Blockchain, DLT and Fintech
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Paper Type
Short
Paper Number
2512
Description
Alternative data in small and medium-sized enterprise (SME)-focused Fintech lending has been defined as data that are gathered from non-traditional data sources and not typically included in the traditional credit process. The use of a broad variety and vast amount of structured and unstructured alternative data to mitigate information friction and augment risk management is at the heart of Fintech lending. By categorizing alternative data into different types and comparing them with traditional data, our study seeks to answer the following important questions in SME-focused Fintech lending: 1) whether traditional data outperform alternative data in managing the convenience-fraud risk conflict, 2) whether traditional and alternative data are overall complements or substitutes in credit evaluation and fraud detection, and 3) how and to what extent traditional and alternative data should be combined for better credit and loan decisions.
Recommended Citation
Zou, Weifei; Vance, Anthony; Straub, Detmar W; and Yan, Jie (Kevin), "The Differential Role of Alternative Data in SME-Focused Fintech Lending" (2020). ICIS 2020 Proceedings. 13.
https://aisel.aisnet.org/icis2020/blockchain_fintech/blockchain_fintech/13
The Differential Role of Alternative Data in SME-Focused Fintech Lending
Alternative data in small and medium-sized enterprise (SME)-focused Fintech lending has been defined as data that are gathered from non-traditional data sources and not typically included in the traditional credit process. The use of a broad variety and vast amount of structured and unstructured alternative data to mitigate information friction and augment risk management is at the heart of Fintech lending. By categorizing alternative data into different types and comparing them with traditional data, our study seeks to answer the following important questions in SME-focused Fintech lending: 1) whether traditional data outperform alternative data in managing the convenience-fraud risk conflict, 2) whether traditional and alternative data are overall complements or substitutes in credit evaluation and fraud detection, and 3) how and to what extent traditional and alternative data should be combined for better credit and loan decisions.
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