Technology infrastructure is where digital, customer data platforms, cloud, adaptive interfaces and computational design all merge. Having the right infrastructure for data acquisition is key. In fact, it’s important to choose the right technology infrastructure for data acquisition, storage and utilization. The number one requirement for updating banking tech infrastructure is knowing how to select and acquire the right data—whether internal or external—and understanding how it should be saved (the format or type of database). Database technologies such as relational databases or graphical databases are relevant. Second, all that data must go into a repository where the data need to be transformed. Some of the data may be taken out for model training. In other cases, subsets of the data can be used as experimental samples by adding new features. The use case, type and amount of data determine which approach to AI is most appropriate; these approaches include supervised learning, unsupervised learning and reinforcement learning.

In operation, the organization must enable the quick extraction of data for model training. This process helps circumnavigate data silos that can exist within the structure.

Banks and other FS organizations must ask questions like these: What kind of data should they get? How does governance feed into that? And should sensitive data be saved? There is a risk to saving sensitive data, but it could also be the key to making the best recommendation, personalization, or prediction. Once these big questions around data protection and industry regulation are understood and the required data is identified, unique rules need to be created to govern how long the data are saved. This is individual to each use case.

Financial services companies can learn about solving key problems with AI by studying how other companies in other industries did the same.

Information sharing is risky and can be costly in FS because of governance. Therefore, an FS organization must understand what data it may have and what data it may not share with people. Meanwhile most banks’ digital transformation programs are about providing the best experience for everyone. But how can personalization be driven at scale in the FS industry?

Personalization at scale really goes in opposite directions—personalization is micro, but “scale” means to increase in size. That’s why organizations should use data science because ML and AI can interpret and utilize very large data sets better than a human can. Data science enables us to hyper-personalize at scale by finding the unique data inputs that are signals to the type of product or by determining how a company should personalize the customer’s experience.

AI enables the identification, classification, and automation of personalized customer experiences at an infinite scale. In fact, the larger the data sets the more ML and AI can learn to increase the accuracy of the model’s intended output.

In this way a model can come up with a unique message for every person, every time, derived from specific attributes of the individual user.

There is a lot that goes into creating a personalized experience for customers in FS. Especially because most FS organizations have multiple brands and organizational siloes (for example USAA Banking and USAA Car insurance), the operations of personalization can be highly complex. This is an area where AI can be used to help understand the customer.

Machine learning finds patterns and anomalies and classifies individuals by using large data sets that a human cannot correlate or understand because of their size and complexity.

The data features that ML uncovers from large data sets can then be fed into neural network models on a per-customer level to create unique content based on the exact inputs of the data features. NLP can be trained to create hyper-personalized communications, and computer vision can create personalized images that increase engagement.

AI and data science give organizations the opportunity to create personalized messages for the right person in the right channel at the right time, even in a world of increasing privacy and more opaque data. By utilizing zero-party data, first-party data, second-party data and third-party data, organizations can create automated models designed to drive 1:1 messaging at scale through segmentation, optimization and other techniques.

Models that are created using advanced analytics and data science offer the opportunity to supercharge quantitative research and identify similarities in audience segments that wouldn’t be obvious to the naked eye. This can allow FS organizations to better understand and message their customers while also continuously optimizing their understanding of consumers.

Whether through automated content construction (e.g., dynamic creative optimization (DCO), continuous optimization or deeper content and audience analysis, data science and AI can help drastically increase an FS organization’s personalization capability.