Payday loans -- blessing or growth suppressor? Machine Learning Analysis
- URL: http://arxiv.org/abs/2205.15320v1
- Date: Mon, 30 May 2022 14:04:58 GMT
- Title: Payday loans -- blessing or growth suppressor? Machine Learning Analysis
- Authors: Rohith Mahadevan, Sam Richard, Kishore Harshan Kumar, Jeevitha
Murugan, Santhosh Kannan, Saaisri, Tarun, Raja CSP Raman
- Abstract summary: This research paper revolves around the impact of payday loans in the real estate market.
The research paper draws a first-hand experience of obtaining the index for the concentration of real estate in an area of reference by virtue of payday loans in Toronto, Ontario.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The upsurge of real estate involves a variety of factors that have got
influenced by many domains. Indeed, the unrecognized sector that would affect
the economy for which regulatory proposals are being drafted to keep this in
control is the payday loans. This research paper revolves around the impact of
payday loans in the real estate market. The research paper draws a first-hand
experience of obtaining the index for the concentration of real estate in an
area of reference by virtue of payday loans in Toronto, Ontario in particular,
which sets out an ideology to create, evaluate and demonstrate the scenario
through research analysis. The purpose of this indexing via payday loans is the
basic - debt: income ratio which states that when the income of the person
bound to pay the interest of payday loans increases, his debt goes down
marginally which hence infers that the person invests in fixed assets like real
estate which hikes up its growth.
Related papers
- Escaping the Subprime Trap in Algorithmic Lending [49.1574468325115]
We study the role of risk-management constraints, specifically Value-at-Risk (VaR) constraints, in the persistence of segregation in loan approval decisions.
We develop a formal model in which a mainstream (low-interest) bank is more sensitive to variance risk than a subprime bank.
We show that a small, finite subsidy can help minority groups escape the trap by covering enough of the mainstream bank's downside.
arXiv Detail & Related papers (2025-02-25T03:43:57Z) - Hypothesizing Missing Causal Variables with LLMs [55.28678224020973]
We formulate a novel task where the input is a partial causal graph with missing variables, and the output is a hypothesis about the missing variables to complete the partial graph.
We show the strong ability of LLMs to hypothesize the mediation variables between a cause and its effect.
We also observe surprising results where some of the open-source models outperform the closed GPT-4 model.
arXiv Detail & Related papers (2024-09-04T10:37:44Z) - Empowering Many, Biasing a Few: Generalist Credit Scoring through Large
Language Models [53.620827459684094]
Large Language Models (LLMs) have great potential for credit scoring tasks, with strong generalization ability across multiple tasks.
We propose the first open-source comprehensive framework for exploring LLMs for credit scoring.
We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks.
arXiv Detail & Related papers (2023-10-01T03:50:34Z) - Factor Investing with a Deep Multi-Factor Model [123.52358449455231]
We develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights.
Tests on real-world stock market data demonstrate the effectiveness of our deep multi-factor model.
arXiv Detail & Related papers (2022-10-22T14:47:11Z) - Machine Learning Models Evaluation and Feature Importance Analysis on
NPL Dataset [0.0]
We evaluate how different Machine learning models perform on the dataset provided by a private bank in Ethiopia.
XGBoost achieves the highest F1 score on the KMeans SMOTE over-sampled data.
arXiv Detail & Related papers (2022-08-28T17:09:44Z) - On the dynamics of credit history and social interaction features, and
their impact on creditworthiness assessment performance [3.6748639131154315]
This study aims to understand the creditworthiness assessment performance dynamics and how it is influenced by the credit history, repayment behavior, and social network features.
Our research shows that borrowers' history increases performance at a decreasing rate during the first six months and then stabilizes.
The most notable effect on perfomance of social networks features occurs at loan application.
arXiv Detail & Related papers (2022-04-13T00:42:27Z) - MugRep: A Multi-Task Hierarchical Graph Representation Learning
Framework for Real Estate Appraisal [57.28018917017665]
We propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal.
By acquiring and integrating multi-trivial urban data, we first construct a rich feature set to comprehensively profile real estate from multiple perspectives.
An evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronouslytemporal dependencies among real estate transactions.
arXiv Detail & Related papers (2021-07-12T03:51:44Z) - Explanations of Machine Learning predictions: a mandatory step for its
application to Operational Processes [61.20223338508952]
Credit Risk Modelling plays a paramount role.
Recent machine and deep learning techniques have been applied to the task.
We suggest to use LIME technique to tackle the explainability problem in this field.
arXiv Detail & Related papers (2020-12-30T10:27:59Z) - Lifelong Property Price Prediction: A Case Study for the Toronto Real
Estate Market [75.28009817291752]
We present Luce, the first life-long predictive model for automated property valuation.
Luce addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data.
We demonstrate the benefit of Luce by applying it to large, real-life datasets obtained from the Toronto real estate market.
arXiv Detail & Related papers (2020-08-12T07:32:16Z) - Mitigating Bias in Online Microfinance Platforms: A Case Study on
Kiva.org [0.348097307252416]
We investigate lender perceptions of economic factors of the borrower countries in relation to their preferences towards loans associated with different sectors.
We find that the influence from economic factors and loan attributes can have substantially different roles to play for different sectors in achieving faster funding.
arXiv Detail & Related papers (2020-06-20T00:22:49Z) - Determining Secondary Attributes for Credit Evaluation in P2P Lending [0.0]
We utilize machine learning classification and clustering algorithms to accurately predict a borrower's creditworthiness.
We achieved 65% F1 and 73% AUC on the LendingClub data while identifying key secondary attributes.
arXiv Detail & Related papers (2020-06-08T16:12:00Z) - Predicting Bank Loan Default with Extreme Gradient Boosting [0.0]
We use an Extreme Gradient Boosting algorithm called XGBoost for loan default prediction.
The prediction is based on a loan data from a leading bank taking into consideration data sets from both the loan application and the demographic of the applicant.
arXiv Detail & Related papers (2020-01-18T18:52:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.