The use of data and AI is impacting every component of the banking ecosystem. As banks and credit unions rethink how to integrate information, analyze data and use insights to improve decision-making, they will be in a better position to reduce costs, increase revenues, enhance customer experiences and reimagine business models.
Copyright: thefinancialbrand.com – “Why Data and Artificial Intelligence Will Transform the Future of Banking”
The importance of data and artificial intelligence is at the center of almost all conversations around digital banking transformation. Until recently, actual use of data and AI has lagged behind the level of interest in the banking industry.
Several studies done by the Digital Banking Report confirm that banking and credit union leaders believe AI provides a competitive advantage, yet broad enterprise adoption beyond the use for security and risk was less than 25%. This is not a recipe for success.
The good news is that while much work still needs to be done in the deployment of data and advanced analytic solutions, the pandemic hastened advances in data collection, AI use cases and skills training. It also increased the rate of adoption of enhanced data democratization. These advances have been made across all asset sizes of organizations and across all regions of the world.
In fact, a 2021 McKinsey global survey on AI found that the number of companies reporting AI adoption in at least one function had increased to 56%, up from 50% in 2020. More importantly, the survey also indicates that AI’s economic return is growing.
But, challenges still remain. At the core is the overall trust in the data collected and the results achieved through analysis. According to IBM research, 78% of organizations across all countries said it is very or critically important that they can trust AI output to be fair, safe, and reliable. Aligned with these findings, 83% stated that there was a need to be able to explain how AI arrived at a decision.
Other challenges include the existence of data silos, the complexity of data, and lack of tools for developing AI models. Not surprisingly, an increasing number of financial institutions are also challenged by limited AI skillsets that inhibit successful AI adoption. […]
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