MadMax is your agentic data scientist: building, testing, and deploying next-generation predictive models that identify fraudulent transactions in minutes, not months.
The first AutoML platform built from the ground up for banks and their regulators.
MadMax deploys entirely inside your security perimeter as a standalone Docker container and Java scoring package. No data is transmitted to external servers, no cloud processing occurs, and no third-party vendor has access to your transaction data at any point.
MadMax is not a general-purpose machine learning tool adapted for fraud. Every component, from the 25 specialized feature engineering families to the multi-algorithm tournament to the ONNX export pipeline, was designed specifically for the fraud detection workflows that regulated financial institutions require.
Every prediction that MadMax produces includes decision-level SHAP explanations, identifying the exact features that contributed to the outcome, the direction and weight of each contribution, and how they combined to produce the final risk probability. When your regulator asks why a specific transaction was approved or flagged, you have a precise, auditable answer.
Given the same data and the same configuration, MadMax will produce the same result every time. When a model decision is challenged, whether by an internal review committee or an external regulator, you can recreate the exact original output on demand and demonstrate that the process is consistent and verifiable.
The platform automatically generates a complete model report as part of every training run, covering methodology, feature selection rationale, cross-validation results, ensemble composition, and timing for each phase. Rather than requiring your team to produce audit documentation after the fact, the documentation is a built-in output of the workflow itself.
Fraud is a data problem disguised as a business problem. MadMax handles the model building so your data scientists can focus on what actually matters: iterating models against your bank's unique fraud patterns.
Most fraud systems evaluate transactions using the same surface-level features: amount, timing, device, and individual account history. When those features look normal, the transaction passes. The problem is that sophisticated fraud doesn't look abnormal at the surface level. It hides in subtler patterns across payment behavior, temporal sequences, and cross-column interactions that rule-based systems are not designed to detect.
Regulators in every major jurisdiction, including APRA CPS 230 in Australia, SR 11-7 in the United States, FCA frameworks in the United Kingdom, and DORA in Europe, now require institutions to explain why a specific transaction was approved or flagged. Most deployed fraud models cannot provide this level of transparency, and when a regulator asks for the reasoning behind a decision, the model has no answer to give.
Even with modern tooling, most institutions still measure model deployment timelines in months. By the time a new fraud model completes development, validation, and deployment, the fraud patterns it was trained on have already evolved. The result is a model that is partially outdated before it reaches production, and a team that is always reacting rather than staying ahead.
Building a competitive fraud model requires deep expertise across feature engineering, algorithm selection, hyperparameter optimization, and ensemble construction. Each of these disciplines typically requires dedicated specialists, and the full process of building a single production-quality model can take a team of experienced data scientists several weeks of focused work.
Most fraud models are validated once against a holdout set that closely resembles the training data. In production, transaction patterns drift, customer behavior changes, and the statistical relationships the model learned begin to weaken. Performance degrades quietly until a spike in losses forces a retraining cycle that takes months to complete.
MadMax automatically designs, iterates, and deploys models in minutes that outperform long-term specialist buildouts.
Upload transaction data as CSV or Parquet. The platform auto-detects column types, profiles distributions, identifies nulls, and previews the dataset. Select your target variable and validation strategy. Your data never leaves your environment.
MadMax automatically generates hundreds of predictive features, including payment behavior patterns, entity relationships, and temporal signals. Work that typically requires a specialist team weeks of iteration, delivered in minutes with leakage-safe methodology built in.
Every dataset is different. Rather than guess which approach works best, the platform runs a structured tournament across five algorithm families and dozens of configurations. A fast scouting phase eliminates weak candidates, then winners are retrained at full scale.
No single model sees the full picture. The platform learns the optimal weighting across top performers, combining their strengths into an ensemble that consistently outperforms any individual algorithm. Training accelerates with GPU when available, but the ensemble delivers peak accuracy on CPU-only infrastructure.
The complete pipeline ships as a production-ready package: ONNX model, standalone Java scorer, SHAP explanations baked into every prediction, and a full model report for your regulator. Millisecond CPU inference. Straight to production, no rework.
Rules-based systems only catch what you already know about. Vendor models are trained on generic data, not yours. MadMax builds an ensemble tuned to the fraud patterns hiding in your specific transaction history, patterns that no off-the-shelf tool will ever see. Score every transaction in real time, explain every decision to your regulator, and retrain in minutes as fraud evolves. The model gets sharper with every iteration because it learns from your data, not someone else's.
Our world-class team integrates directly into your existing infrastructure, tailoring each bespoke deployment to individual partner's unique needs, compliance requirements, and specs.
Connect your transaction data via CSV, Parquet, or database. Initial model training begins immediately. Purpose-built feature engineering generates 200+ fraud-specific features automatically. No data leaves your environment.
Live detection benchmarks on your own data, against your existing system. Side-by-side comparison: what your current tools catch vs. what MadMax catches. Measurable, auditable, on your infrastructure.
Pre-production validation with your model risk and compliance teams. Decision-level SHAP explanations, deterministic reproducibility, and auto-generated model documentation reviewed against APRA CPS 230, SR 11-7, or your local framework.
Standalone Java scoring package deployed alongside your existing systems. Millisecond inference on standard CPU. No rip-and-replace. No Python runtime in production. GPU available for training acceleration, not required for deployment.