Charting the AI landscape in retail banking

The financial services landscape is experiencing considerable political and macroeconomic turbulence, compelling clients to embrace new innovations and advancements to adapt to the evolving market.

Organizations are also having to keep up with rapidly evolving customer expectations, which include demands for an increasingly personalized experience without a drop in efficiency – writes Simon Kent, Americas and Europe Lead, Financial Services, Kearney.

In short, financial services companies are undergoing a profound change, reminiscent of the transformative climate we witnessed during the digital revolution in the nineties.

AI applications in retail banking

While banks have long utilized machine learning, the introduction of AI in day-to-day processes will be, and is, a game changer.

Generative AI takes the problem-solving ability of a computer and combines it with a set of skills that are associated with human capabilities.

A myriad of use cases for generative AI exist across the retail banking landscape.

However, these can largely be categorized into three main cohorts: customer-centricdecision-making, and enterprise-focused use cases (see figure).

From a customer-centric perspective, the spectrum of possibilities is significant. Generative AI unlocks the ability to create personalized experiences by leveraging data to understand an individual’s needs in real time.

For example, when a customer interacts with their bank, they will now receive tailored solutions custom built for them.

From addressing complaints and enhancing day-to-day experiences to analyzing customer sentiment, generative AI can help organizations leverage reams of data to effectively meet these capabilities.

Customer service is also changing with the introduction of AI-powered processes and many consumers are quickly becoming used to engaging with their banks in different ways, such as using chatbots to facilitate routine inquiries or balance checks.

Significant strides have also been made with decision-making applications. For instance, take credit decisioning.

While traditional models heavily rely on bureau data and human underwriters, emerging AI-driven models are proving to be faster and often more accurate, enhancing outcomes for both banks and customers.

That being said, the challenge lies in determining the extent to which humans can take their hands off the wheel, a question that both regulators and institutions are grappling with.

Equally, AI is revolutionizing the risk management landscape by using a broader range of data to enhance risk assessment and capital allocation.

By harnessing the power of AI, banks can more effectively manage risks and identify potential vulnerabilities.

Additionally, in combating fraud, AI serves as a formidable ally with AI-powered solutions offering proactive measures to safeguard against fraudulent transactions.

At an enterprise level, the opportunities largely lie in onboarding and training processes. From streamlining the integration of new hires to optimizing the hiring process itself, AI tools facilitate talent acquisition and development.

This includes crafting personalized learning and development initiatives and harnessing AI-driven insights to support the professional growth of individuals.

When considering the three main cohorts of use cases for AI in retail banking, we’re observing promising progress at the customer-facing level and considerable potential at the decision-making level.

At the enterprise level, while certain applications, especially in HR, have advanced, others remain in varying stages of development.

However, it’s imperative to proceed with caution, balancing employee, customer, regulatory, and risk management concerns with the capabilities of AI systems to utilize the technology responsibly.

Training AI models to meet regulatory standards

Many banks are finding that their models are outperforming human decision-makers, raising questions around accountability and transparency.

Both banks and regulators must work together to navigate these challenges as technology outpaces our ability to assess and mitigate associated risks.

It’s crucial to establish guidelines and regulations that promote fairness and safety for all parties involved, particularly when it comes to vulnerable customers in the retail banking sector.

This requires striking a delicate balance between leveraging advanced technologies and maintaining human oversight where necessary, especially in complex situations.

Human interaction vs. AI in banking

In today’s digital landscape, many customers are already accustomed to interacting with non-human interfaces, finding comfort and convenience in the process.

Whether it’s navigating an online banking app independently or engaging with a chatbot, customers are increasingly at ease with these interactions.

As technology continues to evolve, the question remains: How will customers respond?

Ultimately, it boils down to personal preferences. Some customers may value personalized human interaction, and are willing to pay for it, while others may prefer streamlined digital solutions for certain transactions.

It’s imperative for banks to guide customers to the appropriate channels based on their bespoke preferences and needs.

By understanding and catering to various customer segments, banks can ensure they deliver the right level of service through the most appropriate channels for each inquiry.

Face-to-face interactions in banking will continue to play a vital role in the industry and this rests on three fundamental factors.

Firstly, in situations involving complex transactions or queries, customers usually seek reassurance from a human being who understands their circumstances and can provide tailored solutions.

Secondly, when customers have a complaint, the opportunity to communicate face to face allows for a deeper understanding and resolution of concerns, instilling a sense of confidence in the process.

Lastly, despite the prevalence of technology, face-to-face interactions remain invaluable for guiding customers through the digital tools and platforms provided by the bank. In essence, for complex transactions, complaint resolution, and navigating technological challenges, face-to-face engagement is still vital.

Data will be the key differentiator

Banks have access to an unparalleled set of customer data, surpassing that of many players in the ecosystem.

While big data companies boast impressive tools and capabilities, none possess the depth of insight into customers’ financial behaviors, product preferences, purchasing habits, and credit profiles that banks do.

The real challenge lies in effectively leveraging and managing this data to enhance customer experiences.

However, many banks continue to struggle with outdated legacy systems that scatter data across disparate platforms, hindering their ability to use it innovatively.

We often hear our clients describe their banks as “museums of technology,” grappling with antiquated mainframes and disjointed data infrastructure.

Recognizing the critical importance of data, many banks are embarking on extensive programs to streamline and modernize their data management processes.

As such, we firmly believe that data is a key differentiator in the banking landscape.

Banks that can effectively organize their data and unlock their potential to drive valuable use cases will emerge as the winners in this increasingly competitive landscape.


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