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How AI Transforms Customer Experience

Anantha Sharma

Head of AI Architecture & Strategy ,

Artificial Intelligence

Remember the days when you had to endure long waiting times on the phone, listening to repetitive hold music, just to get a simple query answered by customer service? Those days are rapidly becoming a thing of the past. Today, we're witnessing an unprecedented shift in the customer service landscape, largely driven by Artificial Intelligence (AI) — particularly through Large Language Models (LLMs) like ChatGPT. Contrary to popular belief, though, these intelligent systems aren’t only limited to answering queries instantly via chatbots, their impact extends far beyond. Various industries, including finance, are leveraging AI to transform their customer experience, and this is evident across three key areas. Let's delve into how this is happening.

AI elevates customer-centricity in business operations.

When it comes to customer service, the integration of AI has significantly enhanced the levels of interaction and engagement. Automated customer support embodies a new standard of reliability and precision, adeptly handling a wide range of customer inquiries and flagging when further human attention is required. In fact, in 2025, more than 95% of global customer service inquiries are expected to be supported by AI, leading to faster, more consistent, and accurate responses.

Also, on the customer-centricity front, business should hinge on personalization. One insightful and nuanced aspect of this is understanding customer preferences and histories. Before AI, this was a long, manual, error-prone process, when possible. However, the advent of new technologies facilitates tailored analyses. For instance, in financial guidance, businesses are now able to offer bespoke recommendations for savings plans, investment strategies, and retirement planning.

Working hand-on-hand with this are predictive analytics, which leverage data to anticipate market trends, customer behavior, and economic shifts. The possibility to foresight enables businesses to make proactive decisions, aligning their strategies with potential future scenarios and ensuring a more robust and forward-thinking customer service approach.

Integrating AI also elevates operational focus and efficiency in business processes.

For instance, routine tasks such as document processing, compliance checks, and transaction monitoring can be automated with LLMs, thus reducing the likelihood of errors and improving the overall quality of operations.

Moreover, data, as we all know, is the lifeblood of modern businesses — and AI-powered models are instrumental in harnessing its full potential. They can analyze vast datasets swiftly, identify patterns, and derive valuable insights that would be time-consuming and challenging to obtain manually. The result is a better understanding of operations, market trends, customer behavior, and more, thereby aiding strategic decision-making.

In addition, AI has unprecedented potential in the product management field.

With all this data AI systems can collect and analyze, the possibilities of what can be done and what boundaries can be pushed are more expansive and limitless than ever before. In recent years, we’ve seen financial institutions leverage the capabilities of LLMs to create highly tailored financial products and services. These AI-driven offerings range from personalized investment strategies and financial advice to custom risk assessments and loan offerings, ensuring that every customer interaction is optimally aligned with individual needs and preferences. And what does that translate into? Increased satisfaction, loyalty, and an evident competitive advantage.

Furthermore, a much less talked about benefit — yet equally significant and particularly beneficial for customers among underserved populations — is the newfound ability to offer financial education through chatbots, which can provide easily digestible information on complex financial topics.

AI is powerful, yet its use calls for responsibility and thoughtful management.

While LLMs can significantly enhance the customer experience across a variety of financial institutions, there are several considerations to be aware of as they are implemented and thoughtfully managed:

First, data privacy and security are paramount. Given that LLMs process large amounts of financial data, it’s essential to ensure the confidentiality and security of this information. Financial entities must address concerns about potential data misuse and ensure compliance with data protection regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

Secondly, bias and fairness are significant considerations. There’s a risk that LLMs may inherit biases from their training data, which could lead to the unfair treatment of certain customer segments. Financial institutions must, therefore, implement rigorous measures to detect and mitigate any such biases.

Thirdly, despite the efficiency and capabilities of LLMs, the human touch remains crucial. Particularly in sensitive situations, the empathy and understanding that human advisors bring to the table can’t be fully replicated by any AI technology. Therefore, while LLMs can greatly enhance the customer experience, they should ideally complement, rather than replace, human interactions. So, use it, leverage it, harness its power to accelerate innovation and progress, but never forget — technology like AI may facilitate, but it is people who truly connect.

The Author

Rachel Anderson, Digital Lead at Synechron UK
Anantha Sharma

Head of AI Architecture & Strategy

Anantha Sharma is the Head of AI Architecture & Strategy. In this role he combines domain knowledge, mathematical skills, and expert architectural & programming skills to extract robust, repeatable, and meaningful insights from data.

To find out more, email him at:

Synechron’s Artificial Intelligence practice provides innovative and transformative AI solutions to help grow businesses. We provide an array of related services for: Generative AI (GenAI), AI Strategy and Architecture, AI Research and Development and AI Ethics, Safety and Security.

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