While $12.7 billion of this figure goes to another merchant when a customer is turned away, it must to be noticed that false declines "are also making for a less efficient digital economy". This is because "$7.6 billion of potential spending never came about as the shopper lost interest. In the same report, a senior industry executive pointed out that re-visiting risk appetite is vital. Also, a "lot of sins can be hidden in the name of #fraud prevention, because fraud teams aren't always incentivised to have a very rigorous statistical measure of false positives and false negatives". "Many companies just don't want to get on the MasterCard and Visa chargeback programmes, and that's the guiding principle.

Visa has announced new analysis showing Visa Advanced Authorization (VAA) using artificial intelligence (AI) helped financial institutions around the world prevent an estimated $25 billion in annual fraud. VAA is a comprehensive risk management tool that monitors and evaluates transaction authorizations on the Visa global payment network, VisaNet, in real time to help financial institutions promptly identify and respond to emerging fraud patterns and trends. Visa processed more than 127 billion transactions between merchants and financial institutions on VisaNet last year and employed AI to analyze 100 percent of the transactions--each in about one millisecond--so financial institutions can approve legitimate purchases while quickly identifying and preventing fraudulent transactions. "One of the toughest challenges in payments is separating good transactions made by account holders from bad ones attempted by fraudsters without adding friction to the process," said Melissa McSherry, senior vice president and global head of Data, Risk and Identity Products and Solutions, Visa. "Visa was the first payment network to apply neural network-based AI in 1993 to analyze the riskiness of transactions in real time, and the impact on fraud was immediate. By striking the right balance between human expertise and technology innovation, we continue to evolve our capabilities as new AI breakthroughs expand the realm of what's possible."

After enjoying years of steady growth, your business suddenly sees your customer satisfaction scores sinking. When you investigate, you find that your customer support team is simply not keeping up with the volume of requests that they're receiving. Customers have to wait two or more days for a first response, and they're voicing their discontent in growing numbers on social media. You don't have enough money in the budget to hire and train more support staff, so the only realistic solution is to use AI and automation. Which use cases should you try to automate, and which tools should you use?

In 2016, thieves stole $16 billion, up almost $1 billion from 2015, using either stolen credit or debit cards, or by creating fraudulent accounts. At first blush, it would appear that the good guys are rapidly losing ground, but those numbers do not tell the whole story as losses per victim, during that same time period, dropped an impressive 11%, falling from $1,165 to $1,038 for each targeted individual. The average out-of-pocket cost to the consumer in these cases decreased from $56 to $48. The increase in fraud is partially due to the rise in the number of scammers and is also because of yearly inflation. Artificial intelligence (AI) and machine learning (ML) played a significant role in the decline of loss-per-victim, and seem ready to drive down losses even further.

As artificial intelligence transforms an increasing number of domains, financial institutions and companies are moving fast to keep pace. This can affect the way you bank, invest, receive loans and prevent financial crimes. At the heart of the AI revolution are machine learning algorithms, software that self-improves as it is fed more and more data, a trend that the financial industry can benefit from immensely. Here are some of the key trends that making inroads in the space. As e-commerce has risen in popularity, so has online fraud.

Machine-learning is all the rage in fraud detection, with industry analysts, academics, businesses and technology media examining the advantages of algorithms and big data in the fight against e-commerce fraud. Especially for fraud analysts working in companies with small budgets, machine-learning tools are seen as a cost-effective way to tighten fraud controls while maintaining fast decision times, as Forrester noted in its 2015 cross-channel fraud report. There's no question that machine-learning tools can be an effective component of fraud reduction program, but relying on them to save staffing costs may not be cost-effective in the long run. That's because while machine learning is an invaluable tool in the fight against fraud, it relies on human input and insight to create a comprehensive solution that yields the best results. Algorithms are useful for identifying potential fraud quickly, but due to variability in consumer behavior – such as making online purchases while traveling abroad -- some transactions will be falsely flagged for decline.

When it comes to security in their financial transactions, American consumers want it all. In Total System Services, Inc.'s 2016 U.S. Consumer Payments Study, a whopping 74% of all respondents said they would choose the credit card with the best security features over the credit card with the best rewards. Payment networks like Mastercard Inc. have done their best to respond. In the past couple of years, Mastercard has experimented with a number of cutting-edge security initiatives. These innovations have included everything from an app that allows cardholders to take a selfie for payment authentication to a wearable band that uses the account holder's unique heartbeat as authentication. Late last month, Mastercard introduced its latest pioneering security platform, Decision Intelligence.

Machine-learning is all the rage in fraud detection, with industry analysts, academics, businesses and technology media examining the advantages of algorithms and big data in the fight against e-commerce fraud. Especially for fraud analysts working in companies with small budgets, machine-learning tools are seen as a cost-effective way to tighten fraud controls while maintaining fast decision times, as Forrester noted in its 2015 cross-channel fraud report. There's no question that machine-learning tools can be an effective component of fraud reduction program, but relying on them to save staffing costs may not be cost-effective in the long run. That's because while machine learning is an invaluable tool in the fight against fraud, it relies on human input and insight to create a comprehensive solution that yields the best results. Algorithms are useful for identifying potential fraud quickly, but due to variability in consumer behavior – such as making online purchases while traveling abroad -- some transactions will be falsely flagged for decline.

Financial institutions (FI's) face many challenges, but one of the biggest of all is fighting fraud. With so much money and valuable data at stake, the financial system will always be a target for criminals and FI's must shoulder a lot of the responsibility for security. One of the most effective methods of keeping fraudsters at bay is staying up to date with the latest technologies and there are few areas of technological innovation more exciting than artificial intelligence (AI). It's true that we are still a long way off developing true AI, but the developments that have occurred in this field are already offering great potential for FI's. How do you ensure that your security safeguards and authentication processes are rigorous enough to combat fraud, while delivering the speed and reliability modern-day consumers expect?

When it comes to security in their financial transactions, American consumers want it all. In Total System Services, Inc.'s 2016 U.S. Consumer Payments Study, a whopping 74% of all respondents said they would choose the credit card with the best security features over the credit card with the best rewards. Payment networks like Mastercard Inc. (NYSE:MA) have done their best to respond. In the past couple of years, Mastercard has experimented with a number of cutting-edge security initiatives. These innovations have included everything from an app that allows cardholders to take a selfie for payment authentication to a wearable band that uses the account holder's unique heartbeat as authentication.