At American Express, a lot of decisions are now driven by AI-powered algorithms. This includes detecting fraudulent transactions, determining credit limits for new customers, sending out the right marketing messages, improving customer interactions and many more.
In fact, Amex now encourages most of its employees to acquire some data analytics capabilities. Jayatu Sen Chaudhury, Head of Enterprise Digital & Analytics India and Vice President of Global Commercial & Merchant Data Sciences, American Express, explains why.
Q: What are the use cases of AI/ML that you’re exploring? What are the key challenges you’re facing while incorporating AI/ML and how are you addressing it?
A: AI and ML are being leveraged across the company by different business units to deliver on the exceptional brand experience we are known for, at scale, in the digital age.
Some examples include Credit & Fraud Risk, where we use advanced AI-powered algorithms to detect fraudulent payment transactions at the point of sale and to determine the credit limits of new customers. Also, our Enterprise Digital & Analytics business unit uses AI to deliver the right marketing message at the right time to current and potential Card Members across a number of digital channels. It’s what helps us ensure relevancy in demand generation and customer acquisition. For our Servicing team, we use AI to help us analyze, understand and improve a customer’s interactions with our Customer Care Professionals. Honestly, data analytics is in the DNA of our company, which made our adoption of AI much easier. Senior leadership is also extremely supportive of our use of AI and encourages its adoption throughout the enterprise.
Q: How critical is it to put data analytics capabilities in the hands of those who can add the most value?
A: It is of great importance. At American Express we have a culture where we are constantly empowering and encouraging employees to learn and adopt the latest data analytics capabilities. We host instructor-led trainings and self-learning curricula, send employees to relevant conferences and even have a podcast where specific data analytics use cases are dissected for listeners.
We have always been a data-driven organization; but now we are seeing employees who don’t have a traditional analytics background become curious about and train in new data analytics methods.
Q: How do you really see the future of Analytics in financial services sector? Do you think it will emerge as true business discipline? What are your views on that?
There is already a great amount of analytics work happening in the financial industry. While earlier work relied more on drawing insights from existing patterns, much of the focus is now shifting to using analytics to make predictions. Additionally, we will continue to see data scientists working hand in hand with those on the “business side”, for lack of a better term, to tackle problems together and drive the desired business outcome. This marriage of business and science is key to producing the desired end result.
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