Resistant AI’s mission is to help businesses remain safe from financial crime attacks on their customers’ digital interactions. We do so by using a range of machine-learning techniques we’ve developed, layering them on top of each other to create an in-depth defence. Our technology analyses every interaction each customer makes to check for document forgery, robotic identities, money laundering, fraud, and other threats.
With regards to document manipulation, our system can identify whether a document used for onboarding or in insurance claims, such as a pay slip, invoice, or bill, is legitimate or has been digitally modified. It can also determine what type of document it is. That may sound trivial, but an English-speaking human, for example, would struggle to distinguish between a Chinese utility bill and a Chinese mobile phone bill.
How did you come up with the idea for Resistant AI?
Our company started out in cybercrime, so we’re very knowledgeable in this area. We noticed a growing convergence between this field and the financial and fraud fields.
Whilst cybercriminals in some cases continue to focus on breaching the security systems of organisations, perhaps with the intention to disrupt or steal data, a much larger community of criminals are now focused on extracting financial gain from businesses by attacking the automation and AI systems — the processes — the companies rely upon to conduct business online. It’s this hacking of digital interactions that is blurring the line between fraud, money laundering, and cybercrime.
Fraudsters have organised into specialist roles that can industrially generate new forged documentation and identities — literally to the tune of thousands per hour — then use those to gain access to financial services either for direct gain or often trading such products for use by others, such as in money laundering activities, selling them to others who will commit the crime.
And no one needs to meet in person — all the steps in such a financial crime lifecycle are handled online. With our background in successfully using machine learning to combat cybercrime we felt uniquely placed to deal with this problem.
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How has the company evolved over the last couple of years?
We sold our former cybersecurity company, Cognitive Security, to Cisco in 2013, and then worked with them for several years. The same team went on to use our knowledge and experience to launch Resistant AI in 2019. In the past three years, we’ve developed a host of solutions – and we never stop evolving. This is because we must adapt and improve each time the online attackers do.
For instance, we realised cybercriminals were using neo banks to automate money laundering processes. In response, we created a system that can detect advanced layering and muling techniques, as well as deliver intelligent alert context and prioritisation. If the criminals hadn’t started doing this, we wouldn’t be offering it as part of our solution.
What can we hope to see from Resistant AI in the future?
For us, the future is all about scaling up. The number of customers needing our solutions is continuously increasing and we foresee an even greater demand in the near future.
Today’s AI-powered real-time identity forensics can detect advanced financial crime, fraud, and manipulation, and are adept at joining the dots to uncover previously unidentified vulnerabilities in the underlying systems they protect so that future exploitation can be deterred.