# AML alert scoring

> AML alert scoring - Develop a machine learning model that utilizes historical data, including
> customer and transactional information, to identify alerts that resulted in the generation of a
> Suspicious Activity Report (SAR).

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.584276+00:00` (UTC).

## Primary page

- [AML alert scoring](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/alert-scoring.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Time series and specific use cases](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/time-series/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/use_cases_and_horizontal_approaches/anti-money-laundering)

In this accelerator, delve into the exciting world of machine learning applied to Anti-Money Laundering (AML) alert scoring. The primary goal is to develop a powerful predictive model that utilizes historical customer and transactional data, enabling you to identify suspicious activities and generate crucial Suspicious Activity Reports (SARs).

To ensure a smooth and efficient machine learning process, rely on the DataRobot Workbench. This tool allows you to analyze, clean, and curate the data, ensuring its quality and suitability for modeling. By utilizing the DataRobot API, you can seamlessly create and manage experiments, exploring a wide range of machine learning algorithms tailored for the AML alert scoring task. The flexibility and ease-of-use of the API make it a valuable asset for data scientists throughout the process. With just a few lines of code, you can train multiple machine learning models simultaneously, saving valuable time and computational resources. The model insights offered through the API provide invaluable interpretability. Additionally, the DataRobot API allows us to compute predictions on new data before deploying the model into production. This pre-deployment testing phase enables us to evaluate the model's performance in real-world scenarios and make necessary adjustments to address any potential issues.

Uncover the incredible potential of machine learning in AML alert scoring, where data-driven insights make a tangible difference in the fight against money laundering.