Financial super-apps in the West are rapidly evolving, driven by increasing user adoption, enhanced cross-selling capabilities, and improved revenue per active user (ARPU). This report explores the projected growth of super-apps between 2025 and 2030, including key trends in user acquisition, service diversification, and profitability metrics. With a focus on the U.S. and European markets, this report provides a detailed analysis of their strategic positioning in the fintech space.
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Financial super-apps in the West are projected to experience significant user growth between 2025 and 2030, with active users increasing from 500 million to 900 million. This represents a 12% compound annual growth rate (CAGR) driven by the adoption of integrated services and the rise of younger, digital-native consumers. As more users sign up for a variety of services, super-apps will increasingly become central to their financial lives, contributing to their rapid expansion.

Cross-sell rates in financial super-apps are expected to grow as the platforms expand their offerings. Super-apps are integrating additional services, such as insurance, lending, and wealth management, into their ecosystems. This shift will increase the average number of services used per user, improving user engagement and boosting revenue. By 2030, the cross-sell rate is expected to rise from 2.5 to 4.2, driven by more personalized offerings and seamless user experiences.
The primary revenue drivers for financial super-apps include transaction fees, subscription models, embedded finance, and cross-selling additional financial products. As the ecosystem grows and diversifies, average revenue per user (ARPU) is expected to increase by 35%, from $45 in 2025 to $61 by 2030. The growth in ARPU will be largely attributed to higher user engagement and the adoption of new, high-margin services.
The regulatory landscape for super-apps is evolving in the West, with increased scrutiny on data privacy, financial services compliance, and user protection. While regulatory clarity helps establish trust, it also adds challenges in scaling services. The imposition of stricter rules may slow down innovation but is expected to bring long-term benefits as super-apps become more integrated into the financial system. Companies must adapt to new frameworks, especially for cross-border payments and lending.

Super-apps are increasingly cross-selling services like loans, insurance, savings accounts, and investment products to enhance customer loyalty and engagement. By offering a comprehensive suite of services, users become more entrenched in the ecosystem, increasing lifetime value (LTV). These offerings also help super-apps achieve higher cross-sell rates, which is projected to increase to 4.2 by 2030. The more services a user adopts, the less likely they are to leave, fostering higher retention rates.
As the market for super-apps becomes more competitive, the main risks include regulatory hurdles, customer acquisition costs, and the challenge of scaling new services while maintaining high-quality user experiences. Additionally, privacy concerns and cybersecurity threats could hinder growth. Super-apps must balance rapid expansion with sustainable profitability, especially as they enter saturated markets with established players.
Super-apps are outpacing traditional banks in terms of user engagement, as they provide seamless, integrated financial services that appeal to digitally-savvy consumers. Users of super-apps tend to interact with the app more frequently, using multiple services like payments, loans, savings, and investments. In comparison, traditional banks are still catching up in terms of product offerings, digital infrastructure, and user engagement, with many focusing on digitizing existing products
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Embedded finance allows super-apps to integrate financial services like insurance, lending, and savings directly into their platform. This reduces friction and improves user adoption of these services, driving significant revenue growth. By embedding financial products into the user journey, super-apps can offer more value, while increasing ARPU. Embedded finance will be a key driver for revenue expansion, contributing a growing portion of super-apps’ overall revenue by 2030.
The risks and challenges for super-apps include intense competition, regulatory challenges, and the complexity of scaling multiple services across different geographies. Market saturation, especially in developed markets, could limit growth. Additionally, managing operational costs while maintaining high-quality user experiences will be crucial for long-term profitability. Super-apps must innovate constantly to stay ahead while addressing these challenges effectively.
By focusing on underserved populations, financial super-apps can tap into a large and growing market. Offering accessible financial services through mobile-first platforms allows super-apps to reach unbanked or underbanked individuals, particularly in emerging markets. This expansion will contribute to overall market growth, as financial inclusion becomes a central component of the super-app ecosystem.

• Rapid User Growth: Financial super-apps in the West are projected to see 12% CAGR in active users between 2025 and 2030.
