Introduction

Predictive analytics is transforming how banks and financial institutions manage their loan portfolios. This course offers in-depth training on leveraging predictive models to forecast loan defaults, segment credit risk, and optimize lending decisions. It is designed to empower finance professionals to harness historical and real-time data to improve credit assessment and loan monitoring.

Participants will explore machine learning techniques, statistical methods, and advanced data modeling used to identify patterns in borrower behavior, payment risk, and portfolio stress. The course includes hands-on case studies, enabling learners to build and validate models that help anticipate delinquency, restructure loans proactively, and improve portfolio quality.

General case studies showcase global examples of institutions using predictive analytics to manage retail, SME, and mortgage loan portfolios. These practical scenarios help participants connect theory with real-world outcomes in both traditional and digital lending environments.

Ideal for credit risk professionals, loan officers, data scientists, and fintech leaders, the course offers a practical roadmap to embed predictive capabilities into the core of lending operations.

Course Objectives
Understand core concepts of predictive analytics
Use historical data to model credit performance
Build models to predict loan defaults
Segment customers by risk profile
Forecast non-performing loans (NPL)
Use logistic regression, decision trees, and other models
Validate and interpret model outputs
Automate risk alerts and recommendations
Apply analytics to loan restructuring
Integrate predictive models with lending systems

Organizational Benefits
Enhance credit decision accuracy
Reduce loan losses through early detection
Improve portfolio risk segmentation
Increase approval rates for quality customers
Automate credit scoring and assessment
Optimize loan collection strategies
Strengthen compliance through model transparency
Improve capital planning via forecasting
Enhance customer targeting and pricing
Drive innovation in loan product design

Target Participants
Credit risk analysts
Loan officers and managers
Portfolio analysts
Data science teams in finance
Digital lending platform leads
Retail and SME credit heads
Collections and recovery teams
Business intelligence professionals
Compliance and audit personnel
Fintech product managers

Course Outline

Module 1: Overview of Predictive Analytics in Lending
Evolution of analytics in credit
Key concepts and terminologies
Supervised learning for loan outcomes
Types of models used in lending
Value of prediction vs. description
General case study: Evolution of scoring in a digital bank

Module 2: Data Preparation and Exploration
Data sources in lending
Data cleaning and preprocessing
Feature engineering for credit models
Handling missing values
Exploratory data analysis (EDA)
General case study: Preparing credit card loan dataset

Module 3: Building Credit Scoring Models
Logistic regression basics
Probability thresholds and odds
Interpreting coefficients
Model validation and AUC
Scorecard development
General case study: Retail loan scoring application

Module 4: Machine Learning for Risk Prediction
Decision trees and random forests
Gradient boosting (XGBoost)
Hyperparameter tuning
Avoiding overfitting and bias
Model performance metrics
General case study: Default prediction for SME loans

Module 5: Time-Series Forecasting for NPL
Understanding time-series data
ARIMA and exponential smoothing
Forecasting trends and cycles
Seasonality in loan data
NPL forecasting techniques
General case study: NPL forecast for consumer lending

Module 6: Portfolio Risk Segmentation
Clustering borrowers by risk
K-means and hierarchical methods
Behavioral risk segmentation
Loss Given Default (LGD) analytics
Portfolio concentration analysis
General case study: Risk-based loan pricing

Module 7: Churn and Early Warning Signals
Identifying early warning indicators
Customer churn prediction models
Triggers for intervention
Behavioral scoring with transactions
Lead indicators of delinquency
General case study: Predicting pre-default behaviors

Module 8: Model Deployment and Monitoring
Model lifecycle in lending
Deployment tools and platforms
Monitoring model drift
Updating and retraining models
Dashboard integration
General case study: Integrating predictive model in loan system

Module 9: Ethics and Explainability in Credit Models
Bias and fairness in ML
Transparency and explainable AI
Customer rights and disclosures
Regulatory expectations
Interpreting black-box models
General case study: Ensuring fairness in scoring

Module 10: Predictive Analytics in Collections
Prioritizing collections using prediction
Behavioral scoring for recovery
Offer and settlement optimization
Contact strategy based on risk
Predicting legal recovery success
General case study: Analytics in digital collections

Module 11: Product Innovation through Prediction
Loan personalization based on risk
Predictive pricing models
Dynamic credit limit adjustment
Pre-approved offer targeting
Behavior-linked loyalty features
General case study: Risk-based loan offer engine

Module 12: Scaling Predictive Analytics Across Lending
Setting up analytics teams
Data governance in modeling
Infrastructure and tooling
Agile model development
Continuous learning and updates
General case study: Scaling analytics in enterprise lending

Essential Information

1. Our courses are customizable to suit the specific needs of participants.
2. Participants are required to have proficiency in the English language.
3. Our training sessions feature comprehensive guidance through presentations, practical exercises, web-based tutorials, and collaborative group activities. Our facilitators boast extensive expertise, each with over a decade of experience.
4. Upon fulfilling the training requirements, participants will receive a prestigious Global King Project Management certificate.
5. Training sessions are conducted at various Global King Project Management Centers, including locations in Nairobi, Mombasa, Kigali, Dubai, Lagos, and others.
6. Organizations sending more than two participants from the same entity are eligible for a generous 20% discount.
7. The duration of our courses is adaptable, and the curriculum can be adjusted to accommodate any number of days.
8. To ensure seamless preparation, payment is expected before the commencement of training, facilitated through the Global King Project Management account.
9. For inquiries, reach out to us via email at training@globalkingprojectmanagement.org or by phone at +254 114 830 889.
10. Additional amenities such as tablets and laptops are available upon request for an extra fee. The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a certificate of successful completion. Participants are responsible for arranging and covering their travel expenses, including airport transfers, visa applications, dinners, health insurance, and any other personal expenses.