Hawk expands AML tech with new instant fraud defence models

By Vriti Gothi

Hawk, an artificial intelligence-driven anti-money laundering (AML) and fraud prevention technology, has expanded to a fraud detection platform with the introduction of its new Fraud Day One Defence Models.

The development aims to help financial institutions implement tailored fraud defences rapidly, addressing key vulnerabilities from the moment the system goes live.

The newly released models form a curated library of AI-powered fraud typologies, designed to help banks and other financial firms tackle some of the most common and costly forms of financial crime. These include threats such as authorised push payment fraud, merchant fraud, money mule activity, and account takeover attempts all areas that continue to cause substantial losses across the sector.

According to Hawk, the models can be adapted to each institution’s specific risk landscape in less than three days once initial data and system configuration are complete. By combining an automated feature selection process with a streamlined model pipeline, the company claims it can deliver highly accurate, business-specific protection far faster than traditional, fully custom-built AI models, which often take months to develop.

This approach is intended to help fraud teams respond more effectively to new and emerging threats without compromising customer experience. Hawk’s broader fraud solution includes real-time anomaly detection with an average response time of 150 milliseconds, alongside self-service rule management that allows institutions to adjust controls as required.

Wolfgang Berner, Chief Product Officer and Co-Founder at Hawk, said, “Fraud doesn’t wait, and now, neither do our customers. With our new Fraud Day One Defence Models, financial institutions can now deploy tailored protection in just days. This new offering reflects our commitment at Hawk to helping customers act fast and outpace threats from day one.”

Hawk’s roadmap includes further enhancements to its AI typology models, alongside plans to extend automation, behavioural analytics, and cross-channel capabilities. The company says this will help institutions future-proof their fraud prevention strategies as criminals continue to evolve their methods and exploit digital channels.

The move underscores the broader industry shift towards more agile, data-driven approaches to financial crime prevention, as regulators and customers alike expect higher standards of security and responsiveness from financial services providers.