AI-Based Credit Scoring & Alternative Data Use Training Course

Introduction

This intensive 5-day training course provides a comprehensive and practical exploration of AI-based credit scoring and the transformative use of alternative data. In a world where a significant portion of the population remains "credit invisible" due to a lack of traditional credit history, AI and alternative data are revolutionizing access to finance. This program will equip participants with an in-depth understanding of how machine learning algorithms analyze vast and diverse datasets—from transactional behavior to mobile phone usage—to accurately assess creditworthiness, expand financial inclusion, and mitigate risk in ways previously impossible.

The course goes beyond theoretical concepts, focusing on real-world applications, practical challenges, and the ethical considerations inherent in this cutting-edge field. Through interactive workshops, hands-on exercises with data (simulated where necessary), and discussions of regulatory compliance, attendees will learn to identify relevant alternative data sources, build and validate AI/ML credit models, interpret model outcomes, and address potential biases. Whether you are a credit risk analyst, data scientist, product manager, regulator, or a financial services executive seeking to innovate, this program offers an unparalleled opportunity to master the essential aspects of AI-based credit scoring and leverage alternative data for a competitive edge and greater financial inclusion.

Duration: 5 days

Target Audience:

  • Credit Risk Managers and Analysts
  • Data Scientists and Machine Learning Engineers
  • Quantitative Analysts
  • Fintech Product Managers and Innovators
  • Digital Lending Specialists
  • Compliance Officers and Regulators
  • Business Development Managers in Financial Services
  • AI Strategy and Innovation Leaders

Objectives:

  • To provide a comprehensive understanding of AI/ML fundamentals applied to credit scoring.
  • To equip participants with knowledge of various alternative data sources and their utility in credit assessment.
  • To understand the process of building, validating, and deploying AI-based credit scoring models.
  • To explore the ethical considerations, regulatory challenges, and bias mitigation strategies in AI credit scoring.
  • To enable participants to strategically integrate AI and alternative data into their lending operations.

Course Modules:

Introduction

  • Defining AI-based credit scoring: evolution from traditional methods.
  • What is alternative data and why is it crucial for financial inclusion and enhanced risk assessment?
  • The synergy between AI/ML and alternative data in modern lending.
  • Key benefits: speed, accuracy, expanded reach, and personalized offers.
  • Overview of the course objectives and module structure.

Fundamentals of AI and Machine Learning for Credit Scoring

  • Introduction to Artificial Intelligence, Machine Learning, and Deep Learning concepts.
  • Supervised vs. Unsupervised Learning in credit assessment.
  • Key ML algorithms for classification and regression in credit scoring (e.g., Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, Neural Networks).
  • Feature engineering: transforming raw data into predictive features.
  • Model training, validation, and testing principles.

Traditional Data vs. Alternative Data in Credit Scoring

  • Limitations of traditional credit bureau data (thin files, stale data).
  • Types of Alternative Data:
    • Transactional data (bank statements, payment history).
    • Behavioral data (app usage, Browse patterns, social media - with ethical considerations).
    • Utility and rental payment histories.
    • Educational and employment data.
    • Psychometric and behavioral economics data.
  • Data acquisition, cleaning, and preparation for alternative data.
  • Data quality, accuracy, and reliability assessment for alternative data sources.

Building AI-Based Credit Scoring Models

  • Data ingestion and pipeline design for diverse data sources.
  • Pre-processing techniques: normalization, scaling, handling missing values, outlier detection.
  • Feature selection and dimensionality reduction for complex datasets.
  • Model selection and hyperparameter tuning.
  • Practical exercises in building a simple credit scoring model using an AI/ML library.

Model Interpretation, Explainability, and Bias Mitigation (XAI)

  • Understanding "black box" models and the need for interpretability.
  • Techniques for model interpretability: SHAP values, LIME, Partial Dependence Plots.
  • Identifying and mitigating bias in AI credit scoring models (e.g., disparate impact, protected classes).
  • Fairness metrics and bias detection tools.
  • Ensuring ethical AI in lending: transparency and accountability.

Implementation and Deployment of AI Models

  • MLOps (Machine Learning Operations) for deploying and managing models in production.
  • Real-time scoring via API integration.
  • Continuous monitoring of model performance and drift detection.
  • Retraining strategies and model governance.
  • Scalability and infrastructure considerations for AI-powered lending.

Regulatory Landscape and Data Privacy

  • Current and emerging regulations governing AI in finance and alternative data use (e.g., FCRA, ECOA, GDPR, specific regional laws).
  • Consent management for collecting and using alternative data.
  • Data security and privacy best practices.
  • Regulatory sandboxes and their role in fostering innovation.
  • Compliance challenges and legal implications of AI credit scoring.

Strategic Applications and Future Trends

  • Expanding financial inclusion through AI and alternative data.
  • Personalized lending products and dynamic pricing.
  • Fraud detection and early warning systems leveraging AI.
  • Competitive advantages for lenders adopting AI-based credit scoring.
  • Future of credit scoring: federated learning, verifiable credentials, hyper-personalization, Open Finance.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

 

Ai-based Credit Scoring & Alternative Data Use Training Course in Venezuela (Bolivarian Republic of)
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