Page 49 - Banking Finance October 2025

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Transactional Data: Transaction volumes, types, Pattern Recognition:
frequency, and patterns. Trends Over Time: Analyse how default rates have
Repayment History: Past loan repayment records, changed over the past few years.
defaults, delinquencies. Cluster Analysis: Identify clusters of customers with
External Data Sources: similar financial behaviours and risk profiles.
Credit Bureau Scores: Information from credit 4. Data Modeling
rating agencies.
Model Selection:
Economic Indicators: Inflation rates, unemployment Logistic Regression: Chosen for its interpretability
rates, GDP growth. and efficiency in binary classification tasks.
Social Data: Social media behaviour, online reviews, Random Forest: Selected for its ability to capture
public records. complex interactions and improve predictive
2. Data Preparation accuracy.
Data Cleaning: Training the Models:
Handling Missing Values: Compute missing income Data Splitting: Divide the dataset into training and
data with median values; remove records with testing sets to evaluate model performance.
critical missing information. Model Training: Train logistic regression and
Removing Duplicates: Eliminate duplicate random forest models using the training data.
customer records to ensure data integrity. Model Validation:

Correcting Errors: Standardize address formats, Performance Metrics: Evaluate models using
rectify inconsistent date entries. accuracy, precision, recall, F1-score, and ROC-AUC.
Data Transformation: Cross-Validation: Implement k-fold cross-validation
Normalization: Scale income and loan amounts to to ensure the models' robustness and
a standard range. generalizability.
Encoding Categorical Variables: Convert Model Refinement:
categorical data like occupation and marital status Hyperparameter Tuning: Optimize parameters
into numerical formats. like regularization strength in logistic regression and
the number of trees in random forest.
Feature Engineering: Create new features such as
debt-to-income ratio, loan-to-value ratio, and Ensembling: Combine predictions from multiple
average transaction amount. models to improve overall performance.
Data Integration: 5. Deployment
Merging Internal and External Data: Combine Integration into Loan Approval System: Embed
data from internal databases with external sources the predictive models into the bank's loan
to create a comprehensive dataset. processing workflow to assess the risk of new loan
Ensuring Consistency: Align data formats, units, applications in real-time.
and naming conventions across different sources. User Interface: Develop dashboards and reporting
3. Exploratory Data Analysis (EDA) tools that present model outputs in an accessible
and actionable format for bank officers.
Visualization:
6. Monitoring and Maintenance
Default Rates by Demographics: Use bar charts
to visualize default rates among various age groups, Performance Tracking: Continuously monitor the
occupations, and regions. models' accuracy and other performance metrics
using real-time data.
Correlation Heatmap: Identify correlations
between financial variables and default rates. Feedback Loop: Incorporate feedback from loan

44 | 2025 | OCTOBER | BANKING FINANCE