Human-Machine Partnership is not new; the industrial revolution was an outcome of this, and banks have used this effectively in industrializing their processes through automation. However, what is new is that the premise of this partnership is evolving, where machines are assuming cognitive skills and are progressively helping in improving customer engagement, employee competency, communication and collaboration, value co-creation, and continuous learning. This new paradigm change in the partnership promises to alter the realm of banking.
Banking was traditionally a business that was driven by trust and personal relationships; bankers were well connected in the community and often looked upon for financial counsel. This, however, changed over time where personal banking remained no longer personal; business compulsions and technology advances drove a host of changes in the banking environment where the banker became an endpoint in a manufacturing setup that often churned out products and services at scale. The relationships in a factory setup were that of a producer and consumer-driven by supply chain efficiencies and cost considerations. This technology-driven automation helped develop scale, but the relationship became its first casualty; the banker lost touch of his clientele and trust that existed eroded over some time.
In a hyper-competitive market where niche banks and fintech players are evolving new business and operating models, banks will need to re-orient themselves to stay relevant. They will need to augment their factory setup with intelligence to create a “Human-Machine Partnership Model” that will help the banker meaningfully engage with the customer. This model will drive continuous and “in the moment” learning for both, help them bridge the lost advantage of knowing the customer in the true sense, and be able to personalize customer engagement using that knowledge and to position themselves as a trusted advisor. On the one hand, intelligent systems will take over mundane and straightforward decision-making tasks; they will learn continuously and be able to contextualize responses; their natural language processing skills will help them continually converse with the banker and the customer. These intelligent systems will support the creation of an “intelligent banker.” This intelligent banker would be liberated from non-value-added activities at the bank, with their primary focus being effective engagement with customers in sales and advisory roles, regaining the lost trust and generating newer revenue streams for the bank.
Human-Machine Partnership would evolve around a 5C model (illustration below) in helping build the bank of the future.
Competency – Machines through their intelligence will identify skill gaps in the employees mapped to the evolving business requirements and be able to reskill them through a continuous learning mechanism. Employees will also be able to gather new skills through “in moment learning” facilitated by contextual help. Work will be synthesized into smaller tasks that can be fulfilled by next-gen employees who are task-oriented and specialized in performing tasks effectively. The next-gen workforce would have a lot less of career bankers and far many contracts and short term employees, the mantle of knowledge continuity and service would be dependent on the effectiveness of the Human-Machine Partnership model.
We see this already in many neobanks where the employee churn is high and “in moment learning systems” are used for quickly grooming the new hires, these systems are progressively removing the need for large bank induction training academies.
Continuous Learning – Intelligent systems will help in continuous learning, where the application has inbuilt models and algorithms that will help understand the customer continuously through changes in the patterns of customer and transaction behavior. These advanced models help arrive at the lifetime value of the customer for better customer sales and service engagement, predicting attrition of the customer for devising the right retention strategies, micro-segmentation models that will help with the proper target marketing approaches.
Banks have been using learning systems for attrition analytics as a means to plug the revenue leak caused by customer churn. Banks that have targeted strategies to retain customers through customized offers and a closed-loop system absorb learning to improve the attrition model over time.
Communication and Collaboration – The intelligent banker can communicate and collaborate with their stakeholders effectively and is assisted by machine intelligence through advanced natural language processing capabilities. Machines are not just able to understand the natural language but are also able to generate natural language in being able to sustain and conclude a conversation meaningfully. A Human-Machine Partnership can collaborate seamlessly where the advanced digital assistants have the intelligence to understand the points of handoff, with the context of the conversation also being passed on to the human expert for complex issue resolution.
Advanced customer service capabilities adopted by banks use NLP effectively to understand customer requests, prioritize the request based on tone, and be able to automate servicing these requests to drive down costs and improve efficiency.
Customer Engagement – Personalization at scale is only possible through harnessing the right technology. Employees transform to be digital conductors who use multiple digital and analytical capabilities at their disposal in delivering an exceptional customer experience while at the moment of truth. Technologies have been used to transform the branch experience. These include deep insights through customer 360, facial recognition to alert the relationship manager when the customer visits the branch, tablet banking capabilities to liberate the teller from his desk, and virtual reality to blur the digital and physical divide. Banks need to have an omnichannel platform that helps them understand the customer and be able to engage with them across multiple channels of their choice effectively using platforms embedded intelligence. The next-gen employees would not appreciate mundane work but would look forward to tasks that require some specialization and in the area of expertise. This would mean that banks will need to use machines to augment their workforce to free them up for value-added activities.
Banking customers are increasingly using self-service channels and typically interact only when they have a query or a hurdle. They expect the bank to be intelligent enough to have a context to their query and all their background information about them to provide instant solutions without a need for too much explanation. Intelligent systems, through a comprehensive view of the customer and insights, help through this journey handoff seamlessly.
Co-creating Value – Co-creation of value will be a joint activity of machine and human partnership. Machines will help bankers evolve new products and services, simulate experiences, create bundled offerings at scale. These capabilities that are driven by intelligent systems will help bankers be agile to market changes; they will be able to personalize products, services, and experiences at large. Banks will start looking at delivering life layers as offerings as a result of a deep understanding of the customer, where they can create lifestyle bundles to provide optimal cost with savings.
Neobanking models are already creating sustainable ecosystems, where banks will start to become increasingly invisible to the customer but experienced through these ecosystems.
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