Artificial intelligence (AI) is one of the hottest topics in financial services these days.
Financial institutions face new challenges and new digital-native competitors. AI and machine learning can be the key to generating more revenue, reducing costs, and enhancing customer experience while ensuring data protection and compliance.
The possibilities are nearly limitless and the use cases diverse. From algorithmic trading, fraud detection, and data security to customer experience and credit and insurance approvals, financial services firms are benefiting from the power and capabilities of AI. However, these gains aren’t always easy to attain.
The AI opportunity doesn’t come without challenges. AI is complex and its unique nature can make enterprise-scale implementation challenging. Ensuring that actual results match expected outcomes and investments result in returns will be vital. In particular, there are three areas to be aware of as your organization starts to embrace AI.
Translating Ideas into Revenue
One of the most common complaints regarding AI is the difficulty in moving from modeling to production environments that generate ROI. This “proof-of-concept purgatory” can result in far-ranging negative outcomes. Time and resources are wasted when projects fail to see the light of day. And lines of business are deprived of much-needed insights to drive success. Talented employees can wind up frustrated and leave the company. Executives may also become frustrated and disillusioned, leading to valuable AI projects ending up in the scrap heap.
Supporting Data Scientists
In many cases, data scientists spend time acting more like data shepherds. They have to wrestle with supporting tasks such as data capture, data cleaning and management, and infrastructure management. You can expect a certain amount of this as your organization develops enterprise-grade AI capabilities. But when left unaddressed, valuable resources are underutilized and the business is deprived of higher-order insights.
The advent of the public cloud has been a boon to the development of AI opportunities and capabilities. But, if not used wisely, these same resources and tools can quickly lead to spiraling costs without the business outcomes to justify them. It’s a classic case of strengths potentially transforming into weaknesses as a project proceeds and requires special insight and handling as efforts evolve.
The Promise of AI in Financial Services
As financial institutions face new challenges and new digital-native competitors, AI and machine learning can be the key to generating revenues and enhancing customer intimacy while ensuring data protection and compliance. The opportunity doesn’t come without challenges, however. In our white paper “Can Artificial Intelligence Transform the Financial Services Industry?,” we explore the unique challenges to succeeding with AI in financial services. Pure Storage® enables enterprise-level AI success by redefining the storage experience and simplifying how people consume and interact with data. In fact, AI drives Pure1®, an advanced storage management platform.