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For any business, payment processing is a key enabler and thanks to the pandemic, digital payments have moved from a nice-to-have capability to being an essential service and have enabled merchants of all sizes to adopt an omni-channel approach. But with scale comes its own set of challenges, which customers end up bearing.

PayPal is trying to leverage the power of AI in its platform each time that a customer reaches out to them when they face issues in using the platform.

The most challenging part for any digital company is to solve various kinds of customer issues. And PayPal is addressing this problem with its data lake and AI/ML engine to intelligently monitor customer’s journey not only to deliver experience but also to predict any sort of issue which may occur in the future. And with insights, the customer service executive team is prepared to handle every user in a personalized manner after pre-understanding the issue.

“We deliver empathy at scale in situations where customers are unhappy or face any issue with our platform. We leverage AI and ML to solve and even predict intent and proactively reach out to customers before they can perceive an issue. Our systems are prepared not only to serve customers but also to have forecasts of various issues that a customer may face. And accordingly we handle that problem. We also keep a track of customer satisfaction. This intelligence is making us stand apart from the league in the market and delivering a holistic solution to every customer,” Guru Bhat, GM & VP, Omni Channel & Customer Success, PayPal India said.

AI bias is not a challenge

According to Bhat, in the lifecycle of an AI solution, the attention that is paid to the training period can make all the difference in eventual outcomes. Not just in terms of effectiveness and efficiency of the solution from a business value perspective, but also in terms of aspects like bias. In spite of the scale at which we operate, we rely heavily on “Augmented” Intelligence rather than just “Artificial” Intelligence.

His team relies on extensive human supervision, input, modification and augmentation during the initial phases of learning where decisions made by the underlying machine intelligence framework are scrutinized for not just accuracy from a business perspective but also aspects like bias, inclusion, fairness, etc.

“This is not just the right thing to do, but it is also the smart thing to do in the fintech domain where the “explain ability” of the algorithms is a must-have especially when it comes to decision-making in scenarios like credit where we are legally obligated by regulations to have no bias in our credit decisioning platforms. In the end, it boils down to our culture. A culture tenet which we hold dear is “Inclusion”. This is important to us in everything we do every day and in every way. Eliminating bias from our algorithms is just as important to us as eliminating bias in our interview process or in our compensation/promotion process – because in the end it is about making our products inclusive, fair and inherently usable by ALL our customers, not just a few,” Bhat explained.

Data science for democratizing financial services

The company is beginning to increasingly rely on data science, automation and AI for the mission to democratize access to financial services.

PayPal is managing 28 million merchants and more than 333 million active accounts. The company has enabled data science capabilities to better understand the needs of its customers. The data science team uses AI to help the company better understand individual customers’ journeys, behavior and needs so that they can identify good behavior overlooked by broader statistical models and offer personalized products and experiences.

“Data science and analytics have become a business-critical function across industries today. If you have to take the complete benefits of data, companies need to realize how to manage that data, otherwise that is just what we call ‘digital exhaust’. If companies can take that exhaust and funnel it into creating intelligence and insights, then there’s value creation involved. The success of data management will rely on effective utilisation of in-house data, combining it with the infinite publicly available data and being smart about how we use it,” Bhat added.

PayPal’s Data Analytics teams play a key role in early data anomaly detection, seamless integration with data governance, continuous improvisation using machine learning as well as quality of data certification and scoring. Beside data analysis, data visualization is another critical part of leveraging big data at PayPal, which focuses on presenting the large volume of data in easily recognizable formats. The India team works on multiple data visualization reports that can be used to identify buying and selling patterns across the platform.

https://cio.economictimes.indiatimes.com/news/next-gen-technologies/paypal-is-trying-to-pre-empt-customer-issues-will-it-help/79294974