Design and Evaluation of a Peer-to-Peer Student Lending Platform to Mitigate Information Asymmetry and Credit Risk
Bibliographic record
Abstract
Peer-to-peer (P2P) student lending platforms have emerged as an alternative source of funding for students who are unable to obtain loans from traditional lenders.Traditional loan systems have become less accessible and affordable for all students, and information asymmetry is a significant challenge that can have serious consequences for lenders and borrowers.The rising cost of higher education in many countries has made it challenging for some students to access the funds they need to finance their education.This study aims to design and implement a P2P student lending system that addresses these challenges.The research conducted enables the production of a P2P loan framework using PHP, Mysql, and Javascript that incorporates essential features gleaned from literature and existing P2P applications.The study improves the payback rate by reducing information asymmetry in the P2P lending system using the following methods; detailed applicant profiles, robust credit scoring models, income verification, transparent loan terms, and peer reviews and ratings.The prototype software development model was used to develop the P2P framework.The system is evaluated against industry-relevant metrics, such as loan characteristics, credit risk, and loan performance.The software was developed using HTML, JavaScript, PHP, and CSS, with the Operating System being Microsoft Windows 11.The result of the research is a more accessible and transparent lending system that supports students who require funds to complete their education after a thorough verifiction process by the system.The system was evaluated based on the reviews and ratings gotten from the clients and users.The implementation of this system can significantly impact the education sector by increasing access to funding for students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".