Banks and credit unions are floating in a sea of data. Credit card transactions, depositors’ account transactions, mortgage payments, college funds and investment choices are all locked inside financial institutions’ data warehouses.
The same institutions also have access to a great deal of information about who their customers are: age and gender breakdowns, credit scores, places of residence and more.
Having so much data is both a blessing and a curse. On one hand, data is the key to delivering quality customer experiences, attracting new customers, detecting fraud, complying with regulations and efficiency gains. But for banks and credit unions, especially small ones, the hard part is converting all that data into meaningful insights to derive business value.
Doing that effectively demands the deployment of artificial intelligence and machine learning solutions powered by high-performance computing (HPC). Financial institutions have long depended on HPC for number-crunching. But today these institutions are in the midst of a transition to an artificial intelligence-driven environment. Let’s explore what that means and how banks and credit unions are taking advantage.
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Why AI Is Key to Banks Data Evolution
AI solutions are designed to operate in big data environments. They work by using algorithms that quickly comb through mountains of data to discover the specific information that’s most valuable in making particular decisions.
In other words, AI helps find a narrative that could be lost in a sea of data. This not only informs human decision-making; it can actually automate many decisions, helping banks become faster, more efficient and more profitable.
Applications for AI are numerous in financial services. For example:
- Fraud-detection algorithms can flag suspicious activity if transactions are attempted on a customer’s card in New Orleans when the customer recently made a purchase in Chicago, where he or she is known to live.
- Customer service algorithms can handle many simple transactions and commonly asked questions, either via voice- or text-based bots. Some institutions have started experimenting with AI systems that can detect certain human emotions, like frustration or anger, then prompt the system to turn the interaction over to a live human and help evaluate the humans’ performance on the call.
- Compliance algorithms can help banks meet the wide and complex array of laws and regulations they confront by evaluating the content of email, text and even phone conversations for potential compliance issues.
The Tech Needed to Support Artificial Intelligence
But to get the benefit of HPC-supported AI, institutions need to ensure they have the right technology infrastructure to support high-intensity data processing and to store big data. Investing in the right technology ensures an organization can take in multiple data sources to inform smart decisions and actions.
In particular, institutions require robust storage, networking and processing resources, such as those on offer from leading technology brands such as HPE, Cisco, Intel and Nutanix.
But when making the upgrade, institutions should think through a series of questions:
- Do we already have the necessary storage and network infrastructure to support advanced, rapid data processing? What more storage is needed, and what type of storage might make sense for us?
- What is currently holding back our data and analytics strategy? What are the biggest challenges in this regard?
- How are we identifying customer behavior and needs? How are we delivering services and products based on those needs?
- How are we protecting the organization from internal/external fraud and cyberattacks?
- Are we struggling to meet compliance regulations and reporting? What is the nature of the challenge and how might technology help us meet it?
This is a complex transition for any bank, credit union or other financial institution, one that shouldn’t be undertaken without expert help. We regularly work with financial services firms of all sizes to identify opportunities for AI deployments, design technology solutions that capitalize on these opportunities, and train and test models.
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