for Internet financial services, including recommendation systems. This systematic literature review examines the latest research approaches to financial product recommendation,...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/3/196 |
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| Summary: | E-finance has brought many challenges while promoting the process of financial inclusion, thus raising users’ requirements for Internet financial services, including recommendation systems. This systematic literature review examines the latest research approaches to financial product recommendation, focuses on the characteristics that distinguish financial product recommendation from other recommendation domains, proposes a financial product recommendation system framework, and organizes the literature based on this. By examining 65 publications published from 2018 to 2024, this analysis finds that current research primarily focuses on three categories of financial products: bank financial products, securities financial products, and other financial products. The financial product recommendation problem is characterized by significant features such as multi-objectivity, wide feature space, time sensitivity, and the existence of parallel interactive behaviors. Current research primarily focuses on three categories of financial products: bank financial products, securities financial products, and other financial products. With the aid of personalized recommendation methods, one can capture users’ preferences for the abstract attributes of financial products. Exploring the potential correlations among financial time series enables accurate and rapid prediction of price trends. Characterizing unstructured data using text-mining techniques can improve the accuracy of the model. Existing research methods focus on the multi-domain and time sensitivity of features and have achieved certain results in the field of financial product recommendation, but each method has its shortcomings, and future research can carry out in-depth exploration of multi-behavioral sequence recommendation, multi-task recommendation, and other aspects. |
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| ISSN: | 2078-2489 |