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Hawk AI, a platform for banks to prevent fraud and money laundering, raises $17 million

German startup Hawk AI, which creates AML and related fraud protection smarts for banks, has secured $17 million in a Series B fundraising round.

Hawk AI has announced that it has raised a total of $17 million, bringing its total amount raised to date to $21 million. The business had previously raised $10 million and expects to use the new capital to accelerate product development and international growth. Sands Capital acted as the lead investor for the Series B funding round, which also included Picus Capital, DN Capital, the Coalition, and BlackFin Capital Partners.

It is estimated that $2 trillion, or up to 5% of global GDP, is laundered annually, with just 1% of this amount being recovered. Specifically, Hawk AI is establishing its presence here.

Hawk AI, founded in Munich in 2018, is a cloud-native, modular AML surveillance system that promises the “highest level of explainability” in its AI-powered decision-making engine. This engine is essential for audits and regulatory investigations, and it helps banks and payment companies better manage compliance risks.

Financial institutions and regulators, according to Hawk AI founders and CEO Tobias Schweiger, need to be able to comprehend and trust AI-driven judgements. “The key to developing confidence and acceptance of such an AI is its full explainability.”

Using Hawk AI’s payment screening, customer screening, transaction monitoring, transaction fraud, and customer risk rating tools, businesses may create their own risk-rating model by mixing static data (such as product or geographic data) with real-time data (e.g. transaction data such as suspicious activity reports).

The Brazilian financial institution Banco do Brasil Americas is one of its clients, along with the American payment processing business North American Bancard and the European spend management platform Moss.

Invisible machinery

Notable newer entrants in this market include financial fraud startup Feedzai and venture capital-backed Feature Space, in addition to the established players like Verafin, BAE Systems, and Oracle. While many of the established competitors still rely on cumbersome on-premise installations, Hawk AI highlights its cloud-native credentials and SaaS business model as key differentiators.

However, the company is quick to point out that it is primarily concerned with bringing transparency to the “black box” that artificial intelligence and machine learning algorithms often operate in. Knowing the reasoning behind an algorithm’s decision is crucial, and businesses must be able to explain why a particular customer was labelled as a potential fraudster.

Note that certain anomaly detection tools include context on what could have triggered a flag. Hawk AI, however, claims that its patented system not only provides a score for each risk factor in plain English, but also informs users of the “anticipated range” of behaviour. According to the organisation, seeing the whole picture is crucial when deciding whether or not a certain instance involves suspicious behaviour.

Schweiger explains that “explainability” for Hawk AI is split into two categories. Where did these algorithms that contribute to AI come from, and what justifications are there for AI-driven individual decisions? Officers responsible for ensuring compliance should have full visibility into both.