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Chatbots were the most significant implementation of AI in banking. Chatbots provided a much-needed solution for customer service inquiries that helped reduce customer support staffing needs and increase overall customer satisfaction. However, AI in banking has come a long way from chatbots. AI can now perform complex tasks, optimize services and operations, and enhance security measures.
The application of AI in banking has the potential to transform the entire banking industry by automating tasks, reducing costs, and enhancing customer experience. AI technologies, such as natural language processing, machine learning, and data analytics, can provide banks with deep insights into customer behavior, preferences, and needs. With this information, banks can personalize their services and products, offer more targeted marketing campaigns, and improve their overall operations.
The adoption of digital banking has led to rising customer expectations. During the COVID-19 pandemic, the use of online and mobile banking channels increased by an estimated 20 to 50 percent globally, and this trend is expected to continue even after the pandemic subsides. A significant percentage of consumers across diverse global markets intend to cut back on branch visits after the crisis.
As customers increasingly use digital banking services, they expect more from banks, especially compared to the standards set by leading consumer-internet companies. These digital experience leaders continuously raise the bar on personalization and sometimes anticipate customer needs before the customers become aware of them, offering highly tailored services at the right time, through the right channel.
At the same time, leading financial institutions are steadily increasing their use of advanced AI technologies. According to McKinsey’s Global AI Survey, almost 60 percent of financial-services sector respondents report embedding at least one AI capability. The most commonly used AI technologies include robotic process automation, virtual assistants or conversational interfaces, and machine learning techniques.
While many firms use AI for specific use cases, more banking leaders are taking a comprehensive approach to deploying advanced AI, embedding it across the full lifecycle from the front- to the back-office.
While AI is transforming the banking industry, there are several obstacles that prevent banks from deploying AI capabilities at scale.
One of the key requirements for effective AI deployment is access to high-quality data. However, in many cases, banks struggle with data quality issues such as incomplete or inaccurate data. In addition, some data may not be readily available due to siloed systems, data protection regulations, or privacy concerns. Without access to clean and relevant data, it can be challenging to train AI models effectively.
The banking industry is subject to a wide range of regulatory requirements and compliance standards. When deploying AI capabilities, banks must ensure that they comply with these regulations, which can vary depending on the country, region, or jurisdiction. Meeting these requirements can be time-consuming and costly, and failure to comply can result in significant penalties and reputational damage.
To deploy AI at scale, banks require a team of experts in data science, machine learning, and AI. However, the demand for AI talent far outweighs the available supply, making it challenging for banks to attract and retain skilled professionals in this field. Additionally, it can be difficult to find individuals who have both the technical expertise and domain knowledge required for banking-specific applications of AI.
The adoption of AI requires a significant cultural shift within an organization, particularly in traditional banking institutions that have been operating in a certain way for many years. Employees may be resistant to change or may lack the necessary skills to work with AI systems. Additionally, legacy systems can pose a challenge, as they may not be designed to integrate with newer technologies.
AI deployment can raise ethical and social considerations such as fairness, accountability, transparency, and bias. Banks must ensure that their AI models are unbiased, transparent, and accountable to avoid negative consequences such as discrimination or reputational damage.
Customers now expect banks to be present throughout their end-use journeys and to provide a frictionless experience. Banking activities such as payments and lending are becoming invisible as journeys often begin and end on interfaces outside of the bank’s platforms. To create value propositions that go beyond the core banking product and provide comprehensive solutions that address customer needs, banks will need to rethink their engagement with customers and make several key changes.
First, banks will need to create integrated propositions that target “jobs to be done” and move beyond standardized products. This requires embedding personalization decisions in core customer journeys and designing value propositions that include intelligence that automates decisions and activities on behalf of the customer.
The second shift is to embed customer journeys seamlessly in partner ecosystems and platforms, taking advantage of partners’ data and channel platforms to increase higher engagement and usage.
Third, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction. This involves allowing customers to move seamlessly across multiple modes and retaining the latest context of interaction.
To achieve this, banks will need a clear strategy for engaging customers through non-bank partners’ channels. They will need to adopt a design-thinking approach, engineering engagement interfaces for flexibility, reengineering back-end processes, and ensuring granular data-capture funnels are embedded in the bank’s engagement layer. All of these efforts aim to provide a detailed understanding of customer journeys and enable continuous improvement.