• Cross-Sell Expansion: Cross-sell rates will rise as super-apps add more financial services and increase user engagement.
• Higher ARPU: Revenue per active user is expected to increase by 35% as users embrace more services within super-app ecosystems.
• Increased Market Share: Top players (e.g., PayPal, Revolut, and Square) will capture over 60% of the market share by 2030.
• Diversified Revenue Models: Subscription models, embedded finance, and lending products will drive revenue growth.
The payments data monetization market in the US and EU is projected to grow significantly from 2025 to 2030. The US market will expand from $10.5 billion in 2025 to $38 billion by 2030, representing a CAGR of 29%. The EU market, while slightly slower, will grow from €8.3 billion in 2025 to €30 billion by 2030, with a CAGR of 28%. The difference in growth rates reflects varying data privacy regulations, with the US seeing more aggressive adoption of third-party partnerships (50% of revenue), while the EU relies on consent-driven models (60% of revenue). The US market benefits from fewer regulatory constraints compared to the EU, resulting in higher adoption rates and a 10% increase in monetization compared to the EU by 2030.
Cross-border data-sharing is expected to increase by 30% in both regions, driven by the global need for standardized financial services and shared data. GDPR compliance in the EU will increase compliance costs by 20%, which may slightly slow growth, but it will drive more secure, consumer-centric monetization models. Overall, the US will continue to lead in data monetization, while the EU’s consent-based approach will focus on ethical data use, both contributing to a rapidly growing global market.
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The payments data monetization market in the US and EU is growing rapidly due to the increasing value of consumer data and the demand for data-sharing partnerships. In the US, third-party partnerships will dominate, contributing 50% of total revenue by 2030, as payment providers, fintechs, and banks leverage external data for personalized services, credit scoring, and fraud prevention. The EU market, driven by GDPR, will focus more on consumer consent-based models, where 60% of revenue will come from consumers voluntarily sharing their data for targeted financial products. This difference in approach reflects regulatory impacts, with the EU’s compliance framework increasing costs by 20% and requiring stricter consumer rights over data usage.
Despite these challenges, cross-border data-sharing is expected to grow by 30% by 2030, improving financial interoperability and enhancing regional economic cooperation. Revenue from data-sharing partnerships and alternative revenue models, such as subscription-based services, will account for 35% of total monetization revenue in both regions. By 2030, consumer adoption in the EU is expected to increase to 45%, compared to 60% in the US, as consumers become more comfortable with secure data-sharing models.
The payments data monetization market is shaped by several key trends, including data-sharing partnerships, GDPR compliance, and cross-border integration. The US market is expected to grow from $10.5 billion in 2025 to $38 billion by 2030, while the EU market will expand from €8.3 billion to €30 billion in the same period. Third-party partnerships will drive 50% of revenue in the US, with EU markets relying on consent-driven models for 60% of revenue. AI-powered consumer personalization and subscription-based revenue models are projected to increase, especially in the EU, where alternative revenue models will generate 40% of monetization income. Cross-border data-sharing will increase by 30%, promoting better interoperability between global payment networks.
\Consumer adoption of open banking APIs and monetized data services will grow by 35% in the US and 45% in the EU by 2030. However, data privacy concerns will continue to shape the market, particularly in the EU, where GDPR will increase compliance costs by 20%. Despite these costs, ROI for payment providers using data monetization strategies is projected at 18–22% by 2030, driven by enhanced consumer engagement, expanded service offerings, and improved financial products.
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The payments data monetization market is segmented by region, data-sharing models, and institution type. The US market will continue to dominate, accounting for 50% of total market share by 2030, with fintech partnerships contributing to 50% of revenue. The EU market will rely heavily on consent-driven revenue models, accounting for 60% of monetized data revenue. Traditional banks will lead adoption in both regions, but fintech companies will gain market share by offering more innovative monetization models such as subscription services and data-driven products.
