Industry

Finance & Banking

Churn in SaaS and readmission in healthcare are the same survival model wearing different clothes. Same maths, different vocabulary, different traps.

How we actually do it

The statistics stay identical. An industry variant changes framing and vocabulary, never the maths — so results remain comparable across fields.
Units and traps come from the field. Currency, rates and the specific ways data in your industry misleads are built into the interpretation.
Generic analyses adapt at run time. Most analyses carry no fixed industry; they take their framing from the question you actually ask.

Benford's Law Screening — sample report Open full report →

That is the actual report this analysis produces — not a mock-up. Every analysis on this page delivers the same shape of output on your own data.

Every analysis classified under this industry.

Do these numbers look fabricated or manipulated?

How risky is this series? Rolling volatility, drawdowns, and simple historical VaR.

Analyses the pipeline built from a real question in this field — each one became a module its owner can re-run.

Detects fraudulent credit card transactions using isolation forest (unsupervised) and logistic regression (supervised) scoring methods, enab

Binary classification model to identify fraudulent credit card transactions from 28 PCA-transformed features using SMOTE oversampling and mu

Statistical analysis of fraudulent vs legitimate credit card transactions using exploratory visualizations, distribution comparisons, tempor

Forecast FMCG revenue 3-6 months ahead using seasonal decomposition and ARIMA modeling on historical monthly sales data. Decomposes trend, s