Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Actuarial Approach01:20

Actuarial Approach

278
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
278
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

476
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
476
Aggregates Classification01:29

Aggregates Classification

953
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
953
Classification of Systems-I01:26

Classification of Systems-I

540
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
540
Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

221
Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
221
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

264
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
264

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

ssNetShift: single-sample metabolic network rewiring reveals hidden prognostic subtypes beyond clinical staging in gastric cancer.

Briefings in bioinformatics·2026
Same author

LSTM-augmented DQN for quantitative trading in partially observable markets.

Scientific reports·2026
Same author

Nontargeted screening of carbonyl-containing hormonal residues in meat products for food safety by using intelligent high-resolution mass spectrometry-based workflow.

Food chemistry·2026
Same author

prepIMS: a robust data preprocessing workflow for ion mobility mass spectrometry imaging.

Analytica chimica acta·2026
Same author

Positron emission tomography-based intratumoral spatial diversity for prognosis in nasopharyngeal carcinoma.

Oral oncology·2026
Same author

Local Sample Cohesion Normalization: Preserving Inherent Biological Heterogeneity in Metabolomics Data.

Analytical chemistry·2025
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jan 9, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K

NERHF: a hybrid machine learning-driven efficient credit risk control framework.

Lin Wei1, Jiyang Dong2, Hanyue Yu1

  • 1School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, 116025, Liaoning, China.

Scientific Reports
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid machine learning framework (NERHF) for effective credit risk control. The framework improves credit risk prediction and generates optimal strategies to mitigate financial default risks.

Keywords:
Credit risk assessmentDeep reinforcement learningEnsemble learningNeural network modelsRisk controlRisk prediction

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K

Related Experiment Videos

Last Updated: Jan 9, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K

Area of Science:

  • Financial Technology
  • Machine Learning
  • Risk Management

Background:

  • Credit operations involve significant risks, making accurate credit risk control essential for financial institutions.
  • Effective risk management impacts lending decisions and overall institutional stability.

Purpose of the Study:

  • To propose a novel hybrid machine learning framework (NERHF) for efficient credit risk control.
  • To enhance credit risk prediction accuracy and develop optimal risk mitigation strategies.

Main Methods:

  • Utilized neural network algorithms for feature extraction from credit data.
  • Employed ensemble learning algorithms for credit risk prediction based on extracted features.
  • Applied an improved deep reinforcement learning algorithm (Pre-DDQN) for generating optimal credit risk control strategies.

Main Results:

  • The hybrid framework (NERHF) demonstrated significant advantages in credit risk prediction.
  • Recurrent neural networks combined with lightweight gradient boosting machines showed particular effectiveness.
  • The Pre-DDQN algorithm outperformed comparative methods in credit risk control strategy generation.

Conclusions:

  • The proposed NERHF framework offers a robust solution for credit risk control.
  • The findings highlight the potential of advanced machine learning techniques in mitigating financial default risks.
  • The study underscores the practical applicability of NERHF in the financial industry.