Until recently, financial institutions wanting to use AI for fraud prevention were forced to make a choice:
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Use off-the-shelf AI models that can be deployed quickly but lack precision
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Or use custom-built AI models, which offer more accuracy but take longer to train
An out-of-the-box banking model, for example, will recognize some general patterns to help prevent fraud. But it won't spot patterns that are specific to your customers and business. Custom models offer that precision, but they require development time, tuning, and integration effort from your team.
Hawk has changed that with our new Day One Defense Models. These give you access to typology-specific AI models that are trained on your data but can be deployed in days rather than months.
One customer identified 30% more fraudulent accounts using our merchant fraud model. Scammers were creating fake merchant profiles to trick users into sending money and then quickly cashing out.
And that's just one fraud type. Our full library of models is designed for multiple key fraud typologies, including chargeback fraud, stolen credit card use, mule accounts, account takeover, APP fraud, and new account bust out.
👉 Get more insights into AI models for fraud prevention in this article.
