Understanding credit card fraud detection using artificial intelligence and machine learning technologies in 2020 is imperative. AI and ML technology in today's world of online credit card fraud prevention must be taken seriously.

Credit Card Fraud Detection With AI and Machine Learning

Understanding credit card fraud detection using artificial intelligence and machine learning technologies in 2020 is imperative. AI and ML technology in today's world of online credit card fraud prevention must be taken seriously. Banks and credit card companies using machine learning and artificial intelligence to reduce credit card fraud in 2020 are reporting better than average fraud prevention results.

Algorithms are a science and method using already collected data from past user experiences. The data applies to personal profiles of cardholders. If a person’s profile data changes from the normal activity already programmed flags instantly are up and prevents a user’s card from working. This process renders the card useless until the actual holder calls and verifies the transaction. Machine learning basically educates machines on the important patterns and behaviors of users.

Machine Learning and Why Your Institution Must Adapt

Adapting to banking and credit card security systems that use AI and ML technologies is of utmost importance if a financial institution is to survive in 2020. Customer satisfaction and support is just one reason a cardholder chooses a credit card company, but security is the number one reason. To be competitive credit card companies taking the proper security steps for their financial institution will survive the next-gen of digital transactions. Many banks and credit card companies have already been decimated for not recognizing and implementing big data solutions and machine learning technology.

Why Banks Worldwide are Scrambling to Introduce Machine Learning? 

The majority of the world population now uses online transactional methods of payment. As reported by ITRC, 2018 data breaches increased by 126% and a mere 23% the prior year. These two figures are facts that institutions cannot ignore any longer. This free PDF download can be obtained on the ITRC’s website (see the above link). The sheer number of sensitive records obtained by cybercriminals’ should be enough to convince any security staff that upgrading to machine learning technology is imperative.

Why Banks Worldwide are Scrambling to Introduce Machine Learning?

How Effective is AI and ML in Protecting the Consumer?

The Identity Theft Resource Center Reported an Increase of 17 percent in data breaches in 2019. On the other hand, the amount of secured data exposed decreased by a whopping 65 percent from 2018. Financial security experts are giving credit to AI and machine learning technologies.

Machine Learning Has Gained Steam Most Notably In Identity Protection. 

Most of the world’s population now uses online transactional methods of payment. Artificial intelligence to prevent fraud is not a new concept, however, knowledge already programmed into machine learning in the banking sector has grown over the last few years and credit card fraud is the number one choice for cybercriminals.

Graph Showing Credit Card Industries Are the Most Vulnerable

Cybercriminals in 2019 were successful in acquiring more than 133,000 user’s personal data in the United States alone. These numbers are striking and scary to say the least. The US has some of the tightest security in the world and is still unable to completely prevent fraud. Using AI and ML is only now becoming an integral part of financial institutions and for good reason.

Graph Showing Credit Card Industries Are the Most Vulnerable

How Do Credit Card Companies Detect Fraud Via ML?

Have you ever gone to pay for a product only to find that your card has been blocked? Credit card companies detect fraud by flagging specific transactions which are specifically called Credit Card Fraud Detection. If a transaction does not match the cardholder’s profile the card is automatically blocked by artificial intelligence collected through machine learning. This annoying incident for consumers is a blessing in disguise for credit card companies. This can be annoying for the consumer as well the alternative would be having your account drained by someone who has bought your hacked information.

Why Do Credit Card Companies Need Fraud Detection?

At a loss of seven cents for every one-hundred-dollar directly related to fraud it becomes easy to understand why using machine learning is so important to card companies. Global transactions just in the United States due to credit card fraud amount to 190 billion dollars annually. The FTC reported that credit card fraud rose by 23 percent in 2019. Through artificial intelligence would be fraudsters have little to no chance of hacking through the database. Gemini Advisory reported that over 30 million debit and credit cards were compromised in the United States alone and over 1 million cardholders globally. This single breach in 2019 was the mastermind of one single cybercriminal team.

Anomaly Detection with Machine Learning

Anomaly Detection identifies events that rarely happen. When an event does not generally occur for a credit card user it raises a red flag and essentially blocks a consumer’s card from being used. The consumer must call the card company and go through the verification process to remove the block. Anomaly detection systems effectively stop fraudsters in their tracks bypassing each transaction through the actual card holder’s user profile. This is a very simple solution in a very complex modern digital society. This simple process can only be achieved by implementing artificial intelligence and machine learning using big data into your financial institutions’ machine.

“Organizations must act now and use big data in technology, marketing and cybersecurity strategies to evolve the relationships with their customers, transform data into an asset, and stay ahead of their competitors.”

By Paul Bennett; Country Manager ANZ; MapR Technologies ComputerWorld

Without Big Data Financial Institutions Will Fail.

Money managers across the globe are frantically seeking out new sources of Alternative Data. Companies that are extremely competitive know the importance of big data. Without anomaly detection and alternative data banking financial institutions will lose their competitive edge and enter their place in the history books. Big Data is so enormous and complex it is extremely hard to use without data processing professionals. Whether your institution is in SE Asia or Europe machine learning to prevent credit card fraud will be the new weapon against fraud by credit card information theft.

“Integrate AI solutions with your existing infrastructure to discover new revenue channels, business models, elevate insights and increase performance across all departments”

The SPD Group.


Without a doubt, artificial intelligence will be essential in the success and survival of financial institutions. The SPD Group Research & Development and its technologies including Machine Learning and Big Data should be on your list of companies to watch for in 2020. The amount of financial transactions has grown exponentially and is expected to continue at an even faster pace once developing nations have adapted their banking systems to meet the new digital demand and trends that are happening globally.