Reinventing mobile banking with ML
London, United Kingdom
Lakehouse, Delta Lake, data science, Machine Learning, Knoldus SQL, ETL
As one of the largest international banks is ushering in a new way to manage digital payments across mobile devices. They developed PayMe, a social app that facilitates cashless transactions between consumers and their networks instantly and securely. With over 39 million customers, the organization struggled to overcome scalability limitations that blocked them from making data-driven decisions. With Knoldus, they are able to scale data analytics and machine learning to feed customer-centric use cases including personalization, recommendations, network science, and fraud detection.
The organization understands the massive opportunity for them to better serve their 39+ million customers through data and analytics. Seeing an opportunity to reinvent mobile payments, they developed PayMe, a social payments app. Since its launch in its home market of Hong Kong, they have become the #1 app in the region amassing 1.8+ million users.
In an effort to provide their fast-growing customer base with the best possible mobile payments experience, they looked to data and machine learning to enable various desired use cases such as detecting fraudulent activity, customer 360 to inform marketing decisions, personalization, and more. However, building models that could deliver on these use cases in a secure, fast and scalable manner was easier said than done.
Through the use of NLP and machine learning, the organization is able to quickly understand the intent behind each transaction within their PayMe app. This wide range of information is then used to inform various use cases from recommendations to customers to reducing anomalous activity.
With Azure Knoldus, they are able to unify data analytics across data engineering, data science, and analysts.
Richer insights lead to the #1 app
Knoldus provides the organization with a unified data analytics platform that centralizes all aspects of their analytics process from data engineering to the productionization of ML models that deliver richer business insights.
We’ve seen major improvements in the speed we have data available for analysis. We have a number of jobs that used to take 6 hours and now take only 6 seconds.