The bank needs to develop an AI-powered decision-making layer to deliver personalized messages and decisions to millions of users and thousands of employees in (near) real-time across all engagement channels. AI techniques can either replace or augment human judgment to produce better outcomes, enhanced customer experiences, actionable insights for employees, and stronger risk management.
To establish a robust AI-powered decision layer, banks should shift to an enterprise-wide roadmap for deploying advanced analytics/machine-learning models across entire business domains. This includes making the development process repeatable and capable of delivering solutions effectively and on time, a collaboration between business teams and analytics talent, robust tools for model development, efficient processes, and diffusion of knowledge across teams.
The roadmap should also include plans to embed AI in business-as-usual processes, which requires rewiring business processes, making AI decisioning “explainable” to end-users, and a change-management plan to address employee mindset shifts and skills gaps.
To augment homegrown AI models, banks need to integrate emerging capabilities from specialist providers and promote continuous experimentation with these technologies. Banks also need to establish enterprise-wide digital marketing machinery to translate decisions and insights generated in the decision-making layer into a set of coordinated interventions delivered through the engagement layer. This machinery should include data-ingestion pipelines, data platforms, and campaign platforms that track past actions and coordinate forward-looking interventions across all channels.
For organizations to effectively deploy AI capabilities, it is crucial to have a strong, scalable, and adaptable core-technology backbone. Without the necessary investments for modernization, a weak core technology can significantly reduce the efficiency of the decision-making and engagement layers. The core-technology-and-data layer should consist of six key components.
To ensure success, banks need to have a tech-forward strategy that aligns with the business strategy and determines which elements, skill sets, and talent to keep in-house versus those to source through partnerships or vendor relationships. Additionally, the tech strategy should define how each component of the target architecture supports the bank’s vision of becoming an AI-first institution and interacts with each layer of the capability stack.
Data management is critical in an AI-enabled world. The bank’s data management must ensure data liquidity, which means having the ability to access, ingest, and manipulate data that serve as the foundation for insights and decisions generated in the decision-making layer. The data value chain starts with seamless data sourcing from internal systems and external platforms. The data should then be cleaned, labeled, and made available for immediate analysis, with additional data cleaned and labeled for future analysis. As banks design their centralized data-management infrastructure, they should develop controls and monitoring tools to ensure data security, privacy, and regulatory compliance.
A modern API architecture is also essential. APIs enable controlled access to services, products, and data within and beyond the bank. They reduce the need for silos, increase technology asset reusability, and promote flexibility in the technology architecture. While APIs can unlock significant value, centralized governance is necessary to support their development and curation.
Finally, intelligent infrastructure is vital for an AI-first strategy. Cloud-based platforms offer higher scalability and resilience and reduce IT maintenance costs. They also enable self-serve models for development teams, which allows for rapid innovation cycles by providing managed services.
In order to become an AI-first bank, a new operating model is necessary to achieve the agility and speed required to generate value across all layers. However, many banks are still operating in functional silos with suboptimal collaboration models, hindering their ability to transition to more flexible technology platforms.
The platform operating model is a solution to this problem, as it involves cross-functional teams that are organized into a series of platforms within the bank. Each platform team controls its own assets, budgets, key performance indicators, and talent and delivers a family of products or services either to end customers or to other platforms within the bank. By breaking down silos and increasing the alignment of goals and priorities across the enterprise, banks can improve agility and speed.
To become an AI-first bank, it is necessary to transform capabilities across all four layers of the capability stack. Banks must evaluate how their strategic goals can be enabled by AI technologies and conduct a comprehensive diagnostic to identify areas that require investment and talent. With these insights, banks can create a transformation roadmap that spans business, technology, and analytics teams.
To ensure sustainable change, a two-track approach is recommended that balances short-term projects that deliver business value with an iterative build of long-term institutional capabilities. Additionally, banks can acquire non-differentiating capabilities from technology vendors and partners, including AI specialists, rather than building all capabilities themselves.
As AI technology adoption becomes a strategic imperative for banks, envisioning and building capabilities holistically across the four layers will be critical to success.
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