Germany’s Hawk AI secures $10m to develop AI-powered anti-money laundering as-a-service

Plans to expand into Singapore and the UAE

Ben Wodecki

June 29, 2021

1 Min Read

Plans to expand into Singapore and the UAE

Hawk AI, the Munich-based firm that develops AI-powered products for money laundering detection, has raised $10 million in a Series A funding round led by BlackFin Capital Partners.

The company said it would use the funds to strengthen its position in Europe and the US, as well as expand into Singapore and the United Arab Emirates. Hawk AI also said it expects to double its headcount by the end of 2022.

"We have made it our mission to bring long-overdue technology advancements such as Cloud, AI, and real-time analytics to these sensitive and costly processes," Tobias Schweiger, co-founder CEO of Hawk AI, said.

Like a hawk

Hawk AI was established in 2018 to develop a software platform that identifies patterns of suspicious activity in financial transactions using artificial intelligence, providing anti-money-laundering experts with alerts.

The venture was co-founded by Schweiger and Wolfgang Berner who both previously held senior leadership roles at PAY.ON, a provider of e-commerce payment gateway services acquired by ACI Worldwide for $200 million in 2015.

Hawk AI counts North American Bancard, Modularbank, and Ratepay among its customers. The platform’s cloud-native, modular and open architecture can be quickly integrated with existing banking and payment systems, the company said.

Upon closing its Series A funding round, Hawk AI boldly announced that it aims to become “the leading compliance platform for transaction surveillance.”

Last November, the company entered into a partnership with open banking provider BANKSapi. The team-up saw BANKSapi delpoy anti-money laundering software to monitor transactions for suspicious cases.

Meanwhile, Hawk AI integrated BANKSapi's AI/Connect module, which uses computer linguistics based on neural networks to enable the categorization of account transactions to improve AML/CFT recognition rates.

About the Author(s)

Ben Wodecki

Assistant Editor

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