Challenges Banks Face
With growing reserves of customer data, the aggregation of data and application of Artificial Intelligence in banking to generate actionable customer insights is more important than ever.
Many financial institutions struggle to unify their data across back end systems, and even those that achieve a unified and singular data repository often lack the technology and/or expertise to use it to effectively drive a personalized and unique experience for customers.
This is one of the largest obstacles facing digital transformation initiatives, and the financial institutions that find ways to make the most of their data will win the battle for primary ownership of the customer relationship.
Artificial Intelligence Infancy in Banking
Artificial intelligence (AI), machine learning, and predictive analytics are still quite new to the banking world, but the same models and methodologies have been successfully applied in consumer markets. Financial institutions that adopt these models and mirror the hyper-personalized experiences of consumer markets will be seen as innovators. As they do so, they also unlock the full potential of their data and can use it to deliver superior customer experiences.
Admittedly, bridging the gap between traditional and forward-thinking institutions is not easy. It requires the right technology, but more importantly the right leadership to fundamentally change the perception of technology and data within the organization. Promoting and actualizing data liberation across the enterprise enables the intelligent application of AI and machine learning models that can provide an elevated baseline customer experience with minimal additional resources.
Personalization Drives Customer Experience
Personalization can boost cross-sell and up-sell rates, but more importantly, it can unilaterally transform the way your financial institution interacts with customers daily and how they perceive you. Implementing the latest technologies and strategies to organize multiple data feeds and deliver insights that matter can impact your ability to compete for market share.
“To be sure, personalization in banking is not primarily about selling. It’s about providing service, information, and advice, often on a daily basis or even several times a day. Such interactions, as opposed to infrequent sales communications, form the crux of the customer’s banking experience,” writes Boston Consulting Group.
Automatically analyzing data and surfacing actionable insights to your relationship managers enable them to engage more intelligently — more often. Insights and machine learning can also directly enable commercial clients through their online banking portal, providing an omni-channel data-driven experience.
For more details and an example of five AI and machine learning use cases in banking, download the full tip sheet: 5 Ways Use Data to Power Customer Engagement.
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