The most effective AI tools for financial institutions
Although almost all financial institutions use some form of machine learning or artificial intelligence (AI), most tend to use them in areas for which they are ill-equipped or even highly inefficient.
In our ongoing research series about AI-use by financial institutions, AI Innovation Playbook: Moving Toward a Future of Smart Agent Adoption reports on the experiences of more than 200 financial executives regarding the advantages and limitations of AI, which AI tools are in use, and which functions they help with. The report also explores the reasons financial executives adopt Smart Agents, and how that benefits their organizations.
For example, 54.5 percent of financial institutions surveyed used data mining to enhance customer lifecycle management, even though it’s the third most effective tool for the job. AI systems and neural networks would be much more effective, but less than 3 percent use them for this purpose.
These limitations can be overcome by adding Smart Agents, a powerful, distributed file system specifically designed to store knowledge and behaviors. This distributed architecture allows lightning speed response times (below 1 millisecond), and allows for unlimited scalability and resilience to disruption as it has no single point of failure.
Download AI Innovation Playbook: Moving Toward a Future of Smart Agent Adoption so you can:
- Learn the limitations and advantages of using AI and ML in financial institutions (FI), including staffing concerns
- Understand the functionality of six of the most common AI and ML tools, including how satisfied FIs are with them
- Discover the positive impacts that FI executives believe would result from using Smart Agents to optimize their AI program
- See where your organization fits into the landscape of Smart Agent adoption—and it this technology is most beneficial