Generative AI is a term that is quickly gaining traction in the world of banking and finance. With its knack to create content, analyze data and automate processes, generative AI has the potential to revolutionize the industry. But what exactly is generative AI, and why is it such a big deal?
Generative AI is a subgroup of artificial intelligence involving machines generating new content, such as text, images, or music, based on previous examples. It works by analyzing vast amounts of data and creating new content based on patterns and similarities in that data. This ability to learn from data and create new content makes generative AI a powerful tool for banks, as it can be used to analyze customer behaviour, automate repetitive tasks, and generate personalized marketing messages, among other things.
But why is generative AI such a game-changer for banks? One reason is that it is becoming increasingly accessible. Cloud-based AI engines are now surpassing human capabilities in some specialized skills, and anyone with an internet connection can access these solutions. The availability of generative AI to the masses will unlock novel possibilities for innovation, optimization, and transformation.
While it’s still early in the game, a number of banks are already utilizing generative AI in various applications. These include AI-powered chatbots that can answer customer inquiries about loans and financial topics, personalized next-best-action recommendations for advisors based on customer data analysis, targeted marketing messages customized for specific customer groups or even individuals, and streamlining mid-to-back-office operational expenses like post-trade processing.
According to Accenture’s research on AI, just 1% of financial services firms are AI leaders, and the median score for AI maturity in financial services is 27 on a 0-100 scale. However, banks that fail to harness AI’s potential are already at a competitive disadvantage.
Although banks have utilized AI applications in process engineering and Six Sigma settings to generate conclusive answers based on structured data, generative AI offers greater flexibility. With its capability to search through unstructured data for insights, the potential applications of AI in financial services can be significantly expanded.
While AI can assist humans in various use cases, I believe its ultimate potential lies in helping humans do more work and do it better or freeing them from repetitive tasks. However, in banking, the true Holy Grail will leverage generative AI to significantly reduce the programming cost while dramatically improving the speed of development, testing, and documenting code. Just picture being able to read COBOL code from an old mainframe and rapidly optimize, analyze, and recompile it for a next-gen core. Such capabilities could significantly cut down on bank expenses since roughly 10% of a bank’s cost base currently relates to technology, including the maintenance of legacy applications and code.
The possibilities for generative AI in banking are vast, but some of the most promising areas for its application include:
KYC/AML and Anti-Fraud Work – One of the most promising applications of generative AI in banking is in the area of know-your-customer (KYC) and anti-money laundering (AML) compliance. With the ability to analyze vast amounts of data and detect patterns that may be indicative of fraudulent activity, generative AI has the potential to significantly improve compliance efforts and reduce the risk of financial crime.
Personalization and Customer Engagement – Generative AI can also be used to personalize marketing messages and create targeted customer experiences. By analyzing customer data and generating content that is tailored to individual preferences and behaviour, banks can improve customer engagement and drive revenue growth.
Operational Efficiency – Another area where generative AI can significantly impact is improving operational efficiency. Banks can reduce costs and improve productivity by automating repetitive tasks and optimizing processes.
Overall, it’s clear that AI’s impact on banking is just beginning. It could drive reinvention across every part of the business. Banks that prepare for this now and invest in AI could see significant benefits in the future. But with great power comes great responsibility. Banks must ensure that AI is used responsibly and set guardrails for acquiring, refining, and deploying data. Managing regulatory and privacy risks is a good start, but they must also be vigilant against bad actors who can access the same tools and use them for malicious purposes.
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