• Use of Generative AI to create ever more compelling methods to deceive victims

• Use of AI / Machine Learning to relentlessly test for vulnerabilities in systems

• Use of AI to create malicious content such as phishing emails , Deep Fake videos and even malware

It ’ s important that the industry continues to discuss and address these issues as AI becomes more embedded in our daily lives .

Are there any particular verticals that are most under threat from cybercriminals ?

Cybercriminals are generally motivated by financial gain or to acquire personal data .

From a financial perspective , these criminals are after sensitive data such as personally identifiable information , national identifiers , medical data , payment and financial data etc . This information can easily be monetised in various open and Dark Web forums . Hence , industries such as financial services , public sector , healthcare etc . are generally most under threat from such financially motivated criminals .

What are some of the use cases of AI in payment security ?

Over the past 10 years , at Mastercard , we have been using AI to prevent fraud , detect threats and identify vulnerabilities . Coupled with our unique network-wide view we can prevent frauds of multiple varieties .

We want to stay two steps ahead of cybercriminals and bad actors . To do that we will continue to monitor trends and methods , using our world leading technology to innovate and create new solutions that continue to protect our digital ecosystem . Thanks to our AI powered solutions we help secure the digital ecosystem for all . Some examples of this are :

• Decision Intelligence :

• A real-time decisioning solution – already helps banks score and safely approve 143 billion transactions a year .

• Thanks to newGenerative AI technology we can now scan an unprecedented one trillion data points to predict whether a transaction is likely to be genuine or not .

• DI Pro – In less than 50 milliseconds , this technology improves the overall DI score , sharpening the data provided to banks . Initial modelling shows AI enhancements boost fraud detection rates on average by 20 % and as high as 300 % in some instances .

• Gen-AI Card Fraud Predictor

• This new technology works by scanning transaction data across billions of cards and millions of merchants at faster rates than previously imaginable . In doing so it alerts Mastercard to new , complex fraud patterns . Using Generative AI-based predictive technology built by Mastercard it is able to protect future transactions against emerging threats , by :

• Doubling the detection rate of compromised cards ,

• Reducing false positives during the detection of fraudulent transactions against potentially compromised cards by up to 200 %,

• Increasing the speed of identifying merchants at-risk from – or compromised by – fraudsters by 300 %.

• RiskRecon Third Party Risk Solution

• RiskRecon by Mastercard continuously monitors millions of companies globally , assessing wide-range vulnerabilities including own enterprise and subsidiary risk , third-party risk , fourth-party risk , and extended nth-party supply chain risk .

• We constantly scan 19mn entities regularly to assess their defences in the areas of ( software patching , application security , web encryption , system reputation , breach events , system hosting , email security , DNS security and network filtering ).

• It provides data analytics and a risk score that enables organisations to identify vulnerabilities across their third-party networks and supply chains .

What are the latest trends and tactics used by cybercriminals ?

Fraudsters are using technology in more innovative and sophisticated ways to trick consumers , and the problem is growing with global e-commerce fraud losses estimated at US $ 48 billion in 2023 . The techniques and methods used by bad actors are constantly evolving . Based on the data seen by our cybersecurity systems , we see the following most commonly used methods to target organisations :

• Phishing and spear-phishing

• Exploitation of known vulnerabilities

• Malware deployment ( trojans , ransomware , etc .)

• SQL injection

• Cross-site scripting ( XSS )

• Distributed Denial of Service ( DDoS )

• Supply chain attacks .

We want to stay two steps ahead of cybercriminals and bad actors .

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