Synthetic Data

Credit Card Default Prediction Using Synthetic Datasets

Team Syncora

August 8, 2025

As per a study carried out, global credit card defaults pose significant risks for financial institutions worldwide.

As AI is integrating into many fields, including finance and banking, it’s more important than ever to train financial models using datasets that include default patterns and risk signals.

But the question remains: where do you get a real-world credit card default dataset when such data is wrapped in complex compliance regulations?

The answer is synthetic data: it is privacy-safe and compliant with regulatory norms in the finance industry. You can generate synthetic data for finance with synthetic data generation tools or download a ready-to-use synthetic credit card default dataset with 50K entries.

Let’s see in detail.

What is a Credit Card Default Dataset?

A credit card default dataset is a collection of client records and payment histories. It is used to train machine learning models to classify whether a client will default on their next payment. These datasets typically include demographic details, credit behavior, repayment history, and a binary target indicating default or no default.

Traditionally, these datasets use real client data, which raises privacy concerns and makes it hard to comply with regulations like GDPR and other financial laws. Synthetic data generation bridges this gap by producing privacy-safe credit data that closely resembles real-world distributions without exposing sensitive information.

Where to Get the Synthetic Credit Card Default Dataset?

You can get a credit risk modeling synthetic dataset generated with Syncora.ai for free below. It is a high-fidelity synthetic financial dataset designed for AI, machine learning modeling, and credit risk assessment and is privacy-safe and compliant with GDPR and other laws.

Features of this Dataset

Our synthetic financial dataset for AI is modeled after the widely used UCI Credit Card Default dataset from Taiwan, but removes all privacy risks by generating entirely synthetic records. Below are features of our free downloadable dataset:

LIMIT_BAL: Credit limit of the client (numeric).

SEX: Gender indicator (1 = male, 2 = female).

EDUCATION: Educational level.

MARRIAGE: Marital status (1 = married, 2 = single, 3 = others).

AGE: Age in years (integer).

PAY_0 to PAY_6: Past monthly repayment status indicators (categorical, -2 to 8).

BILL_AMT1 to BILL_AMT6: Historical bill amounts for the last six months (numeric).

PAY_AMT1 to PAY_AMT6: Historical repayment amounts for the last six months (numeric).

default.payment.next.month: Target variable (0 = no default, 1 = default).

All records are synthetic, but keep the real-world patterns needed to build strong credit risk models.

Dataset Characteristics and Format

This synthetic financial dataset for AI replicates realistic credit card client behavior while ensuring 100% privacy safety. Here are a few characteristics of this dataset:

Size: 50,000 fully synthetic records modeled on real-world credit risk patterns.

Variables: Includes demographics (age, sex, education, marital status), credit behavior (limits, bill amounts, repayment status), and a binary target indicating default (0 = no default, 1 = default).

Type: Privacy-safe credit data generated using advanced AI synthesis, with statistical properties aligned to real datasets.

Format: Ready-to-use CSV compatible with Python, R, Excel, and other data tools.

Data Balance: Maintains a realistic target class distribution for the dataset for classification use cases.

Utility: Preserves feature relationships for accurate machine learning model training and testing.

Compliance: 0% PII leakage.

Common Banking and Finance AI Use Cases with This Dataset

With the credit card default database, you can

Build binary classification models (logistic regression, random forests, XGBoost, or neural networks) to predict default risk.

Create new features like credit usage, payment consistency, and bill changes to improve accuracy.

Use LIME or SHAP to understand which factors influence default risk.

Compare accuracy, precision, and recall across different models.

Use it for educational purposes.

How to Generate Synthetic Credit Card Default Data in 2025?

You can create credit card default datasets in two ways:

A) Manual Method:

Start with real or sample data (if available).

Pick the features you want, like demographics, payment history, or credit usage.

Create synthetic samples using rules, statistics, or AI models like GANs.

Check the data for accuracy, balance, and realism.

B) Using Synthetic Data Generation Platform

Upload your raw data here.

AI agents instantly clean, structure, and generate synthetic data.

Download a ready-to-use, privacy-safe credit card default dataset in minutes.

FAQs

What is synthetic credit card default data, and how is it different from real credit card data?

Synthetic data is artificially generated data that mimics the patterns, distributions, and relationships found in real credit card default data but contains no actual customer information. Because of this, no privacy concerns or regulatory compliance issues arise while using data.

Can synthetic data be used to improve credit risk prediction in practical financial institutions?

Yes, synthetic data allows financial institutions to safely develop, test, and refine credit risk models without exposing sensitive customer data.

To Sum it Up

Synthetic datasets make credit card default prediction easier, safer, and fully compliant with financial regulations. They offer realistic patterns without exposing sensitive data, making them perfect for AI training, testing, and education. Whether you create one manually or use a synthetic data generation platform, synthetic data gives you the flexibility to build accurate, explainable, and reliable credit risk models. With ready-to-use credit cards default datasets like the one from Syncora.ai, financial teams can innovate confidently while meeting compliance standards.