XGBoost predicts bank loan defaults with improved accuracy.
problem Predicting bank loan defaults to reduce bad loans.
method Used XGBoost algorithm on loan data.
result Improved accuracy metrics in loan default prediction.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
Trend · papers per month
48 results for “loan prediction”
Two models predict net loan losses using Bayesian and frequentist regression.
problem Predicting net loan losses using financial and sociological data.
method Bayesian and frequentist regression analysis.
result Improved understanding of net loan loss relationships.
Logistic Regression and Support Vector Machine algorithms, together with Linear and Non-Linear Deep Neural Networks, are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two phase model is proposed; the first phase predicts loan rejection,…
Networked-guarantee loans may cause the systemic risk related concern of the government and banks in China. The prediction of default of enterprise loans is a typical extremely imbalanced prediction problem, and the networked-guarantee make this problem more difficult to solve. Since the guaranteed loan is a debt oblig…
Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with var…
Deep neural networks reduce loan portfolio risk.
problem Minimizing risk in peer-to-peer lending portfolios.
method Proposed DeNN and DSNN models to predict default probability and time.
result DeNN model significantly reduces portfolio VaRs at various confidence levels.
This paper proposes a two-stage scoring approach to help lenders decide their fund allocations in the peer-to-peer (P2P) lending market. The existing scoring approaches focus on only either probability of default (PD) prediction, known as credit scoring, or profitability prediction, known as profit scoring, to identify…
This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a…
A comparative analysis of machine learning algorithms for predicting probabilities of defaultq-fin.RM
This paper compares ML algorithms for PD prediction, finding XGBoost to be the most effective.
problem Predicting the probability of default in loan portfolios.
method Comparison of five ML algorithms (Random Forests, Decision Trees, XGBoost, Gradient Boosting, AdaBoost) with logistic regression.
result XGBoost outperforms other ML algorithms for PD prediction.
An extendable, integrated, and dynamic approach to forecasting and stress-testing credit riskq-fin.CP
An integrated and extendable approach for stress-testing loan portfolios
problem Stress-testing loan portfolios
method Simulate completed portfolios, generate uncertain cash flow history, compute credit risk metrics
result Enhanced stress-testing practices within any bank
Kiva is an online non-profit crowdsouring microfinance platform that raises funds for the poor in the third world. The borrowers on Kiva are small business owners and individuals in urgent need of money. To raise funds as fast as possible, they have the option to form groups and post loan requests in the name of their …
What Matters to Individual Investors: Price Setting in Online Auctions of P2P Consumer Loansq-fin.GN
Retail investors set interest rates for P2P loans based on borrower characteristics.
problem Understanding how individual investors price credit risk in online consumer loan auctions.
method Reverse auction framework, analyzing interest rate variance and borrower characteristics.
result Retail investors exhibit strong predictability in pricing, with gender and marital status influencing interest rates.
5D AI model detects bad loans without biased features, improving consumer protection.
problem Detecting bad loans without biased features and improving consumer protection.
method Machine learning, BiMOPT features, European Banking Authority principles, AI principles, historical and validation datasets.
result 5D correctly detected 1,461 bad loans out of 1,613 (Sensitivity = 0.91, Prevalence = 0.0253, Positive Predictive Value = 0.19).
Transfer learning improves loan recovery rate forecasting under data scarcity.
problem Data scarcity in loan portfolios limits RR modeling accuracy.
method Introduces FT-MDN-Transformer, a mixture-density tabular Transformer architecture for TL.
result FT-MDN-Transformer outperforms baseline models in RR forecasting, especially under covariate and conditional shifts.
Survey examines machine learning for credit rating predictions.
problem Manual loan approvals are slow and error-prone.
method Examines sentiment analysis techniques in credit rating.
result Machine learning improves credit rating predictions.
Paper uses BERT to assess P2P borrowers' credit risk from loan descriptions.
problem Information asymmetry in P2P lending due to lack of borrower data.
method Fine-tunes BERT on Lending Club dataset to generate risk scores from loan descriptions.
result BERT-generated risk scores improve XGBoost classifier's performance in loan granting.
Model predicts loan default risk using dynamic multilayer graph neural networks.
problem Credit risk assessment through borrower connections.
method Dynamic multilayer graph neural network with attention mechanism.
result Attention mechanism improves model performance.
