Today’s customers interact with financial institutions in a variety of ways, whether in-person or through online banking platforms and mobile applications. These interactions are creating a growing volume of customer data that contains tremendous insight for companies that know how to collect, process, and act on it. High performance computing (HPC) tools are allowing financial services companies to make sense of their growing volumes of customer data, enabling a complete 360° view that can help drive profits, prevent fraud, and enhance customer satisfaction.
With each new interaction, customers are leaving valuable digital breadcrumbs behind that hold the key to improving their experience and serving them as effectively as possible. To uncover these valuable nuggets of information, financial services companies must merge both transactional and behavioral data to gain a complete view of each customer. Transactional data, which results from activity like an ATM withdrawal or a visit to a branch, helps companies collect data regarding their most frequent interactions with customers. Behavioral data goes a step beyond and incorporates known information about the customer, such as lifestyle and income, to make assumptions about their typical financial behavior.
A true 360° view of the customer must also include past, present, and future data in order to gain a complete picture. For example, using a customer’s transaction history can provide insight into their preferred method of interaction with a company, or products that may have caught their interest in the past. Present data incorporates data the company knows about the customer right now, including who they are and how they relate to the organization. And future data refers to actions that the company plans to take to further their relationship with the customer, upsell, or personalize offers.
Leveraging this rich customer data can help financial institutions enhance their operations in a variety of ways:
- Drive higher profits – A complete knowledge of the customer base can help financial companies understand who their most profitable customers are, and focus more on selling high-value goods and services to that particular segment.
- Prevent fraud – A solid understanding of normal customer behavior can help pinpoint anomalies or unusual activity that may signal a fraudulent transaction is taking place.
- Personalize offers – Knowledge of the customer’s needs and preferences can help companies better tailor offers to specific customers where they are likely to get the most uptake.
The challenge for many financial services companies is that they are dealing with so much customer data that they struggle to store, process, and analyze in real-time. Customer data is too often stored in siloes, which makes it very difficult to analyze. And traditional compute infrastructures are struggling to sort and compare massive datasets on the fly.
The parallel processing capabilities of NVIDIA graphics processing units (GPUs) are perfectly suited to handle large volumes of complex datasets, both structured and unstructured, which is useful for financial companies who are collecting customer information from a variety of data sources. NVIDIA GPUs are also providing the compute power and performance to support deep learning algorithms, which the financial sector is increasingly adopting in an effort to improve personalization and customer service. The backbone of GPU computing is a robust server platform capable of supporting large data sets and delivering the highest levels of performance, and efficiency.
Today’s financial services companies have a wealth of information at their fingertips that can help them not only effectively serve customers, but delight them in the process. HPE and NVIDIA are providing the HPC solutions that help financial services companies use this valuable customer data to their advantage.
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