Agentic AI in Banking: Who Wins the Customer’s Trust When Decisions Are Delegated to AI?
Introduction
Will a customer in future trust their personal adviser more, or an AI agent that acts on their behalf? This question sounds hypothetical at first. It is not. Mastercard is already integrating agentic payments into its payment infrastructure, Standard Chartered autonomously manages millions of customer interactions across 59 markets, and customers are increasingly getting used to no longer searching, comparing and deciding for themselves, but instead formulating a goal and leaving its execution to software that acts independently.
For banks, this is more than just another wave of technology. Agentic AI is shifting who holds the customer interface in future, how trust is formed, and how business models in the financial services sector sustain themselves. This article first sets out what distinguishes Agentic AI from classic generative AI, shows using the example of Standard Chartered how it is already being used today, and then turns in depth to the question that actually matters for the industry: what does this mean for banks?
What Is Agentic AI? A Definition
Unlike conventional AI models, which operate within predefined limits and depend on human intervention, Agentic AI is characterised by autonomy, goal orientation and adaptability. The term “agentic” refers to the agency of these models, in other words their ability to act independently and purposefully.[1]
Agentic AI builds on generative AI techniques and uses large language models to function in dynamic environments. While generative models focus on creating content based on learned patterns, Agentic AI applies these generated outputs to concrete goals. A generative model like ChatGPT produces text, images or code; an agentic AI system uses this content to complete complex tasks autonomously by calling on external tools and systems. Agentic AI can therefore not only name the cheapest time to buy a property, but go on to draw up a financial plan, make purchase recommendations, arrange viewings and handle the purchase itself, because it can communicate with various data sources and systems via APIs.
Five core characteristics distinguish Agentic AI from classic systems: goal orientation, meaning working towards defined goals rather than merely reacting to requests; autonomy, the ability to make decisions and carry out actions without permanent human control; planning capability, breaking down complex tasks into coordinated sub-steps; interaction with people, systems and other agents; and learning capability, adapting behaviour based on feedback and new data.
In principle, agentic systems can be designed as single-agent or multi-agent systems. In a single-agent architecture, one agent takes on perception, decision-making and the execution of actions, whereas multi-agent architectures rely on several specialised agents that divide tasks among themselves and work together in a coordinated way to solve complex problems. The agents can take on different roles here, for example analysis, decision-making or execution tasks.[2]
Set against the opportunities, highly granular personalisation in customer contact, productivity gains through automated end-to-end processes, faster product development and improved risk and resilience management, are real risks: regulatory uncertainty and rising compliance costs, a lack of explainability in decisions, cyber, manipulation and dependency risks, and a possible loss of control at high levels of autonomy. For a regulated industry such as banking, it is precisely this tension between opportunity and risk that lies at the heart of the debate.
Agentic AI in Practice: The Standard Chartered Example
How does this look in practice? Standard Chartered provides an instructive example. The bank operates in 59 markets, with a correspondingly wide range of channels from traditional bank counters through mobile banking to social media channels. Customers expect a personalised, real-time service at the same time as pressure on service costs and quality remains high. It is precisely in this tension, between a better customer experience and lower costs at the same time, that Standard Chartered is turning to Agentic AI.
The solution is based on Sprinklr Unified-CXM with integrated Agentic AI. In concrete terms, this means: autonomous management of customer interactions across all channels, independent prioritisation of requests and their routing, proactive escalation of critical cases, semi-autonomous dialogue as part of conversational AI, and self-learning optimisation of service processes. The AI therefore not only decides where a request is routed, but independently recognises when a case becomes critical and human intervention is needed, and continuously learns from its own interactions.
The impact is considerable: the platform has already handled over a million interactions, with tenfold growth in requests via social media channels such as X/Twitter or Facebook. The stated goal is to handle 70 per cent of customer interactions fully digitally; human advisers are to focus in future only on complex cases that genuinely require human judgement.[3]
What distinguishes this case from a classic chatbot, and makes it a genuine example of Agentic AI, is not merely answering individual queries, but the independent deciding, coordinating and optimising of the entire customer journey: the active management of a service process across channels and over time.
What Does This Mean for Banks Now?
This brings us to the real core of the matter, and it goes well beyond cost savings in customer service. Agentic AI is fundamentally changing how customers interact with financial services in future, and who actually holds the direct customer relationship.
