Cintia Scovine Barcelos is CTO of Bradesco, responsible for Infrastructure, Cloud, Operations, Cybersecurity, Architecture, Data and AI.
Companies such as Amazon, Netflix, Spotify and Uber have permanently reshaped what customers expect from digital experiences: journeys that are fluid, simple, highly contextual and increasingly aware of each person’s needs and timing. What once felt exceptional has become the baseline. Today, customers bring those same expectations to every interaction, including financial services.
We have moved from personalization based on basic, static data and broad customer segments to hyperpersonalization: the use of real-time signals, behavioral patterns, transactional data, life events, channel preferences and contextual information to treat each individual as a unique customer, with interactions, recommendations and communications tailored to their specific moment.
The rise of generative AI and intelligent agents has significantly expanded the ability to understand context, interpret intent, anticipate needs and support customers proactively throughout their financial journeys. This is what makes the current moment so relevant: Technology has finally made personalization at scale not only possible, but practical.
This shift also creates measurable business value. According to McKinsey’s Global Banking Annual Review 2026, the industry is entering a phase in which AI is accelerating precision strategies and reshaping customer ownership. In this environment, the ability for institutions to combine speed, relevance and responsibility has become critical.
The Need For Responsible Hyperpersonalization
Responsible hyperpersonalization emerges from the need to balance what technology can do with how it should be used. The objective is no longer to maximize short-term conversion, but to create lasting value for customers, institutions and society. Ethical governance is increasingly linked to business performance: Research from the IBM Institute for Business Value shows that organizations investing more deeply in AI ethics achieve stronger outcomes from their AI initiatives.
A central pillar is zero-party data: information customers intentionally and proactively choose to share because they expect a clear benefit in return. The ethical principles behind this approach are transparency, consent, fairness, equity and privacy. Trust has become the decisive condition for sustainable progress. As consumer research continues to show, people value personalization, but they also want clarity, security and meaningful control over how their data is used.
Responsible hyperpersonalization requires strong institutional accountability and governance. Organizations need clear frameworks that define roles, responsibilities, oversight mechanisms and continuous monitoring of AI systems. This includes regularly assessing model performance, identifying unintended consequences, reviewing automated decisions and ensuring human intervention whenever the impact on the customer requires additional scrutiny.
Customers must also have meaningful control over their experiences. They should be able to understand, adjust or limit the degree of personalization they receive. When people retain control over their data and digital interactions, trust becomes an active choice rather than merely a by-product of regulatory compliance.
Another critical pillar is bias mitigation. Bias can emerge not only from the data used to train AI models but also from the objectives and optimization criteria embedded in those models. Addressing these risks requires continuous monitoring, fairness testing, diverse perspectives in model development and human oversight in high-impact decisions. AI should be a force for inclusion and access, not a mechanism that amplifies existing inequalities.
Finding the right balance between personalization and privacy is therefore essential. Fortunately, these goals are not mutually exclusive. By adopting privacy by design, organizations can embed data protection into products and services from the start, turning regulatory compliance from an obligation into a competitive advantage.
Responsible Hyperpersonalization In Financial Services
One of the most common misconceptions is to associate hyperpersonalization simply with selling more products. When implemented responsibly, its real purpose is to reduce friction and help customers make better decisions. It may mean suggesting an alternative payment plan to avoid higher interest costs, reminding a customer of a bill before it becomes overdue or recommending an investment option aligned with their risk profile and long-term goals.
The opposite path is also possible, and significantly more dangerous. If AI systems are designed only to stimulate transactions, they may reinforce harmful incentives, such as encouraging excessive use of overdraft facilities, extending credit to customers who are already over-indebted or prioritizing products that generate higher returns for the institution rather than better outcomes for the customer. In these cases, personalization stops being a tool for empowerment and becomes a mechanism of manipulation.
To sustain trust, organizations must continuously ask a few fundamental questions:
• Is this interaction genuinely relevant to the customer?
• Is it transparent and secure?
• Does it respect individual autonomy?
• Does it contribute to better financial outcomes?
The answers to these questions ultimately determine whether personalization strengthens or weakens the customer relationship.
Trust is the enabling asset of hyperpersonalization. Without it, even the most accurate recommendation may feel intrusive. In financial services, where decisions often involve credit, investments, savings and personal security, the stakes are even higher. As financial institutions scale AI and adopt increasingly autonomous systems, trust becomes a prerequisite for both value creation and risk management.
What Comes Next
Looking ahead, I believe that over the next five years, hyperpersonalization will evolve far beyond isolated recommendations. Customers will spend less time navigating menus and more time expressing goals, supported by AI agents capable of executing complex tasks within clearly defined boundaries. This transformation will require a new level of governance maturity, including stronger controls over identity, permissions, scope of authority, traceability and accountability in agentic AI systems.
As organizations across sectors accelerate the use of AI and data to personalize experiences, the nature of governance is fundamentally changing. In the agentic era, intelligent systems may increasingly recommend, decide and act on behalf of users or institutions. That makes accountability, traceability, supervision and clearly defined guardrails indispensable for scale. The true competitive advantage will not come from personalization alone, but from the ability to deliver personalization with integrity, compliance, security, transparency and trust.
Ultimately, responsible hyperpersonalization means ensuring that technology remains in service of people. In the digital economy, products can be replicated, technologies can be acquired and business models can be imitated. Trust is different. Trust is earned over time, reinforced in every interaction and increasingly becoming the most valuable competitive asset of the AI era.
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