Communication is the lifeblood of the banking sector. Effective communication is essential for banks to meet the expectations of their stakeholders, deliver value, and gain competitive advantage.
However, communication in the banking sector faces unprecedented challenges and opportunities in the context of digital transformation and customer expectations.
To overcome these challenges and seize these opportunities, leveraging technology, especially AI (artificial intelligence) to enhance communication with stakeholders, is an effective strategy.
This analysis spans across 4 key areas,
- What to Expect: Challenges and Opportunities
- AI-powered decision-making in modern banking
- Reimagining engagement with stakeholders through technology
- Building the AI-bank capability stack
What to Expect: Challenges and Opportunities
Using technology to enhance communication with various stakeholders involves both challenges and opportunities for banks.
Some primary challenges are,
- System integration: Legacy systems, data silos, and integration issues that hinder the adoption and implementation of new technologies.
- Regulatory compliance: Regulatory constraints, security risks, and compliance requirements that limit the scope and speed of innovation.
- Organizational culture: Cultural resistance, skill gaps, and change management affect employees’ and customers’ readiness and willingness to embrace new technologies.
- Human touch: Balancing the human and digital aspects of communication, ensuring that personal touch and empathy are not lost.
- Customer loyalty: Maintaining customer trust, loyalty, and satisfaction in the face of increased competition and expectations.
In parallel, some key opportunities are,
- Personalised services: Providing personalized, proactive, and predictive services to customers across diverse bank platforms and partner ecosystems using AI technologies.
- Digital collaboration: Improving internal communication and productivity among employees by using digital collaboration tools.
- Trust and transparency: Complying with regulatory requirements and fostering trust and transparency with regulators and partners by using technology.
- Operational efficiency: Reducing operational costs, human errors, and credit risk while increasing customer lifetime value and company brand value by using AI-ML-powered technologies.
- Customer retention: Enhancing customer loyalty and retention by offering rewards, incentives, and gamification using technology.
These challenges and opportunities take place in a unique stakeholder landscape. When simplified some key internal stakeholders are, employees, managers, board members, and shareholders. In parallel, some key external stakeholders are customers, regulators, partners, and even the media.
It is important to remember that based on specific geographic segmentation, all banks face their unique macro and micro environments, that directly and indirectly affect the effectiveness of stakeholder communication.
AI-Powered Decision-Making for the Bank of the Future
One of the key benefits of AI technologies for banks is that they can help them make better and faster decisions across the customer life cycle, from acquisition to retention.
By analyzing data from various sources, such as transactions, interactions, and social media, AI technologies can help banks understand customer needs, preferences, and behaviours.
The segmentation of customers based on their profiles, preferences, and behaviours, allows AI technologies to offer customized products and services to customers.
The most crucial purpose of these initiatives is to optimise customer journeys with the help of relevant information, guidance, and incentives to customers at each touchpoint.
Here are some examples of how banks can use AI to personalize services, increase customer lifetime value, lower operating costs, and reduce credit risk:
- Personalised services: Banks can use AI to offer tailored financial advice, dynamic pricing, and proactive alerts to customers based on their needs and goals. For example, Affiniti, a product suite developed by McKinsey, uses advanced analytics and machine learning to provide personalized recommendations for financial products, such as mortgages, loans, and investments.
- Increase customer lifetime value: Banks can use AI to cross-sell and up-sell products and services based on customer behaviour and life events. For example, Affiniti can identify customers who are likely to need additional products or services, such as insurance, retirement planning, or wealth management, and suggest relevant offers at the right time.
- Lower operating costs: Banks can use AI to automate repetitive tasks, streamline processes, and enhance efficiency and quality. For example, Affiniti can automate customer service requests, such as account opening, balance inquiry, and password reset, by using natural language processing and chatbots.
- Reduce credit risk: Banks can use AI to predict customer default probability, optimize credit scoring models, and manage collections strategies. For example, Affiniti can use machine learning algorithms to analyze customer data and behaviour patterns to assess their creditworthiness and risk profile.
To reap the full benefits of AI-powered decision-making, banks need to design and build a highly flexible and fully automated AI-bank capability stack that comprises four layers: engagement AI-powered decision-making core technology and data infrastructure and a platform-based operating model.
In the next section, we’ll dive deeper into this topic.
Reimagining engagement with stakeholders through technology
Another key benefit of AI technologies for banks is that they can enable them to provide superior customer experiences across diverse bank platforms and partner ecosystems.
In order to provide seamless omnichannel experiences for customers, banks can integrate data and services across multiple channels such as mobile apps, web portals, voice assistants, and chatbots using artificial intelligence technologies.
AI technologies can also help banks enhance customer loyalty and satisfaction by providing personalized feedback rewards and recognition based on their interactions and achievements.
