AI in Banking - How Can Banks Meet the AI Challenges?

AI in Banking - How Can Banks Meet the AI Challenges?

AI in Banking - How Can Banks Meet the AI Challenges?

Artificial Intelligence (AI) is enabling digital transformation in the banking industry.

As per a joint research conducted by National Business Research Institute and Narrative Science in 2020, about 32% of banks are already using AI technologies such as predictive analytics and voice recognition, thus increasing the need and demand for AI development services globally. 

With AI, banks can enhance customer experiences, secure payments, deliver personalized content, and improve ROI. In fact, McKinsey estimates that AI can add up to USD 1 trillion of value each year for global banking.

As banks are implementing and integrating AI technology into their business processes, they are also facing unique challenges, which is hampering the technology’s adoption at full scale. 

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Challenges in AI adoption in the Banking Sector

Evidently, the banking sector is facing two types of challenges. While on one hand, they need to achieve speed and agility in their operations, on the other hand, they also need to continue managing the security standards and regulatory compliances.

To better understand the challenges, let us look at the three major obstacles banks face.    

Legacy Infrastructure

Implementation of AI into business processes often pose new requirements for data, infrastructure, and technology needed to build and scale models. It is not only expensive to replace large legacy systems but building AI banking applications can be compute-intensive, resulting in off-putting upfront costs. 

Governance Structures and Regulations

Implementation of any new technology can be at odds with the highly regulated banking and financial services industry. Risk and compliance teams may struggle to understand the potential vulnerabilities or create appropriate internal regulations. Without the right governance, the deployed AI solution may result in unintended consequences such as access denied to financial products or privacy breaches.     

Lack of Clear Strategy

In a constantly evolving technological environment, amid high costs for new infrastructure and human capital, executives may fail to fully embrace AI technology. If that happens, it will be difficult to implement a successful organization-wide strategy to effectively utilize the AI technological capabilities. 

The Future of AI in Banking

Despite the industry’s current challenges, the future of AI in banking is bright. As reported by Finextra in July 2021, about 83% of financial service providers believe that AI is important to their company’s future success. Also, about 34% businesses believe that AI will increase their company’s revenue by 20% or more.  

As AI technology continues to gain momentum, enterprises will not only look forward to gaining new capabilities and leveraging the growing importance of mobile apps in banking, but they will also prepare legacy systems with dynamic technologies. 

How Banks Can Adopt the AI-First Approach

With the promising future and current adoption challenges of AI in banking, how can banks leverage the technology to remain competitive in the market? 

Banks must invest in transformation capabilities across four areas - customer engagement, AI-powered decision-making, core technology, and operating model. While each of them have a unique individual role to play, when all of them work in unison, they enable a bank to provide customers with distinctive omnichannel experiences and drive rapid innovation cycles critical to remaining competitive in the market. 

  • Customer engagement: With the rapid technological advances, customers expect their banks to be present throughout their journey to enable a frictionless experience. For the bank to be present in customers’ lives always, while solving their latent and emerging needs, they will have to reimagine how they engage with their customers and undertake the required shifts.
  • AI-powered decision-making: Banks will have to utilize the full potential of AI technology to be able to deliver personalized messages and decisions to millions of users in near-real-time. The AI techniques applied across multiple bank domains can either replace or augment human judgment to produce significant outcomes (such as higher accuracy), enhance customer experience, and gain actionable insights for employees.   
  • Core technology: To integrate AI in banking processes successfully, banks require scalable and resilient core technology components such as a technology-forward strategy, modern API architecture, and data management techniques. A weak legacy infrastructure needing modernization can reduce the effectiveness of investments made in the above two areas - customer engagement and AI-powered decision-making. 
  • Platform operating model: To incorporate AI successfully, banks will need a new operating model for their organizations to achieve the required agility and unlock the value across the aforementioned areas. The platform operating model envisions cross-functional teams in a bank organized as a series of platforms. Each platform team controls its assets such as data, infrastructure, and KPIs. In return, the team delivers a suite of products or services either to end customers or to other platforms within the bank.      

Final Thoughts

A practical way to get started with an AI-first approach is to evaluate how the bank’s strategic goals can be accomplished using AI technologies. Once this is thought through, banks should evaluate their transformation capabilities across all the four areas mentioned above. Once this is evaluated, banks can convert the insights into an executable transformation roadmap.

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Anas Bouargane

Business Expert

Anas is the founder of CEF Académie, a platform that provides guidance and support for those willing to study in France. He previously interned at AlphaSense. Anas holds a bachelor degree in applied economics from the University Paris Sud.

   

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