risk modeling | BestHub

DataFunSummit

Mar 22, 2024 · Artificial Intelligence

Risk Control Model Construction for Online Small Loans: Pre‑loan, In‑loan, Post‑loan and Monitoring

This article presents a comprehensive overview of risk control model building for online small‑loan scenarios, covering pre‑loan, in‑loan and post‑loan stages, the associated data pipelines, model deployment strategies, optimization attempts, and monitoring frameworks to ensure accuracy, stability and effectiveness.

credit scoringdata pipelineloan management

0 likes · 16 min read

Risk Control Model Construction for Online Small Loans: Pre‑loan, In‑loan, Post‑loan and Monitoring

Model Perspective

Jan 14, 2024 · Fundamentals

How Graph Theory Can Predict Global War Risks: A Quantitative Model

This article presents a graph‑theory based mathematical model that treats nations as nodes and their relationships as weighted edges, using centrality metrics to quantitatively assess and forecast potential war risks, illustrated with a 2024 case study of key global regions and an adjacency matrix.

centrality metricsgraph theoryinternational relations

0 likes · 9 min read

How Graph Theory Can Predict Global War Risks: A Quantitative Model

DataFunTalk

May 28, 2023 · Artificial Intelligence

Applying External Data in Consumer Credit Risk Management: Framework, Evaluation, and Joint Modeling

This article presents a comprehensive overview of using external data in consumer credit risk management, covering the risk operating framework, data types, challenges of data integration, evaluation methods, joint modeling techniques, and practical solutions to improve model performance and business outcomes.

credit riskdata evaluationexternal data

0 likes · 18 min read

Applying External Data in Consumer Credit Risk Management: Framework, Evaluation, and Joint Modeling

DataFunTalk

Jan 2, 2023 · Artificial Intelligence

Tail Traffic Modeling and Data‑Driven Risk Strategies at 360 Shuke

This article presents 360 Shuke's practical approach to modeling low‑volume (tail) credit traffic using accumulated data, covering the characteristics of tail traffic, sample expansion under low approval rates, timeliness‑based data clustering, and ranking optimization for high‑quality head customers.

data clusteringmodel optimizationrisk modeling

0 likes · 19 min read

Tail Traffic Modeling and Data‑Driven Risk Strategies at 360 Shuke

DataFunSummit

May 23, 2022 · Artificial Intelligence

Applying Graph Machine Learning for Intelligent Anti‑Fraud: Models, Algorithms, and Real‑World Applications

This article explores how graph machine learning can be leveraged for intelligent anti‑fraud, covering business background, common fraud models and graph algorithm principles, practical deployment of graph algorithms, challenges in fraud modeling, and future research directions.

Graph Machine Learningfraud detectiongraph algorithms

0 likes · 20 min read

Applying Graph Machine Learning for Intelligent Anti‑Fraud: Models, Algorithms, and Real‑World Applications

DataFunSummit

May 3, 2022 · Artificial Intelligence

Scientific Data Definition, Application, Evaluation, and Explanation for Financial Risk Modeling

This presentation explores how to scientifically define, apply, evaluate, and interpret data in financial risk management, covering data alignment with business goals, feature selection, model metrics such as KS and PSI, handling pandemic effects, and methods for model explainability.

Feature Engineeringdata sciencefinancial data

0 likes · 13 min read

Scientific Data Definition, Application, Evaluation, and Explanation for Financial Risk Modeling

DataFunTalk

Apr 25, 2022 · Artificial Intelligence

Scientific Data Definition, Application, Evaluation, and Explanation in Financial Risk Modeling

This presentation explores how to scientifically define, apply, evaluate, and interpret data in financial risk management, covering data alignment with business goals, feature selection, model metrics like KS and PSI, handling pandemic impacts, and methods for model explanation and improvement.

KS metricPSIdata science

0 likes · 14 min read

Scientific Data Definition, Application, Evaluation, and Explanation in Financial Risk Modeling

DataFunTalk

Mar 18, 2022 · Artificial Intelligence

Alternative Data Mining: From 19th‑Century Cholera Mapping to Modern AI‑Driven Risk Modeling

This talk reviews the concept of alternative data, illustrates its early use in John Snow's cholera map, explores contemporary AI‑powered systems such as IBM's Debater and satellite‑based poverty estimation, and presents the speaker's own research on using unconventional data for financial‑market risk detection and prediction.

Artificial IntelligenceData Miningalternative data

0 likes · 14 min read

Alternative Data Mining: From 19th‑Century Cholera Mapping to Modern AI‑Driven Risk Modeling

DataFunSummit

Sep 20, 2021 · Artificial Intelligence

Graph Algorithm Applications in Douyu Live Stream Anti‑Cheat: Architecture, Evolution, Modeling, and Case Studies

This article explains how Douyu leverages graph algorithms for live‑stream traffic anti‑cheat, detailing the platform’s risk scenarios, the overall graph architecture, its evolution, modeling workflow, practical case studies, and the resulting improvements in fraud detection and interpretability.