Here are some examples of how banks can use AI to reimagine their engagement with stakeholders through technology:
- Create seamless omnichannel experiences: Banks can use AI to provide consistent and convenient services to customers across different channels and devices. For example, modern technologies can integrate data from various sources, such as bank accounts, credit cards, and loyalty programs, to provide a unified view of customer finances and activities. You can enable customers to access bank services through voice assistants, such as Alexa or Google Assistant, by using natural language understanding and speech recognition.
- Enhance customer loyalty and satisfaction: Banks can use AI to provide personalized feedback, rewards, and recognition to customers based on their interactions and achievements. The use of gamification techniques to motivate customers to achieve their financial goals by providing them with badges, points, and levels is one strategy. Banks also can use sentiment analysis to measure customer satisfaction and emotions by analyzing their feedback and reviews.
- Expand reach and offerings: Banks can use AI to collaborate with partners from different industries and ecosystems to provide more value and convenience to customers. Modern AI can enable customers to access partner services, such as e-commerce, telecom, and health care, through the bank platform by using APIs and smart contracts. Customers can also be enabled to pay for partner services using bank products, such as credit cards or digital wallets.
In addition to enhancing customer experiences, AI technologies can also help banks improve their internal communication and productivity among employees.
Banks can foster a culture of innovation and learning among their employees by utilizing digital collaboration tools such as cloud-based platforms, video conferencing, and instant messaging
- Enabling knowledge-sharing
- Peer feedback
And continuous improvement.
Digital collaboration tools can also help banks empower employees by providing them with access to information, tools, and resources they need to perform their tasks effectively. Through clear, consistent, and transparent communication, digital collaboration tools can also align employees with banks’ vision, values, and goals.
Furthermore, AI technologies can also help banks comply with regulatory requirements and foster trust and transparency with regulators and partners.
By using technology, banks can automate compliance processes, such as reporting, auditing, and monitoring. Technology can also help banks enhance data security and privacy by implementing encryption, authentication, and authorization. Technology can also help banks demonstrate accountability and responsibility by disclosing their data sources, methods, and outcomes.
To achieve these benefits, banks need to design and build a highly flexible and fully automated AI-bank capability stack that comprises four layers: engagement, AI-powered decision-making, core technology and data infrastructure, and a platform-based operating model.
The next section will discuss this in more detail.
Building the AI-bank capability stack
To leverage technology for better stakeholder communication for modern banking, banks need to design and build a highly flexible and fully automated AI-bank capability stack that comprises four layers: engagement AI-powered decision-making core technology and data infrastructure and a platform-based operating model.
The engagement layer is the interface between the bank and its stakeholders providing personalized and seamless experiences across channels and partners. This layer includes the applications and platforms that enable customers employees regulators and partners to interact with the bank such as mobile apps web portals voice assistants chatbots cloud-based platforms video conferencing and instant messaging.
The AI-powered decision-making layer is the engine that drives the bank’s value creation providing insights recommendations and actions based on data analysis and prediction. This layer includes the tools and techniques that enable machines to perform tasks that require human intelligence such as learning reasoning and decision making. This layer also includes the products and services that are powered by AI technologies such as Affiniti.
The core technology and data infrastructure layer is the foundation that supports the bank’s operations providing data integration storage processing and security. This layer includes the hardware software networks databases and cloud services that enable the bank to collect store manage analyze and protect data from various sources.
The platform-based operating model layer is the framework that enables the bank’s agility scalability and innovation providing modular services flexible processes and open APIs. This layer includes the principles practices and standards that govern how the bank organizes delivers and evolves its products services and capabilities.
To design and build a highly flexible and fully automated AI-bank capability stack, banks need to follow some best practices, such as:
- Define their vision and strategy: Banks need to have a clear vision of what they want to achieve with AI technologies and how they align with their business goals and customer needs. Banks also need to have a strategy of how they will implement AI technologies in their organization, such as what use cases they will prioritize, what resources they will allocate, and what metrics they will measure.
- Assess their current capabilities and gaps: Banks need to have a realistic assessment of their current capabilities and gaps in terms of technology, data, skills, and culture. Banks need to identify what they have, what they need, and what they can leverage from external partners and platforms.
- Prioritize their use cases and initiatives: Banks need to have a systematic approach to prioritize their use cases and initiatives based on their potential impact, feasibility, and alignment with their vision and strategy. Banks need to focus on solving real problems for their stakeholders and delivering tangible value.
- Design their architecture and governance: Banks need to have robust architecture and governance for their AI-bank capability stack, ensuring that it is scalable, secure, compliant, and transparent. Banks need to define the roles, responsibilities, and processes for each layer of the stack, as well as the standards, policies, and guidelines for data quality, ethics, and accountability.
Technology can help banks improve their communication with their stakeholders by providing data integration, automation, and personalization. Technology can also help banks create new communication opportunities by providing insights, recommendations, and actions based on data analysis and prediction.
To leverage technology for better stakeholder communication in modern banking, adopting AI technologies as the foundation for new value propositions and distinctive customer experiences is critical. By adhering to the following frameworks explained in this analysis, modern banks can benefit from high-value stakeholder communication that positively affects the bottom line.