How banks can improve customer care now with generative AI
In the fast-evolving landscape of banking, technology is a game-changer, and one phrase dominating discussions is “generative AI” (gen AI). Beyond the traditional focus on interest rates, gen AI has emerged as a pivotal tool with the potential to reshape customer care, enhance productivity, and outpace competitors. In this article, we explore how banks can leverage generative AI to revolutionize customer care, improve services, and navigate potential risks.
Embracing Generative AI: A Banking Evolution
Banks have already begun integrating generative AI into their operations, with notable examples including Morgan Stanley’s use of OpenAI’s GPT-4 for wealth management knowledge and Goldman Sachs and Westpac employing it to assist developers in code writing. As gen AI evolves, numerous use cases are emerging, prompting financial institutions to strategize their entry into this transformative technology.
Areas of Impact: Where Banks Can Utilize Generative AI
Understanding the potential applications of generative AI is crucial for banks aiming to harness its power effectively. Key areas of impact include:
1. Assisting Customer Care Agents
Recent research from Gartner underscores the significance of generative AI in enhancing customer care. While emphasizing the need for human oversight, the study suggests that banks can leverage gen AI to empower customer care agents. This includes creating personalized customer insights, cross-selling products, and offering real-time guidance for complex transactions.
Generative AI proves its worth by synthesizing data dynamically during customer interactions, providing a valuable crib sheet for agents. Moreover, it facilitates product and service innovation by analyzing large volumes of unstructured data, such as contact center calls, to uncover customer problems and preferences.
2. Delivering Thoughtful Investment Recommendations
Generative AI significantly reduces the time and effort required for investment research. By processing qualitative and quantitative information, it can swiftly generate reports for investment advisors. This enables quicker and more thoughtful buy/sell/hold recommendations, especially in the crucial moments before the market opens, ultimately leading to increased customer satisfaction.
3. Reducing ‘Time to Yes’ for Commercial Loans
In the realm of commercial lending, generative AI accelerates processes. It assists relationship managers and credit officers in approving business-to-business loans approximately 60% faster by quickly analyzing relevant financial documents and generating credit memos. This efficiency not only satisfies commercial clients but also fosters increased business for the bank.
4. Accelerating Financial Crime Investigations
Generative AI aids banks in combating financial crimes more effectively. While traditional AI is used to detect suspicious activities, gen AI takes it a step further by automating narrative writing, a task that consumes up to 30% of investigators’ time. This ensures that analysts can focus more on investigation and less on summarizing, enhancing client protection.
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Considerations Before Implementation
Before diving into the realm of generative AI, banks must consider several crucial factors:
1. Value Prioritization
Understanding where generative AI will provide the most value is essential. Banks should identify and prioritize use cases that align with their objectives and customer needs.
2. Technical Architecture
Deploying a cloud-based technical architecture that supports AI at scale is vital. This ensures seamless integration and scalability as banks incorporate generative AI into their operations.
3. Responsible AI Governance
Establishing a robust governance framework for responsible AI usage is critical. This includes embedding analytics across workflows, ensuring strong data management with governance, and developing a scalable operating model.
4. Skill Development
As generative AI becomes integral, banks need to invest in skill development programs for employees. Nurturing new skills and roles ensures that the workforce is well-equipped to leverage the technology effectively.
Managing Risks: A Pragmatic Approach
While generative AI promises transformative benefits, managing associated risks is imperative. Here are key areas to focus on:
1. Combating Financial Crime
Gen AI is a double-edged sword, as criminals are quick to leverage its capabilities for fraudulent activities. Banks must invest in advanced identity validation methods, such as bio-verification and voice identification, to stay ahead of fraudsters.
2. Regulatory Compliance
Regulators are adapting to the rise of generative AI, and banks must keep pace with evolving regulatory landscapes. Addressing issues of transparency, fairness, and consumer protection is crucial to navigate the regulatory complexities.
3. Understanding Inherent Risks
Generative AI comes with inherent risks, including a lack of truth-seeking capabilities and potential biases. While transparency can be enhanced, banks must establish responsible frameworks that incorporate guardrails to mitigate risks arising from pre-trained models.
4. Embracing Change
Despite the challenges, banks are encouraged to embrace generative AI, recognizing its continuous improvement. Executives should identify areas for improvement in customer care, establish responsible governance, stay attuned to regulatory shifts, and prioritize customer-centric efforts.
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Generative AI: A Customer-Centric Future
Generative AI is not just a buzzword; it’s a transformative force that banks can harness to create a customer-centric future. By strategically implementing gen AI in customer care, investment recommendations, loan processes, and crime investigations, banks can enhance efficiency, satisfaction, and innovation. As banks navigate the evolving landscape of technology and regulation, embracing generative AI with a customer-focused mindset will undoubtedly shape the future of banking services.