Model improves mortgage credit risk prediction with spatio-temporal machine learning.
problem Improving accuracy of default probabilities and loan portfolio loss distributions in mortgage credit risk.
method Combines tree-boosting with a latent spatio-temporal Gaussian process model.
result Predictive models outperform conventional methods due to non-linear and spatio-temporal effects.
System designs for analyzing and pricing non-performing consumer credit portfolios.
problem Technical challenges in analyzing and pricing portfolios of non-performing consumer credit loans.
method Bottom-up architecture, simultaneous quantile regression, R-copula, Gaussian one-factor copula model.
result Successfully developed a methodology for analyzing credit portfolio risks of consumer loans.
Study uses AI to refine loan assessments, improving credit default predictions.
problem Improving credit default prediction accuracy using AI-refined text.
method Comparative analysis of human-written and AI-refined loan assessments using deep learning techniques.
result AI-refined texts significantly enhance credit default predictions, especially when combined with structured data.
This study simulates biases in classifiers to assess fairness.
problem Mitigating biases in predictive models to ensure fairness.
method Agent-based model (ABM) to generate synthetic datasets with controlled biases, applied to offline and online learning approaches.
result Demonstrates how biases in data affect classifier outcomes and how mitigations impact feature usage.
In this paper we first introduce two new financial products: stock loan and capped stock loan. Then we develop a pure variational inequality method to establish explicitly the values of these stock loans. Finally, we work out ranges of fair values of parameters associated with the loans.
The paper analyzes Lending Club's loan applicants to predict default risk.
problem Predicting default risk in loan applicants of Lending Club.
method Exploratory data analysis and machine learning (Logistic Regression, Random Forest) were used.
result A credit derivative based on Credit Default Swap was designed to hedge default risk.
Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique t…
Economic effects on households of an augmentation of the cash back duration of real estate loanq-fin.CP
Examines how extending home loan durations affects French households financially.
problem Financial implications for households with extended home loan durations.
method Analysis of French and international home loan systems, including bullet loans and Japanese home loans.
result Extending home loan durations can reduce monthly payments but raises financial risks.
Proposes a deep neural network for predicting survival times with cure fractions.
problem Predicting survival times with cure fractions in medical studies.
method Flexible survival models integrated into a deep neural network framework.
result Better predictive performance and realistic covariate effects.
The paper is aware of the importance of certain figures that are essential to an understanding of Credit Scoring models in credit acceptance process optimization, namely if the power of discrimination measured by Gini value is increased by 5% then the profit of the process can be increased monthly by about 1 500 kPLN (…
Current auto loans converge to super-prime credit despite remaining underwater.
problem Inefficient consumer behavior in auto loans leading to suboptimal credit risk.
method Large-sample statistical hypothesis test on transition matrix between risk bands.
result All current risk bands converge to super-prime credit, despite remaining underwater.
A stock loan is a loan, secured by a stock, which gives the borrower the right to redeem the stock at any time before or on the loan maturity. The way of dividends distribution has a significant effect on the pricing of the stock loan and the optimal redeeming strategy adopted by the borrower. We present the pricing mo…
Extends ASRF model for green and brown loans, accounting for systematic and idiosyncratic risks.
problem Credit risk assessment for portfolios of green and brown loans.
method Two-factor copula structure, skewed distributions for systematic risk, Gaussian for idiosyncratic risk, non-uniform exposure setting.
result Portfolio loss convergence to a limit reflecting green and brown loan characteristics.
Paper solves stock loan pricing with finite maturity using integral equations.
problem Valuation of margin-call stock loans with finite maturities.
method Fourier Sine transform and Volterra integral equation approach.
result Integral representation of margin-call stock loan value.
Derivatives impact U.S. banking sector's systemic risk, but loan and leverage ratios are more significant.
problem Systemic risk in U.S. banking sector due to derivatives and loans.
method Analysis of derivatives and loan data to assess systemic risk.
result Loan and leverage ratios are more influential in systemic risk than derivatives holdings.
Stock loans with liquidationq-fin.PR
We derive a "semi-analytic" solution for a stock loan in which the lender forces liquidation when the loan-to-collateral ratio drops beneath a certain threshold. We use this to study the sensitivity of the contract to model parameters.
