Financial service entities are suited to use advanced analytics for external and internal benefits, as they have experience with analytical tools and large data sets. In fact, insight is gathered from various processes including everyday banking activities and payment services, which can help in boosting the power of machine learning.
Both credit unions and banks claim at first that they will use their data to enhance the customer experience. However, most organizations and the entire finance industry have failed to keep up with customer expectations around digital engagement or digital capabilities in comparison to what large tech companies are offering.
For this reason, there is a weakening of trust and a large amount of lost revenue because of failing to know the customer and mismanaged relationships.
According to research conducted by the Digital Banking Report, 35 percent of financial organizations have applied at least a single machine learning solution. This figure is higher in comparison to previous studies done by the same entity.
Machine learning can make banks smarter in terms of providing better intelligence and customer insights, resulting in improved customer experience. This aspect is believed to be the key to growth, increased profits and differentiation.
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AI use in Financial Services
According to the study, financial organizations that were yet to apply a solution, 23 percent of them were convinced that they would implement an AI solution in the coming six months. Another 13 percent believed that they would deploy a machine learning solution by the end of 18 months.
Furthermore, 17% hinted that a machine learning solution as part of their blueprint in the coming 18 months, while 12 percent of the entities did not have plans for implementing an AI solution in the same duration.
The state of AI application in terms of an organization ’s size was found to be considerably higher for the largest financial organizations(more than $50 billion). In fact, they were found to have used at least one machine learning or AI solution.
Additionally, most financial organizations suggested that they were considering deploying credit scoring and fraud AI or machine learning solutions in the coming 18 months.
The usefulness of AI in Banking
Past findings have discovered that the deployment of machine learning and AI solutions in the banking space has mainly been due to evolutionary solutions like security, fraud and credit scoring instead of revolutionary ones like proactive alerts and personalization among others.
Furthermore, 61% of respondents queried during the survey were convinced that most solutions even now are more evolutionary than revolutionary.
AI and machine learning technologies are the most effective ways of managing fraud and risk.
Machine learning can provide complex authentication processes and security safeguards that are enough to avert fraud while delivering the reliability and speed needed by modern-day customers.
Advantages of AI in Banking
In the financial services space, AI and machine learning systems are being deployed by more organizations on a daily basis.
The research by the Digital Banking Report revealed that AI is currently being utilized in fraud and risk reduction as well as providing customer support and enhancing the targeting of messages.
Source FinancialBrand
