AI and machine learning not being used to full potential in finance

With high-volume transactional data, historical insights, accessible computer power and new analytic tools, few industries are better suited for using artificial intelligence and machine learning than banking. Yet, few organizations have fully leveraged the many ways machine learning can improve back office operations or the consumer experience.

Financial services organizations realize they have the potential to apply advanced analytics for both internal and external benefits since they have large data sets and experience with analytical tools. From payment services to everyday banking, insight is captured that can make machine learning more powerful.

The good news is that banks and credit unions state that they are going to apply the data at their disposal to improve the customer experience, first and foremost. Unfortunately, most institutions – and the industry as a whole – have not kept pace with consumer expectations around digital capabilities or digital engagement compared to other industries or what the large technology companies are providing. As a result, there is a significant amount of lost revenue and weakening of trust due to mismanaged relationships and the inability to know the consumer.

In research done by the Digital Banking Report, we found that 35% of financial organizations have deployed at least one machine learning solution. This number is quite a bit higher than other recent studies done in the industry and by the Digital Banking Report. For instance, in a survey done by the Digital Banking Report in the Fall of 2017, only 15% of financial services organizations globally had implemented an AI solution. Part of this variance may be that the size of organization skewed smaller in the earlier study.


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