Optimal student loan repayment strategies vary based on loan size.
problem Finding the most cost-effective repayment strategy for federal student loans.
method Analyzing the impact of different repayment strategies on total cost for varying loan sizes.
result Optimal repayment strategies depend on the loan balance, with different approaches for small, large, and intermediate balances.
This paper works out fair values of stock loan model with automatic termination clause, cap and margin. This stock loan is treated as a generalized perpetual American option with possibly negative interest rate and some constraints. Since it helps a bank to control the risk, the banks charge less service fees compared …
Banks are interested in evaluating the risk of the financial distress before giving out a loan. Many researchers proposed the use of models based on the Neural Networks in order to help the banker better make a decision. The objective of this paper is to explore a new practical way based on the Neural Networks that wou…
Paper calculates loan loss after default using Bayesian model.
problem Determining loan loss after borrower default.
method Bayesian scheme considering repayment period, volumes, moments, and parameters.
result Allows setting LGD less than or equal to 1 for accurate estimates.
A stock loan is a contract whereby a stockholder uses shares as collateral to borrow money from a bank or financial institution. In Xia and Zhou (2007), this contract is modeled as a perpetual American option with a time varying strike and analyzed in detail within a risk--neutral framework. In this paper, we extend th…
Procedure verifies if machine learning models assign fixed predictions that preclude access.
problem Models assign fixed predictions that preclude access to credit and employment.
method Model-agnostic recourse verification with reachable sets.
result Models can inadvertently preclude access by assigning fixed predictions.
In 1979 following a decade of hyperinflation, Iceland introduced Verðtryggð lán, negatively amortised, index-linked loans whose outstanding principal is increased by the rate of the consumer price inflation index(CPI). The loans were part of a general government policy which used indexation to the CPI to address the ec…
Flashot visualizes Flash Loan attacks in DeFi systems.
problem Lack of standard tools to study Flash Loan attacks in DeFi.
method Proposes Flashot, a prototype to transparently illustrate asset flows.
result Demonstrates the effectiveness of Flashot in studying Flash Loan attacks.
The study examines how limited liability and haircut affect a bank's loan portfolio's liquidity risk.
problem Impact of limited liability and haircut on a bank's loan portfolio's liquidity risk.
method Constructed a novel loan portfolio model with limited liability and haircut constraint, analyzed at three time steps.
result Model with haircut constraint leads to lesser liquidity risk.
Zero-Liquidation loans protect ETH borrowers from liquidation risks.
problem Risk of liquidation in DeFi lending protocols.
method Allows borrowers to repay in either USDC or pledged ETH, compensating liquidity providers with higher yield.
result More robust and less contagion-prone lending compared to traditional protocols.
A new method detects and removes false trailing balances in credit data.
problem False trailing balances in credit data corrupt risk event timing.
method TruEnd-procedure defines and removes false trailing balances.
result Improved accuracy in predicting risk events and reducing credit losses.
The authors examine the concept of probability of default for asset-backed loans. In contrast to unsecured loans it is shown that probability of default can be defined as either a measure of the likelihood of the borrower failing to make required payments, or as the likelihood of an insufficiency of collateral value on…
We redefine SICR-events for better loan classification under IFRS 9.
problem Ambiguity in SICR-event definition under IFRS 9.
method Proposed alternative framework with three parameters: delinquency, stickiness, and outcome period. Varying these parameters, we generated 27 unique SICR-definitions and fitted logistic regression models.
result The proposed SICR-models outperform the PD-comparison approach as an early-warning system for credit losses.
Online Peer to Peer Lending (P2PL) systems connect lenders and borrowers directly, thereby making it convenient to borrow and lend money without intermediaries such as banks. Many recommendation systems have been developed for lenders to achieve higher interest rates and avoid defaulting loans. However, there has not b…
Quantum credit loansq-fin.GN
Quantum mechanics applied to credit loans for better repayment schedules.
problem Improving repayment schedules for credit loans.
method Introducing quantum mechanics concepts to credit loans, defining operators for debt, amortization, interest, and installments, and using SO(M) symmetry to optimize periodic payments.
result Optimized repayment schedules for borrowers without altering lender's earnings.