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How it works

Comprehensive exploratory analysis of credit card transactions revealing patterns, distributions, and statistical relationships that distinguish fraudulent from legitimate activity.

Analyze transaction features to understand which behavioral and transactional patterns characterize fraud vs. legitimate activity?

Built for: Fraud analysts, fraud data scientists, financial crime analysts, risk managers, compliance officers, data scientists in banking and fintech

Typical data source: Transaction-level datasets with fraud labels, PCA-transformed features, transaction amounts, and timestamps (typically 500-1M+ rows)

bankingfinteche-commercepayment_processinginsurance

What data do you need?

Dataset with 31 columns

fraud_flag (binary)	transaction_amount (numeric)	transaction_time (numeric)	pca_component_1 (numeric)	pca_component_2 (numeric)	pca_component_3 (numeric)	pca_component_4 (numeric)	pca_component_5 (numeric)	pca_component_6 (numeric)	pca_component_7 (numeric)	pca_component_8 (numeric)	pca_component_9 (numeric)	pca_component_10 (numeric)	pca_component_11 (numeric)	pca_component_12 (numeric)	pca_component_13 (numeric)	pca_component_14 (numeric)	pca_component_15 (numeric)	pca_component_16 (numeric)	pca_component_17 (numeric)	pca_component_18 (numeric)	pca_component_19 (numeric)	pca_component_20 (numeric)	pca_component_21 (numeric)	pca_component_22 (numeric)	pca_component_23 (numeric)	pca_component_24 (numeric)	pca_component_25 (numeric)	pca_component_26 (numeric)	pca_component_27 (numeric)	pca_component_28 (numeric)

Minimum 100 rows

What's in the report?

Fraud Eda Tf093D2 analysis with interactive charts, tables, and AI insights.

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Class Imbalance

Interactive bar visualization

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Transaction Amount by Fraud Status

Interactive box visualization

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Transaction Amount — Density by Class

Interactive violin visualization

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PCA Component Fraud Signals

Interactive horizontal_bar visualization

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Temporal Fraud Patterns

Interactive heatmap visualization

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Feature Correlation Matrix

Interactive heatmap visualization

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Summary Statistics by Class

Interactive table visualization

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AI Insights

Plain-English interpretation — what the numbers mean, what's significant, and what to do next.

The Question This Answers

Understand fraud prevalence and signal patterns

What fraction of transactions are fraudulent in my portfolio, and which behavioral or transactional patterns signal fraud? Use this analysis to quantify fraud burden and identify key fraud signals (unusual amounts, specific PCA components, time-of-day patterns) that inform rule-based detection or machine learning features.

Questions?

See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.

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