How American Express Leverages ML To Achieve Lowest Card Fraud Rates In The World

Manish Gupta

The American Express Company, also known as Amex, is an multinational banking and financial services corporation headquartered in New York City. The company is a financial giant with 114 million cards in force, 64,000 employees worldwide and $1.24 trillion worldwide billed business. 

For Amex, there are billions of transactions going through its system every month. With such a volume of card transactions, it is not just the dollar amount which is high, the network also generates massive amounts of data, including trillions of transactional data combinations which need to be analysed in almost real time

In such a situation, advanced techniques in machine learning and deep learning are essential, and the company uses them extensively in detecting and preventing frauds. But how does Amex do that on such a large scale?

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To understand better, Analytics India Magazine reached out to Dr Manish Gupta, Vice President, Machine Learning & Data Science Research, and Head of Risk COE Bangalore at American Express.

According to Dr Gupta, Amex has built and deployed best in class ML models, leveraging state-of-the-art technologies like deep learning, machine learning for various business decision-making processes. 

How would you define your leadership role at American Express in terms of the initiatives you are driving?

Dr Gupta: I lead the machine learning and data science team that builds state-of-the-art machine learning solution frameworks and leverages them in risk and analytics decisions on behalf of our customers across the globe. 

How did the journey of Amex in machine learning begin?

Dr Gupta: When it comes to American Express, early on, our leadership recognised the value of big data analytics and data to drive better decision-making and ultimately support risk management. As a 170-year old company, it has instilled a culture that embraces innovation, combining its technology and infrastructure with new computing techniques to make better and faster decisions. 

This mindset is a critical advantage to American Express since it began its work in machine learning nearly 10 years ago. We have recently started to explore deep learning techniques to generate the next set of data innovations by deriving intelligence from the global, integrated network of American Express. We are challenging ourselves to leverage deep learning capabilities to bring human-level intuition from this structured data. Deep learning has helped us to improve credit and fraud decisions and elevate the payment experience for millions of Card Members across the globe.

How does American Express use machine learning to maintain a competitive edge? Can you elaborate with a few examples?

Dr Gupta: American Express has three unique advantages: its data, advanced machine learning and deep learning techniques, and decision science talent. American Express has deployed what we believe to be one of the most advanced machine learning systems in the financial services industry. Our machine learning algorithms across credit and fraud risk are used to monitor in real-time more than $1.2 trillion worth of transactions annually around the world. We use sophisticated tools and methods to evaluate data available only to American Express since we operate as a card issuer, merchant acquirer and a network.

As a result of deploying machine learning within our fraud models back in 2014, we have continued to maintain the lowest fraud rates in the credit card industry (half that of our competitors), according to the February 2020 Nilson Report. Having our card members backs is our top priority and keeping our fraud rates low is key to achieving this goal.

Please tell us about the deep learning approach at Amex to counter fraudulent transactions. Can you discuss some brief details about data science techniques you use to create fraud analytics solutions? 

Dr Gupta: We have been leveraging advanced embedding techniques and deep learning generative and sequential models to better train our models to identify genuine transaction patterns from fraudulent ones, which has led to a significant drop in fraud losses and improvements in customer experience. We have recently invested in high-performance deep learning infrastructure based on the GPUs to implement real-time inferencing frameworks.

How do you test your models to simulate real-time frauds?

Dr Gupta: Our models are real-time, allowing us to be proactive about fraud prevention. Each incoming transaction is evaluated for its propensity of being fraudulent so that we can stop it before any losses are incurred. Our state-of-the-art decision engines help in evaluating models using complex feature calculations in a few milliseconds.

What according to you is the most significant breakthrough in AI/ML in the last five years, and why?

Dr Gupta: I’ll give you my top three. First, we have the Deep Learning Frameworks such as Tensorflow, Pytorch etc., which helped democratise AI rapidly. Now, people can build AI models very easily. Second, there are GPUs that make AI economical: One of the big reasons AI is now such a big deal is because of the cost of crunching, so much data has become affordable. Finally, there are Neural Architectures such as BERT, ResNet etc. has accelerated the innovation in NLP and Computer Vision Domains. 

Vishal Chawla
Vishal Chawla is a senior tech journalist at Analytics India Magazine and writes about AI, data analytics, cybersecurity, cloud computing, and blockchain. Vishal also hosts AIM's video podcast called Simulated Reality- featuring tech leaders, AI experts, and innovative startups of India.

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