Top 5 Tips for CIOs to Drive the AI Continuum In Banking

By Banking CIO Outlook | Tuesday, August 27, 2019

AI is now a natural fit for businesses and continues to become increasingly suitable for banks as banking operations are becoming progressively digital.  

FREEMONT, CA: As Artificial Intelligence proves its value, industries are jumping headfirst, investing in data experts and technologies to get into the AI game. Banking institutions are increasingly engaging in practices that enable AI and other advanced analytics to unlock the most exceptional value. Such practices need a unified AI vision for leaders, using standard methods and expert development teams, tailoring talent strategies to enable AI at scale, and embedding AI in the decision-making process.

Digital transformation is revolutionizing every industry, and the banking sector is no exception. Modern banking is undoubtedly providing a fertile ground for technologies, especially AI, in order to attract and engage customers. Banking being information-oriented makes it a heaven-made match for AI, driven by data. This switches the business paradigm to an AI-first model for banks for leading the charge in digital and mobile-first strategies. If adequately deployed in a few years, AI can bring sea changes in banking. For this, banks need a clear plan to get this transformation right, and the prime responsibility is vested with CIOs. Below are top tips that can help CIOs who are asking to add up AI in banking operations. 

•  Assessing AI Relevance

Usually, AI is used to enhance existing applications and processes in banking. To establish an AI strategy, CIOs should first measure their organization against AI maturity model. This is used as a framework to identify where the organization is on the potential growth curve, and with this, the management can decide what steps need to be taken. It does not matter where the bank is on the map and how far it has to go, but CIOs must ensure that their strategies are highly adaptive with ample space for experimentation.

•  Overcoming AI Obstacles

CIOs are citing finding uses cases, defining strategy, privacy risks, and integration complexity as the top barriers of AI adoption in banking. This can hinder expected AI project timelines. To this end, banks need to set realistic schedules for AI projects, and CIOs must ensure the desire to push forward with popular technologies to overrule drawbacks. It is also challenging to determine AI projects ROI because most banks are too early in the process to see any return. To surmount these hurdles, CIOs should set realistic expectations and identify suitable use cases and create a new organizational structure.

•  Improving Data Assets

Most models of AI for banking are relatively easy to build and purchase. But what makes them valuable is data when it is easy to access, gather and prepare for AI algorithms. Major banks are having severe problems organizing their data as it is scattered across systems making it difficult to retrieve. Also, customer interactions within branches are partially logged, resulting in losing the value it can provide. To remediate these challenges, CIOs need to develop a systematic way to build up a bank's data assets, using an internal and external source. CIOs also need to subtly balance proactive collation of different data sets with a reactive search for data when the business seeks to deploy AI application.

•  Deploying AI at Scale

To be effective AI-first banks need to deploy AI models at scale. Deploying AI is not particularly difficult, but applying it to a million people is a daunting task due to the unpredictable nature of client contexts. CIOs need to develop their infrastructure with transparent deployment platform across the bank and a method for managing models. This method can enable AI initiative at scale, including automation, customer transaction analysis, customer service, and many more.

•  Incorporating AI in Organizational Culture

CIOs need to make sure that employees see AI as an opportunity but not as a threat to their job security. AI can be applied widely to support team and fully automate processes. To identify opportunities, employees need to scout them. Lastly, the management must also make sure that AI services are extending beyond banking services as the boundaries between banking and financial services. AI will only accelerate this trend, and CIOs must be keen to catch this present opportunity for banks to become trusted financial advisers. This extension beyond banking services highlights the potential of AI to extend the playing field.

Could be a tough transition for traditional banks! But letting go of static, outdated models will enable them to embrace innovations that focus on customer needs. In a world where most consumers think and act digital, CIOs must be prepared to leverage new technologies. AI that assists human intelligence to accomplish banking operations could rapidly upend the competitive landscape across banking. It contributes to a more protected banking environment and offers customers with assistance and insights that give them more control and an enhanced sense of security in banking operations. It is sure that the banking and financial service sector will see increasing adoption of AI in the coming years. CIOs who are successfully scaling AI focus on guiding their organizations in the long run.

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