April Miller is Managing Editor a ReHack Magazine.
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Neobanks are digital-first, tech-driven financial institutions built around apps, APIs and automated decisioning, rather than branches and batch processing. They are reshaping everyday credit and debit card habits, from how quickly a card can be issued to how granularly spending can be controlled. As artificial intelligence (AI) matures inside modern banking stacks, cards are becoming programmable tools for security, budgeting and cash-flow management.
The Tech Foundation With AI and Automation
Neobanks run on cloud-native infrastructure built for continuous data ingestion and fast iteration. That architecture enables scoring transactions as they occur and automates back-office workflows. Legacy banks can add these capabilities, but many still struggle with fragmented cores, slower release cycles and risk models designed for delayed reconciliation.
AI investment signals where the industry is heading. Market forecasts expect AI in banking to grow from its 2020 baseline to more than $64 billion by 2030, reflecting how quickly automation is becoming central to product design.
Adoption varies widely across banks, and that gap can determine security and competition. Institutions that move faster can detect fraud sooner and roll out stronger card controls, while slower adopters risk falling behind on protection and customer experience.
According to a study by IBM, only 8% of banks developed generative AI systematically in 2024, while 78% pursued it through tactical initiatives. It linked deeper AI integration to fewer service outages and higher IT customer satisfaction. Neobanks often see these gains sooner because their systems support faster model updates and automated responses.
A New Standard for Consumer Cards
Consumer card behavior is shifting toward institutions that feel more like security-forward software products than traditional accounts. Trust is part of this shift — 54% of global consumers trust at least one large technology company more than banks. This is a sign that experience and perceived competence influence where people feel safer managing money and identity data.
Radically Improved User Experience
Neobank cards are managed like configurable endpoints, with real-time purchase notifications reducing the “unknown transaction” window that attackers depend on. Spending analytics also run in near real time, helping cardholders recognize subscription creep, merchant anomalies and unusual geographies before they become chargebacks.
Card life cycle actions also happen from inside the app. Freezing and unfreezing accounts, setting travel rules, changing PINs and provisioning a card to a mobile wallet can be handled after a few authenticated actions. The key detail is latency reduction. Faster visibility and response compress the blast radius of both fraud and account takeover.
Advanced Security and Control
Neobanks typically apply AI-assisted risk scoring across device signals, transaction contexts and behavior patterns. These include device binding and anomaly detection.
Some offer controls that support threat modeling for online card fraud. Virtual cards can limit the usefulness of stolen card details by reducing reuse. Merchant or category limits and location-aware prompts can also block unexpected spending or trigger extra verification when an activity deviates from normal patterns.
While these do not eliminate fraud, they convert security from a hidden back-end function into an active control surface where the user can participate in containment.
Revolutionizing Commercial Card Usage
For small and medium-sized enterprises, neobanks position cards as operating infrastructure. Traditional business banking often treats cards, lending and treasury as separate products with different onboarding flows. Neobanks unify these capabilities in a single interface with role-based access, programmable controls and integrations that fit modern finance teams.
The result is tighter financial control without adding administrative load. Businesses can connect banking to accounting systems, payroll platforms and payment processors, then use those connections to automate policy enforcement. Better data lineage and faster categorization thereby reduce the blind spots where fraud and compliance failures thrive.
AI-Powered Underwriting and Credit
Neobanks use automation to evaluate cash flow data, invoices, payment histories and account activity to adjust limits or extend credit faster than manual review cycles. End-to-end automation also improves risk management across the lending life cycle by analyzing large volumes of financial statements, histories and market signals to arrive at informed credit decisions and reduce exposure to losses.
Automation changes how businesses use cards daily. Faster underwriting means a company can access credit sooner, then continue using it without the constant stop-start that happens when assessments drag on. Ongoing monitoring also keeps things moving. If a transaction looks risky, the system can step in right away by reducing a limit, launching a quick verification or flagging a vendor.
Streamlined Expense Management
Rather than passing around one corporate card, finance teams can give each employee, project or vendor its own card and set specific rules. A contractor can get a card that works only for a week. A project card can be limited to certain merchants. A high-risk category can be blocked outright. Receipts can also flow in automatically, so expenses get matched and coded sooner.
From a cybersecurity standpoint, segmentation reduces the value of any single compromised credential. Virtual cards can be rotated frequently, employee access can be revoked instantly and anomalous expense patterns can trigger finance and security.
What This Means for Traditional Banking
Incumbent banks are responding to neobanks, partly because customers now seek instant alerts, self-service freezes and app-native dispute flows as baseline features. Regulators are also paying attention to how AI changes risk and resilience, especially when models depend on third-party providers or introduce new attack surfaces.
The U.S. Federal Reserve has even emphasized the need to balance innovation with safety, soundness and evolving risk management practices as AI adoption expands. Supervisors in Europe have also described banks using AI for credit scoring and fraud detection as adoption becomes more mainstream.
Next Steps for Safer and Smarter Card Use
Cards now act like smart controls for identity, risk and cash flow. Neobanks pushed that shift by using AI and automation to speed up processes for a range of financial services. As these systems improve, credit and debit use will adapt in real time, staying more secure and fitting more naturally into daily spending and business operations.