Customers No Longer Act Themselves, They Let Others Act
The most far-reaching change concerns customer behaviour itself. Customers are increasingly delegating decisions to AI agents rather than making them themselves: less manual interaction, more goal definition. The customer no longer formulates “I want a loan of X francs on these terms”, but rather an overarching goal, such as “I want to finance my home as cheaply as possible”, and leaves the detailed work to the AI. At the same time, customers expect the agent to act proactively: it should recognise problems itself and offer solutions before it is even asked.
This shifts the real customer relationship. It is no longer the bank or the adviser who is the first point of contact, but the agent the customer trusts.
The Decisive Question of Trust
This raises the central strategic question banks face today: will I in future trust an Agentic AI more than my bank adviser? And: will I trust my own bank’s Agentic AI more than an external provider? Neither question is an academic thought experiment. They determine who will control the customer interface in future. If the answer is negative, meaning customers do not trust their own bank to build or offer the best agent, other players will fill that gap.
It is also interesting that early studies suggest people feel more comfortable asking an AI certain financial questions that they would not want to discuss with a bank adviser.[4]
Potential Competitors Are Taking Over the Customer Interface
These other players are already positioning themselves. Four groups are particularly relevant:
- BigTechs already control the major AI agent platforms today, and with them, potentially, the customer interface in financial services too.
- AI-native FinTechs are targeting profitable segments of the banking business, without carrying the burden of traditional banking infrastructure.
- Platform providers are increasingly embedding financial functions agentically into existing ecosystems, for example in shopping, travel or mobility platforms that build in payment and financing functions directly.
- DeFi, combined with autonomous AI, is coming into focus over the longer term and fundamentally calls traditional intermediaries into question.
For banks, this means: whoever does not actively answer the question of the customer interface for themselves risks becoming mere infrastructure in the background, while others take over the actual customer relationship.
Agentic AI Is Changing Banks’ Business Model
This shift has an immediate impact on two levels of the business model. With a focus on the customer, it means a shift away from pure product sales towards continuous financial services along the entire customer journey. A platform and ecosystem logic emerges, in which banks no longer just offer products but are embedded in a wider usage experience. Competition is shifting from pure price competition towards competition on intelligence: whoever offers the better, more trustworthy agent wins. Banks must accordingly position themselves actively as a “trusted AI provider”, rather than ceding this role to others without a fight.
With a focus inwards, equally far-reaching consequences arise: growth without linear cost becomes possible, because autonomous agents allow economies of scale that could not be achieved with human labour alone. Value creation is shifting increasingly towards data and intelligence as the real differentiating factor, no longer solely towards the product range or branch network.
Where Agentic AI Takes Hold First: A Hypothesis
A look at the likely sequence of this change is worthwhile. Much suggests that the payments area, for instance in connection with shopping and everyday transactions, will be captured by Agentic AI considerably more strongly and quickly than the investing and financing areas, which have so far been less in focus. The reason is probably simple: convenience is decisive, and in payments the benefit of an autonomous agent is felt most directly. At the same time, it is becoming clear that the agent itself is becoming the main visitor to websites, including banking websites. It is no longer the human who clicks through online banking, but their agent who reads out information, compares terms and, within the scope of the set goal, independently makes decisions.
This has practical consequences: if the actual “visitor” to a bank’s own digital channels is in future an agent rather than the human themselves, banks need to design their digital interfaces accordingly differently, machine-readable, API-first and optimised for automated interaction, rather than primarily for human user guidance.
Conclusion: Four Key Messages for Banks
- Customers will increasingly delegate decisions to AI agents. The direct customer relationship is thus shifting from the adviser to the agent the customer trusts (and to whom they are even more willing to share matters they would not raise with a bank adviser).
- The decisive question is then no longer whether Agentic AI is coming, but who wins the customer’s trust: the bank itself or an external provider.
- BigTechs, AI-native FinTechs, platform providers and, in the longer term, DeFi players stand ready to take over the customer interface if banks do not actively answer this question for themselves.
- Banks that build their own Agentic AI capability have the potential to position themselves credibly as a “trusted AI provider”, and so, alongside productivity gains, can also benefit from service gains and retain their customers, rather than becoming interchangeable infrastructure in the background.
[2] Designing Multi-Agent Intelligence – Microsoft for Developers
[3] Agentic AI in Banking: Use Cases, Impact & Benefits | Sprinklr
[4] AI in Finance report design
