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The customer relationship in banking and insurance is evolving quickly. Digital usage is increasing, contact volumes are rising, and customers now compare their experience to platforms that provide instant answers and seamless interactions.
Personalization is no longer just a marketing argument; it has become a real driver of trust. Banks and insurance companies must understand customer needs, deliver accurate information, and offer a frictionless journey across every channel. AI now makes this level of expectation achievable without adding complexity to internal processes.
Expectations have shifted under the influence of digital giants. Customers want to start a conversation on one channel, continue on another, and be understood immediately. They expect answers tailored to their context, without having to repeat themselves. They want their bank or insurer to understand their situation, their needs, and their history.
This expectation goes far beyond convenience. It has a direct impact on loyalty. When a customer experiences a smooth and personalized journey, they stay. Conversely, a generic experience creates frustration and increases attrition. Industry data is clear: a customer satisfied with a contextualized experience generates more long-term value. Personalization has therefore become a key lever that directly influences performance.
For a long time, “personalization” meant pre-filled forms or recalling a few basic details. Today, AI-powered conversational agents or chatbots enable a much deeper understanding of what the customer is actually expressing. They identify not only the reason for the request, but also the nuances of the context, the data already provided, and the implicit signals that guide how the interaction should be handled.
AI can then propose a tailored omnichannel journey, qualify the request progressively and logically, clarify uncertainties, and ensure full coherence across every step. In a banking or insurance customer service context, this means eliminating redundancies, instantly recognizing a sensitive request, or prioritizing a potentially critical situation.
The role of AI is not to replace advisors. It prepares the ground, clarifies the situation, reduces friction, and allows customer service advisors to step in at the right moment, with reliable and complete context.
This combination is what improves both the quality of the response and the overall fluidity of the journey.
Banks and insurance companies receive a large number of recurring requests every day: information about an account or card, a need for a certificate, follow-up on a file, issues accessing the customer space, questions about coverage or options… These requests may seem simple, but they represent a significant share of the volume handled by contact centers. Their particularity is that they come back continuously, often in dozens of different formulations, which makes them harder to process when relying solely on static decision trees or traditional FAQs.
From an operational standpoint, these are the requests that consume the most time and generate the most difficult workload fluctuations. A surge in access-code reset requests, a wave of card-replacement questions, or a spike linked to a tax document can overwhelm a customer service team within hours. A conversational AI agent helps absorb these fluctuations by instantly identifying the intent, even when poorly phrased or incomplete, and by guiding the customer to the right answer instead of forcing them to navigate multiple steps on their own.
AI also brings a level of consistency that is not always easy to maintain internally. Where two advisors might give different answers depending on their experience or interpretation, AI ensures a shared, structured, and up-to-date knowledge base.
This reduces processing times, prevents ticket reopenings, and significantly improves the customer experience. In practice, customers receive faster, clearer, and process-aligned answers, while teams can focus on truly complex or sensitive situations.
This distribution of work streamlines the entire service and strengthens the quality perceived by customers.
The main challenge is not the response itself, but the continuity of the journey. A customer may start a request on the website, continue on mobile, and then call. In many banks and insurance companies, these channels still operate in silos. The result: personalization disappears, the customer repeats the same information several times, and the perceived quality drops.
In banking and insurance, much of the experience depends on how context flows from one channel to another. It’s not just a technical matter, it’s a matter of trust. A customer who must restate their issue after waiting 20 minutes does not feel supported. A customer whose request is picked up immediately with the right information feels the opposite: known, recognized, and understood.
AI creates a unified thread from the very first interaction. It identifies the real intent, even if the customer uses vague wording, and structures the essential information for the rest of the journey. This means that whether the customer stays in self-service or is routed to an advisor, the full context is already prepared: intent, recent history, steps already completed, key information.
In practice, this prevents customers from having to restart their request and enables advisors to begin the conversation with enough information to be immediately effective and relevant. The channel no longer determines the quality of the experience: it remains consistent everywhere. Personalization becomes independent of the tool and relies instead on a true understanding of the customer’s situation, reinforcing both fluidity and trust in the service.
For an AI approach to truly deliver on its promises, it must go beyond technical implementation. In banking and insurance, success relies first on the ability to understand how customers express their requests, how teams actually handle them day-to-day, and where friction points appear. Without this field-level understanding, even a sophisticated solution will remain under-used.
A key requirement is having an intent database that accurately reflects real customer-service situations. Not a theoretical map, but a reference built on the customer’s own language, their most common reasons for contact, and the scenarios that generate the most confusion. This precision is what enables AI to correctly identify requests, even when they come in unpredictable formulations.
Another essential aspect is the sequence of steps. An AI agent or chatbot may understand the intent perfectly, but if the journeys behind it are poorly structured, personalization will fail. Orchestration must act as the guiding thread: which information should be collected first, when to offer self-service, when to route to an advisor, and how to transmit context seamlessly.
Banking and insurance come with high expectations regarding security, confidentiality, and compliance. Effective personalization therefore depends on a controlled and trustworthy AI, capable of operating in an environment where GDPR compliance, encrypted exchanges, secure access, and full traceability are non-negotiable.
In a context where sensitive data circulates between several systems, internal validation mechanisms such as LLM as a Judge become essential to ensure coherence, compliance, and the reliability of generated responses.
AI must leverage internal tools to deliver responses that are contextualized, coherent, and reliable. This requires smooth integration with the CRM, which centralizes history and key information, as well as with CCaaS platforms used by customer-service teams.
The goal is not to multiply connections but to align AI with these core systems so that every interaction, automated or human, fits within the same operational framework and relies on the same source of truth.
Finally, sustainable personalization requires ongoing monitoring: analyzing new intents, spotting misunderstandings, adjusting journeys, refining certain formulations. Customer behavior and expectations evolve quickly. It is this regular, usage-driven refinement that ensures conversational AI agents remain relevant over time.
After focusing on ensuring consistency across channels, more and more banking institutions are moving toward an opti-channel approach. The goal is no longer just to guarantee continuity between touchpoints, but to identify which channel is truly the most relevant for each situation, depending on the intent, urgency, and level of assistance required.
Opti-channel does not aim to multiply touchpoints. On the contrary, it seeks to simplify the customer’s life by directly suggesting the most suitable channel. The challenge lies in using AI to read the situation, understand the context, and naturally guide the customer to the right point of contact.
For banks, this represents a genuine shift in mindset. It helps absorb large volumes more effectively, reduce unnecessary interactions on the most expensive channels, and improve perceived service quality. Customers feel they are being guided intelligently, while teams can focus on situations where their intervention truly adds value.
Customer expectations will continue to rise. Banks and insurance providers capable of delivering a personalized, seamless, and consistent experience will gain a decisive advantage. Those that do not will continue to face the consequences of undifferentiated service: dissatisfaction, customer churn, and loss of value. AI now enables banks and insurers to industrialize personalization while streamlining journeys and strengthening customer trust.
