Leveraging AI and Machine Learning Against Mobile Fraud
Mon, April 19, 2021

Leveraging AI and Machine Learning Against Mobile Fraud

Almost everyone today owns a smartphone. Recent statistics show that more than five billion people across the globe, two-thirds of the world’s population, have a mobile phone connection / Photo by: pikselstock via Shutterstock

 

Almost everyone today owns a smartphone. Recent statistics show that more than five billion people across the globe, two-thirds of the world’s population, have a mobile phone connection. Thus, it’s not surprising that mobile users are using their devices for almost everything, from checking bank balances to online shopping.

With the increasing benefits and opportunities that smartphones can provide to both consumers and businesses, cybercrime has also become more common. Reports estimate that cybercrime, of which mobile fraud is a component, now costs over $600 billion – accounting for 0.8% of the global GDP. Despite efforts to address the problem, cybercriminals are just getting smarter, leveraging advances in technology for their own benefit. 

Forbes, a global media company focusing on business, investing, technology, entrepreneurship, leadership, and lifestyle, reported that there has been a 680% increase in global fraud transactions from mobile apps from October 2015 to December 2018. The Radicati Group, a research company that provides quantitative and qualitative research on email, security, instant messaging, social networking, and more, stated that one of the fastest-growing cybercriminal markets is the hacking of mobile apps and devices. 

The growth in mobile payments is partly behind the soaring incidence of bank and credit card fraud in recent years. It is projected that losses from card fraud across the world could reach $44 billion by 2025. To address these issues, banks and companies are incorporating technologies such as artificial intelligence and machine learning into their processes. For instance, several banks across the world are using machine learning-based fraud detection solution to detect fraud within more than one type of transaction or application, or both of these at the same time.

How AI and Machine Learning Can Help

The mobile market is huge and highly fluid. Reports show that in the US alone, 80 million mobile users have switched providers, 90.3 million have purchased or upgraded their mobile phones, 12.9 million devices have been lost or stolen, and 25.8 million have changed their mobile phone number. These frequent changes make it extremely hard to establish and maintain a digital device identity, which can open the doors to mobile fraud. 

AI can address these problems. It has the ability to interpret trend-based insights from supervised machine learning which can significantly reduce cases of payment fraud. AI knows the difference if a particular transaction or series of financial activities is fraudulent or not. According to Finextra, the leading independent newswire and information source for the worldwide financial technology community, AI algorithms can analyze millions of data points in seconds and detect unique fraudulent behaviors.

Using a combination of supervised and unsupervised machine learning, devices can determine legitimate or illegitimate patterns. It can also anticipate and understand the behaviors of each account holder, including transaction behavior patterns, which can help in identifying any anomalies in mobile transactions. If any spending activity doesn’t fit with the user’s usual activity, algorithms can assume that this is illicit behavior.

The mobile market is huge and highly fluid. Reports show that in the US alone, 80 million mobile users have switched providers, 90.3 million have purchased or upgraded their mobile phones / Photo by: THE YOOTH via Shutterstock

 

AI and Machine Learning Can Reduce Mobile Fraud

AI is ideal for searching anomalies in large-scale data sets in seconds. It would identify fraud--not just one or two at a time, but hundreds or maybe thousands more. The more data an AI system has to train on, the more accurate its predictive value. In fact, several companies are already using AI to leverage their fight against mobile fraud, including Kount, a software company that detects and prevents fraud for online businesses. Kount has a large universal data network containing billions of transactions over 12 years, more than 180 countries and territories, 6,500 customers, and multiple payment networks.

Kount’s large universal data network also includes different transaction verticals, complexities, and geographies. In this way, machine learning models can be properly trained to predict risk accurately. This is just one of the ways AI and machine learning can solve mobile fraud. 

AI is ideal for searching anomalies in large-scale data sets in seconds. It would identify fraud--not just one or two at a time, but hundreds or maybe thousands more / Photo by: Brian A Jackson via Shutterstock

 

Due to the rising occurrence of fraud and other cybercrimes, many businesses and merchants shy away from using mobile payment platforms. According to Payments Journal, the fastest growing payments and banking news and information portal on the web, payment providers and customers lose billions of dollars to online fraud every year. A report from LexisNexis, a corporation providing computer-assisted legal research, showed that retailers lost around $32 billion to fraud.

“For AI and machine learning, this is one of the many areas where AI-based technologies show their potential. AI-powered fraud detection models can be used by mobile payment providers to sift through tons of transactional data, flagging suspicious transactions that match a predetermined fraud model,” the report said.

Through AI and machine learning, reduced false positives are possible, saving customers from having to re-authenticate who they are and their payment method. They are also a great help to fraud analysts. These technologies reduce the time fraud analysts take for manual reviews of the items ordered online. They can automatically approve and reject more orders than usual as well as focus on gray area orders. This frees them to do more strategic, rewarding work. At the same time, AI and machine learning can provide a real-time transaction risk score to businesses so they can keep on track for potential mobile fraud.