Enterprise AI Analysis
Machine learning powered financial credit scoring: a systematic literature review
This systematic literature review (SLR) provides a comprehensive analysis of Machine Learning (ML) applications in financial credit scoring, covering 63 research papers published between 2018 and 2024. It identifies major ML methodologies, evaluates their strengths and limitations, and highlights emerging trends and challenges. The study aims to contribute to understanding effective ML techniques and guides future research in ML-based credit scoring, focusing on improving accuracy, fairness, and efficiency while promoting financial inclusion.
Authors: Helmi Ayari, Pr. Ramzi Guetari, Pr. Naoufel Kraïem
Publication Date: Published online: 18 November 2025
Executive Impact Summary
Machine Learning significantly enhances financial credit scoring by offering robust, data-driven assessments that reduce default risk and streamline operations. This leads to more equitable and efficient lending decisions, fostering greater financial stability and inclusion.
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Peak Accuracy Achieved
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Improved Risk Detection
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Enhanced Operational Efficiency
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Alternative Data Sources Integrated
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Exploring Machine Learning Models for Credit Scoring
The review categorizes ML models into traditional, deep learning, and ensemble, often incorporating hybrid approaches for enhanced performance. Each category offers distinct advantages for assessing creditworthiness.
| Model Type | Key Strengths | Key Limitations |
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| Traditional ML (LR, DTs, SVM) |
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| Deep Learning (ANN, CNN, LSTM) |
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| Ensemble Learning (RF, XGBoost, GBDT) |
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| Hybrid Models |
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Critical Challenges in Adopting ML for Credit Scoring
Despite the immense potential of ML, its adoption in credit scoring faces several significant hurdles that require careful consideration for successful and ethical deployment.
- Utilizing XAI tools like LIME and SHAP
- Developing inherently interpretable models
- Balancing model complexity with explainability
- Pre-processing (data manipulation)
- In-processing (algorithm adjustments)
- Post-processing (output refinement)
- Effective feature selection methods (filter, wrapper, embedded)
- Dimensionality reduction techniques (e.g., PCA)
- Careful hyperparameter tuning
Key Emerging Trends and Advances
The field of ML-powered credit scoring is rapidly evolving, with new approaches and technologies continually enhancing predictive accuracy and operational efficiency.
Advancing Financial Inclusion with Alternative Data
The review highlights how alternative data sources, such as social media, mobile phone usage, and psychometric assessments, are transforming credit scoring. This approach enables lenders to evaluate the creditworthiness of individuals lacking traditional financial histories, promoting financial inclusion for underserved populations. Models leveraging these diverse data types offer new pathways for risk assessment.
Key Learnings:
- Expanded access to credit for thin-file borrowers.
- Improved predictive accuracy beyond traditional metrics.
- Ethical considerations for data privacy and bias are critical for responsible deployment.
SHAP & LIME
Leading Explainable AI Tools for Model Transparency
These advances underscore a shift towards more sophisticated, ethical, and inclusive credit scoring systems. The integration of explainable AI tools like SHAP and LIME is becoming paramount for regulatory compliance and building trust, ensuring models are not only accurate but also transparent and fair in their decision-making processes.
Systematic Literature Review Process
Our systematic review followed rigorous PRISMA 2020 guidelines to ensure transparency, reproducibility, and minimize bias. The process involved a structured sequence of steps for identifying, screening, and selecting relevant studies.
Enterprise Process Flow
Identification
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Screening
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Eligibility
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Inclusion
This systematic approach ensured that only high-quality, relevant studies published between 2018 and 2024 were included, providing a contemporary and robust foundation for our analysis of ML in financial credit scoring.
Estimate Your Enterprise AI ROI
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Projected Annual Cost Savings
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Your AI Implementation Roadmap
A phased approach to integrate AI into your credit scoring processes, ensuring a smooth transition and maximizing value.
Phase 1: Discovery & Strategy Alignment
Conduct a thorough assessment of existing credit scoring models, data infrastructure, and business objectives. Define clear AI goals, identify key use cases, and establish performance benchmarks. This phase sets the foundation for a successful AI integration, ensuring all stakeholders are aligned.
Phase 2: Data Preparation & Model Selection
Gather, clean, and preprocess relevant traditional and alternative data sources. This includes addressing class imbalance and ensuring data quality. Select the most suitable ML models (e.g., hybrid ensembles, advanced DL) based on data characteristics and desired interpretability, and begin initial model training.
Phase 3: Model Development & Validation
Iteratively develop and fine-tune selected ML models, incorporating feature engineering and optimization techniques. Rigorously validate models using appropriate metrics (Accuracy, AUC, F1-score) and ensure robustness against diverse datasets. Address potential biases and interpretability challenges using XAI tools.
Phase 4: Pilot Deployment & Regulatory Compliance
Implement a pilot program to test the AI-powered credit scoring system in a controlled environment. Monitor performance, gather feedback, and ensure full compliance with financial regulations (e.g., IFRS 9, GDPR) and ethical guidelines. Refine models based on real-world performance.
Phase 5: Full-Scale Integration & Continuous Optimization
Roll out the AI system across the enterprise, integrating it with existing financial systems. Establish continuous monitoring, retraining mechanisms, and performance tracking to ensure long-term effectiveness. Explore new data sources and model advancements to maintain a competitive edge and adapt to evolving market conditions.
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