Published on February 16, 2024, 11:33 am

Generative AI is revolutionizing various industries, and the banking sector is no exception. While the implications of generative AI on business and society are well-documented, banks face unique challenges and opportunities when it comes to adopting this technology.

According to Avanade’s latest research, bankers primarily view automation and efficiency as the greatest benefits of generative AI. By harnessing its potential, AI can completely transform customer onboarding processes, enhance fraud detection capabilities, automate regulation and compliance requests, among many other possibilities.

However, despite recognizing the advantages of generative AI, nearly half of bankers surveyed admitted that they don’t fully understand its intricacies and governance requirements. This lack of comprehension poses a significant hurdle in realizing the full potential of AI within the banking industry.

Implementing generative AI incorrectly can lead to unwanted problems. For instance, there is a risk of “hallucinations,” which refers to incorrect information being presented as fact. Additionally, there’s also what experts call “black box syndrome,” where it becomes unclear how AI decisions are made. Transparency becomes crucial for building trust in AI systems if banks want to fully embrace their capabilities.

One essential factor that determines the success or failure of implementing generative AI is data quality. To unlock the true power of your data, it needs to be prepared for AI integration. This involves moving data into dynamic cloud environments and consolidating information from different sources scattered throughout the business ecosystem. Inaccurate or conflicting data can lead to poor decision-making with potentially severe financial and reputational consequences.

Banking leaders are becoming increasingly aware of this issue. The research from Avanade shows that almost two-thirds of them do not completely trust the data their companies currently use. Furthermore, only slightly over a quarter have full trust in their ecosystem partners’ ability to protect customer data adequately.

The banking industry faces additional hurdles due to their heavy reliance on mainframes for core business processes within 90% of the top 100 banks. These legacy systems are often highly customized, complex, and expensive to maintain. Data is commonly stored in isolated “islands” and silos that hinder sharing and collaboration. Some of this data may also be insecure or no longer fit for purpose.

Addressing these challenges is crucial for banks. They must prioritize the necessary data and ensure it is ready for AI integration. Doing so can lead to remarkable outcomes. For example, a European bank partnered with experts to tackle churn reduction in its mortgage business by utilizing generative AI. By feeding over 100 variables into machine learning models, the bank successfully predicted mortgage churn risk and fraudulent credit card transactions. The result was a nearly 50% reduction in mortgage churn over six months, alongside a 2% decrease in underwriting fees and a 7% increase in early detection of credit card fraud.

The potential of generative AI in the banking sector is immense. Unleashing the power of data is the key to success for financial institutions looking to stay ahead in this evolving landscape. If you’re interested in learning more about how banks can become AI-ready, Avanade’s report titled “Banks: Are You AI-Ready?” provides valuable insights.

Sponsored Links: [Here you can add relevant links related to generative AI within the banking industry]

Share.

Comments are closed.