Bot-based fraud has become extremely advanced. Today, the complexity and scale of fraud rings have grown to all-time highs. More similar to contemporary corporations, fraud rings even have a financial crime value chain. We need to do a better job at preventing them from having too much power. Trace Fooshee, Senior Analyst at Aite Group, created a financial crime value chain framework. It explains the enterprise-level scale that fraud strategists use today.
Fraudsters Are Getting Smarter
Fraud commit bot-based fraud by relying on an automatic network of bots that accomplish tasks needed in every level of the chain. This can begin with gathering raw materials like card information.
The malicious bot environment is quickly changing as those with bad intentions use bot-based fraud to capitalize on the sudden acceleration in e-commerce due to CVOID-19. Therefor, we need to step up our game as well. You can see the ratio of malicious login attempts to legitimate ones on a graph below. Keep in mind, the chart made below reflect pre-COVID data.
There are a couple of things that we can do to provide better bot detection. We need to fight fire with fire. Therefor, this involves the use of a more sophisticated bot. AI. Not only is our AI bot a ‘superbot’ of sorts, but it’s also got the advantage of being under our control. We need to use machine learning to analyze all available data that’s in the Identity Trust Global Network.
Using Bots to Thwart Bots
We can use machine learning and AI to detect potentially malicious bots. Therefor, we can respond to the threat immediately and an approach we can take is to use artificial intelligence to learn patterns of bot-based fraud over time. The Identity Trust Global Network can calculate Identity Trust Levels in fractions of a second. This reduces friction and blocks fraud while delivering significantly improved user experience.
The Identity Trust Global Network has more than 10 years of trust and fraud signals. This data is built from companies and organizations across multiple industries and geographies, so you know its valid. This, combined with their expertise in AI and machine learning can turn trust into a sales.
First, an effective strategy would be to combine the strengths of both supervised and unsupervised machine learning. This can provide a (TSR) Transaction Safety Rating. A TSR would reduce the uncertainty of any typical transaction. It can do so in milliseconds by taking into account the insights provided by supervised and unsupervised algorithms that provide experiential-based data. This would give us fewer false-positives, therefor increasing customer loyalty. Doing something like this would make a business easier to buy from. The image below shows how Transaction Safety Ratings are made:
Then, we can uses the data provided by machine learning to find the best response to bot-based fraud with more accurate trust levels. Specific organizations or businesses can use customized levels. The adaptive trust level of a transaction can be used to tailor specific selling strategies in addition to thwarting bot fraud attempts.