As with any new technology, the adoption of Artificial Intelligence in the business world has been welcomed with open arms and met with some hesitation. However, current research and forecasting show large benefits in adopting AI for banking.
The integration of AI in the financial sector is predicted to lower costs by 20%, leading to savings that are over $1 trillion, by 2030. However, while some can see the benefits of artificial intelligence, others need more convincing. For the implementation of AI in banking to be successful, businesses should focus on improving their operations and customer service while alleviating any customer concerns about the use of AI. Here are the top three benefits of AI in Banking.
Automating Tasks with AI
Though AI can often be used as a broad term that encompasses many intelligent emerging technologies, two of the most relevant branches of AI in the financial industry are machine learning and natural language processing. Machine learning can help customers manage their accounts and asset portfolios and recommend certain courses of action based on analysis of customer history and current trends in the financial industry. The more data that is available to analyze, the more that machine learning algorithms can adapt and improve, leading to better recommendations.
This technology can also make automated recommendations for loans and investments, both in finding the best fit for the customer and helping banks with loan and mortgage approvals. Closer to home, banks are implementing AI assistance for standard banking tasks such as account transactions, paying bills and money transfers. And, using natural language processing, banks are developing software so that customers can log in and perform these tasks using vocal interactions.
Using AI to Improve Customer Service
In addition to automating standard tasks, banks are using AI to foster personalized, attentive customer service. Since customers will often have questions that don’t require a human customer service representative, AI-powered chatbots ensure that customers can get answers to their questions quickly. Banking mobile apps that are integrated with AI can assist in the creation of personalized financial plans. Predictive analytics and machine learning can be utilized to shape the user experience within the app based on feedback from user interaction.
AI financial assistants are already being implemented in the financial industry. Bank of America created an AI digital assistant for their banking mobile app that is now being used by over one million BoA members. The AI, “Erica,” interacts with customers to help with standard banking tasks, even providing a seamless transition if a human representative is needed. This feature is important as customers can often become frustrated when they have difficulty finding the answers to their questions, especially when they have to repeat their issue over and over again.
While Erica is already improving the customer banking experience, the goal is that the AI will continue to increase in usefulness as a financial assistant. Current goals include the ability for Erica to give personalized financial updates and proactive advice to users. As AI continues to evolve, more uses will be tested and the most beneficial services will be implemented.
AI Advancements in Fraud detection
AI is also being implemented to combat fraud and money laundering. It can be used to spot inconsistencies and anomalies in order to flag potentially fraudulent transactions. The extreme efficiency and automation of machine learning are opening up opportunities for fraud detection that only became plausible in recent years.
Due to the extremely large amount of data that is generated every day, it would not be possible for humans to monitor or catch all instances of fraud and money laundering. Additionally, since machine learning has the ability to adapt over time, the algorithms get better at picking up threats. It is in this ability that the true benefit of AI for fraud detection lies.
While the automated detection capabilities of machine learning are much more efficient and accurate than human capabilities, these benefits increase even more when paired with predictive analytics. With these two technologies combined, financial institutions can look for places where financial or personally identifiable information could be compromised. This enables banks to implement proactive strategies to improve the security of their data, preventing financial crimes before they occur.
AI in Banking Use Case
A specific use case of AI in banking is preventing credit card fraud. Customers may not even be aware that this technology is helping to keep their information safe. However, if you’ve ever received an alert about a flagged transaction, it is because AI has the ability to analyze customer purchase behavior. This is a similar approach to what is used in retail in order to make product recommendations. When something looks out of the ordinary, AI alerts the bank and the customer who may be at risk.
Though it might be an inconvenience when the purchase was actually legitimate, cardholders are extremely grateful when their stolen information is found early on before too much damage has been done. They can cancel their card and move on, rather than finding out months down the road and having to haggle with their bank in order to prove which transactions were theirs and which were made by a credit card thief on a shopping spree. To maximize the benefits of this alert system, there are certain things that customers can do to limit the number of false alarms, such as letting their bank know in advance when they are planning to travel. As time goes on, the accuracy of fraud detection using AI will continue to improve.
From automating banking tasks to customer service and fraud prevention, AI continues to benefit banking through increased efficiency and a customer-centered approach. In order to reap the benefits of artificial intelligence, however, the implementation of AI requires the work of experienced developers. 7T specializes in emerging technologies such as machine learning, natural language processing, and data lake creation.
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