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 […] 
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.
Machine learning has potential to make banks exponentially smarter. “Smarter” in this case means delivering better customer insights and intelligence, and thus a better customer experience — something most in the banking industry now believe is the key to differentiation, growth and increased profits.
Use of AI in Financial Services
For those organizations that have yet to deploy a solution, 23% believed they would have an AI solution in place in the next 6 months to a year, with another 13% believing they would have a machine learning solution in place within 18 months. While 17% indicated a machine learning solution was on their roadmap in the next 18 months, 12% of organizations surveyed had no plans to implement any artificial intelligence solution in the next 18 months. Given that almost half of executives surveyed in another report thought that AI would be mainstream in the next 2 years, many organizations may be caught off guard from a technology perspective.
When we evaluated the state of AI deployment by the size of organization, it is not surprising that significantly more of the largest financial institutions (over $50B) have deployed at least one AI or machine learning solution. When we asked financial organizations which AI or machine learning solution they have deployed or were planning to deploy in the next 18 months, fraud and credit scoring solutions were the most likely to be in place or on the short-term horizon. In fact, the category of fraud, security and biometrics represented 3 of the top 5 solutions in place or being considered. This is not surprising given that these are traditional uses for machine learning and AI deployment.
The next most likely functionality to be in place, or in near-term plans, were related to chatbot implementation (3rd place in the survey) and personalization solutions. Interestingly, of all of the solutions listed, chatbots had the lowest ‘no plans’ response.
Read the source article in The Financial Brand.