AILive Streaminganti-cheat

0 likes · 16 min read

Graph Algorithm Applications in Douyu Live Stream Anti‑Cheat: Architecture, Evolution, Modeling, and Case Studies

DataFunSummit

Jul 31, 2021 · Artificial Intelligence

Credit Risk Strategies: From Rule‑Based Scoring to Machine Learning Models

This article presents a comprehensive overview of credit risk control strategies, covering industry background, traditional scoring‑card development, data integration, feature engineering, model evaluation, rate and limit optimization, and advanced machine‑learning approaches for loan underwriting.

Scoringcredit riskfinancial analytics

0 likes · 11 min read

Credit Risk Strategies: From Rule‑Based Scoring to Machine Learning Models

DataFunTalk

Dec 8, 2020 · Artificial Intelligence

Financial Big Data Risk Control Models: Techniques, Applications, and COVID‑19 Challenges

This article presents a comprehensive overview of financial big‑data risk control models at Du Xiaoman, covering traditional scoring cards, AI‑driven time‑series and text processing, graph‑based networks, model interpretability, probability calibration, stability analysis, and the specific challenges introduced by the COVID‑19 pandemic.

Artificial IntelligenceBig Datacredit scoring

0 likes · 14 min read

Financial Big Data Risk Control Models: Techniques, Applications, and COVID‑19 Challenges

DataFunTalk

Jun 22, 2020 · Artificial Intelligence

Ctrip's Automated Iterative Anti‑Fraud Modeling Framework for Payment Risk

The article describes Ctrip's payment fraud risk characteristics, a comprehensive automated iterative anti‑fraud model framework—including variable system, GAN‑augmented sample generation, RNN behavior encoding, and tree‑based classifiers—and demonstrates how this approach restores recall performance compared with traditional static models.

GANRNNanti-fraud

0 likes · 12 min read

Ctrip's Automated Iterative Anti‑Fraud Modeling Framework for Payment Risk

DataFunTalk

Sep 2, 2019 · Artificial Intelligence

Credit Risk Strategies: Data, Rules, and Model Development for Consumer Lending

This article presents a comprehensive overview of consumer credit risk management, covering industry background, traditional scoring‑card and machine‑learning model development processes, risk‑rate and limit strategies, rule effectiveness diagnostics, and advanced model‑optimization techniques to improve underwriting performance and cost efficiency.

consumer lendingcredit risklimit strategy

0 likes · 10 min read

Credit Risk Strategies: Data, Rules, and Model Development for Consumer Lending

DataFunTalk

Aug 22, 2019 · Artificial Intelligence

End‑to‑End Group Risk Perception Modeling: From Requirement Mining to Deployment

This article presents a comprehensive workflow for group risk perception, covering business requirement mining, data acquisition and understanding, feature engineering, model training and evaluation, deployment, and practical user applications, with detailed objectives, methods, and deliverables for each stage.

Data MiningFeature EngineeringModel Deployment

0 likes · 11 min read

End‑to‑End Group Risk Perception Modeling: From Requirement Mining to Deployment

DataFunTalk

Jun 12, 2019 · Artificial Intelligence

Credit Scoring Cards vs Machine Learning in Financial Risk Control: Comparative Analysis and Practical Applications

The article compares traditional credit‑scoring‑card models with modern machine‑learning approaches for financial risk control, detailing feature selection criteria, non‑linear handling, data characteristics, practical ML techniques, large‑scale modeling challenges, and summarizing insights for future development.

Feature Engineeringcredit scoringfinancial risk

0 likes · 14 min read

Credit Scoring Cards vs Machine Learning in Financial Risk Control: Comparative Analysis and Practical Applications

DataFunTalk

Jun 6, 2019 · Artificial Intelligence

Design and Machine Learning Practices for Automotive Finance Risk Control

This article outlines the end‑to‑end design of automotive finance risk‑control processes, discusses key data integrity and customer segmentation considerations, and details machine‑learning modeling practices—including logistic regression, decision trees, GBDT, XGBoost, LightGBM and CatBoost—along with an automated platform to streamline model development and deployment.

Automotive FinanceData IntegrityGBDT

0 likes · 17 min read

Design and Machine Learning Practices for Automotive Finance Risk Control

DataFunTalk

Apr 17, 2019 · Artificial Intelligence

Evolution of Ctrip Financial Risk Control Models: From Data Platform to AI‑Driven Scoring and Anti‑Fraud Systems

This report details Ctrip Financial's end‑to‑end risk control development, covering business overview, a three‑layer data platform, the progression of credit scoring and anti‑fraud models from rule‑based to advanced AI techniques, and the evaluation, monitoring, and social‑network‑based fraud detection strategies employed.

Big DataFinancial AIanti-fraud

0 likes · 16 min read

Evolution of Ctrip Financial Risk Control Models: From Data Platform to AI‑Driven Scoring and Anti‑Fraud Systems