An ensemble semi-supervised learning method for predicting defaults in social lending

The Household Finance Landscape in Emerging Economies

Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.

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A state of the art survey of data mining-based fraud detection and credit scoring

This open access book presents a comprehensive and up-to-date collection of knowledge on the state of crowdfunding research and practice. It considers crowdfunding models and their different manifestations across a variety of geographies and sectors, and explores the perspectives of fundraisers, backers, platforms, and regulators. Gathering insights from a wide range of influential researchers in the field, the book balances concepts, theory, and case studies. Going beyond previous research on crowdfunding, the contributors also investigate issues of community, sustainability, education, and ethics. A vital resource for anyone researching crowdfunding, this book offers readers a deep understanding of the characteristics, business models, user-relations, and behavioural patterns of crowdfunding.

Advances in Crowdfunding : Research and Practice

Online peer-to-peer lending: A review of the literature

Recent years have witnessed the popularity of online peer-to-peer lending, which allows individuals to borrow from and lend to each other on an Internet-based platform. Using data from a large P2P ...

Xuchen Lin

Papers

Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China