Challenges in designing an inclusive Peer-to-peer (P2P) lending system

Sulastri, Reni Endang; Janssen, Marijn

doi:10.1145/3598469.3598475

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Therefore, for a solid experimental conclusion, another 4-fold cross-validation experiment was also studied. No sampling (original data) Logistic regression 0.9882 (12) 0.9920 (10) 0.9506 (12) 0.9709 (12) 0.9639 (12) Random forest 0.9979 (4) 0.9999 (3) 0.9902 (6) 0.9951 (4) 0.9938 (4) Gradient boosting 0.9961 (9) 0.9999 (3) 0.9812 (10) 0.9905 (9) 0.9882 (9) Over-sampling Logistic regression 0.9914 (11) 0.9895 (12) 0.9685 (11) 0.9789 (11) 0.9736 (11) Random forest 0.9999 (2) 1.0000 (1) 0.9999 (2) 0.9999 (2) 0.9999 (2) Gradient boosting 1.0000 (1) 1.0000 (1) 1.0000 (1) 1.0000 (1) 1.0000 (1) Under-sampling Logistic regression 0.9950 (10) 0.9900 (11) 0.9856 (9) 0.9878 (10) 0.9847 (10) Random forest 0.9986 (3) 0.9989 (8) 0.9940 (3) 0.9965 (3) 0.9956 (3) Gradient boosting 0.9979 (4) 0.9992 (7) 0.9908 (4) 0.9950 (5) 0.9937 (5) Combined sampling Logistic regression 0.9966…”

Section: Resultsmentioning

confidence: 99%

“…These P2P platforms, which are widely used in many countries, are involved with higher risk than traditional lending, because they depend on individuals [3]. However, there are many advantages superior to banking credit, i.e., lenders' and borrowers' direct interaction, detailed credit scoring [4], and the opportunity to gather and analyze large numbers of data which can be used to assess trustability and reduce risks [5]. Therefore, several previous research works have been studied to build an efficient model to predict the risk of lending [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Supervised Model Performance in Credit Risk Classification Using Sampling Strategies and Feature Ranking

Wattanakitrungroj,

Wijitkajee,

Jaiyen

et al. 2024

BDCC

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For the financial health of lenders and institutions, one important risk assessment called credit risk is about correctly deciding whether or not a borrower will fail to repay a loan. It not only helps in the approval or denial of loan applications but also aids in managing the non-performing loan (NPL) trend. In this study, a dataset provided by the LendingClub company based in San Francisco, CA, USA, from 2007 to 2020 consisting of 2,925,492 records and 141 attributes was experimented with. The loan status was categorized as “Good” or “Risk”. To yield highly effective results of credit risk prediction, experiments on credit risk prediction were performed using three widely adopted supervised machine learning techniques: logistic regression, random forest, and gradient boosting. In addition, to solve the imbalanced data problem, three sampling algorithms, including under-sampling, over-sampling, and combined sampling, were employed. The results show that the gradient boosting technique achieves nearly perfect Accuracy, Precision, Recall, and F1score values, which are better than 99.92%, but its MCC values are greater than 99.77%. Three imbalanced data handling approaches can enhance the model performance of models trained by three algorithms. Moreover, the experiment of reducing the number of features based on mutual information calculation revealed slightly decreasing performance for 50 data features with Accuracy values greater than 99.86%. For 25 data features, which is the smallest size, the random forest supervised model yielded 99.15% Accuracy. Both sampling strategies and feature selection help to improve the supervised model for accurately predicting credit risk, which may be beneficial in the lending business.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Supervised Model Performance in Credit Risk Classification Using Sampling Strategies and Feature Ranking

Wattanakitrungroj,

Wijitkajee,

Jaiyen

et al. 2024

BDCC

View full text Add to dashboard Cite

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Balancing Innovation and Regulation for Financial Inclusion: The Future of P2P Lending in Indonesia

Shi

Wang

2023

IJIED

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This comprehensive analysis delves into Indonesia’s burgeoning Peer-to-Peer (P2P) lending sector, focusing on its transformative potential for financial inclusion and the inherent challenges of customer protection. The article scrutinizes the exponential growth trajectory of P2P lending platforms, a phenomenon propelled by escalating internet penetration, widespread smartphone usage, and a substantial unbanked population in the country. It also sheds light on the risks associated with the proliferation of illegal platforms outside the regulatory framework. The article underscores the need for robust regulatory compliance and effective risk management strategies to ensure customer protection and maintain market stability in this rapidly evolving sector. It delves into the regulatory measures established by Indonesia’s Financial Services Authority (OJK). It discusses the necessity for these regulations to be continuously reviewed and enhanced to keep pace with market developments. The article further explores the potential of P2P lending platforms in promoting financial inclusion by providing an accessible and convenient avenue for individuals and small businesses to access credit. It discusses how these platforms leverage alternative data for credit scoring, thereby enabling those without a traditional credit history to gain access to loans. In conclusion, the article emphasizes that striking a delicate balance between fostering innovation and ensuring stringent regulatory compliance is crucial for Indonesia’s sustainable growth of the P2P lending sector. It advocates for a regulatory environment that nurtures innovation while safeguarding customer interests and maintaining overall market stability. The article posits that achieving this balance will enable Indonesia to fully harness the potential of P2P lending, thereby driving financial inclusion while simultaneously ensuring customer protection and market stability.

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Challenges in designing an inclusive Peer-to-peer (P2P) lending system

Cited by 2 publications

References 25 publications

Enhancing Supervised Model Performance in Credit Risk Classification Using Sampling Strategies and Feature Ranking

Enhancing Supervised Model Performance in Credit Risk Classification Using Sampling Strategies and Feature Ranking

Balancing Innovation and Regulation for Financial Inclusion: The Future of P2P Lending in Indonesia

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