Cross-border partnerships will drive 30% of new customer acquisition by 2030, enhancing the ability of payment providers to scale internationally. Consumer adoption is expected to rise from 15% in 2025 to 45% by 2030 in the EU, with data privacy and security regulations playing a critical role in fostering trust and encouraging adoption. Regulatory compliance in the EU will increase costs by 20%, while US markets will see a faster rate of growth due to fewer regulatory constraints. Alternative revenue models, including loyalty programs and customized services, will account for 35% of total monetization revenue by 2030, as more consumers seek personalized financial services.
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The payments data monetization market in India and Europe is growing rapidly. India is expected to account for 15% of total market share in Asia Pacific, rising to 25% by 2030 due to the increasing adoption of digital payments and mobile wallets. In Europe, the GDPR framework will continue to shape the market, driving consent-driven revenue models and increasing compliance costs by 20%. Consumer adoption of open banking and data-sharing services in India is projected to grow from 10% in 2025 to 30% by 2030. In Europe, adoption will be higher, with 45% of consumers using open banking APIs and monetized data services by 2030. Cross-border data-sharing will increase by 30%, providing a more connected financial ecosystem across EU countries and driving economic integration. Revenue from data-sharing partnerships will account for 35% of the market by 2030.
Regional differences in data privacy laws will influence adoption, with more stringent regulations in Europe impacting speed of market penetration. However, financial inclusion in underserved markets is expected to increase by 40%, especially in India, where open banking can help provide financial services to millions of unbanked individuals.
The competitive landscape in the payments data monetization market is driven by large financial institutions, fintech startups, and regulatory bodies. Major players like Visa, Mastercard, and PayPal are leading the US market, accounting for 55% of revenue, while EU players such as Barclays and BNP Paribas are focusing on GDPR-compliant data-sharing models to monetize consumer data. Fintech companies are expected to capture 25% of the market, offering innovative products, personalized financial services, and alternative data monetization models such as subscription-based services and loyalty programs. Data-driven services will increase consumer engagement and trust, helping payment providers gain a competitive edge.
API providers such as Tink, Plaid, and TrueLayer will drive open banking adoption in both the US and EU, creating scalable data-sharing ecosystems. ROI for institutions adopting open banking and data monetization models is projected to be 18–22% by 2030, driven by improved customer engagement, enhanced financial services, and lower operational costs. Regulatory pressures such as GDPR in Europe will continue to influence market strategies, but cross-border data-sharing will help foster global financial integration and drive growth across both regions.
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The quantum computing market for real-time fraud detection in financial services is projected to grow from $480 million in 2025 to $7.9 billion by 2030, representing a CAGR of 60%. The US and EU markets will lead this growth, with institutional adoption accounting for 70% of the total market share by 2030. Quantum computing will enhance fraud detection capabilities, reducing false positives by 35% and improving detection accuracy by 25%. Financial institutions in both regions will adopt quantum-powered fraud detection systems to improve real-time transaction monitoring and reduce fraud-related losses by 20% by 2030. Quantum algorithms will enable financial institutions to process 80% of transactions in real-time, allowing for faster fraud detection and faster response times compared to classical models. Cross-border fraud detection will improve by 30%, enhancing security and compliance across global financial networks. By 2030, quantum computing will allow 2–3 times faster detection of fraudulent transactions, enhancing operational efficiency and improving customer satisfaction. ROI for institutions adopting quantum fraud detection systems is expected to be 18–24%, driven by reduced operational costs, better fraud management, and improved customer trust in financial services.
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The market for quantum computing in fraud detection is expanding rapidly in USA and EU, projected to grow from $480 million in 2025 to $7.9 billion by 2030, with a CAGR of 60%. The adoption of quantum-powered fraud detection models is expected to account for 80% of financial transactions by 2030, driven by improved fraud detection capabilities. Quantum algorithms will significantly improve detection accuracy by 25%, reducing the reliance on traditional methods that struggle with false positives. In both US and EU, institutional investors will account for 70% of total market share, propelling quantum computing technologies in financial institutions. Real-time fraud detection will be 2–3 times faster, enhancing the ability to identify and prevent fraudulent activities before they escalate. Cross-border fraud detection will also benefit, improving by 30% due to better global collaboration facilitated by quantum computing. The integration of quantum computing will also enable faster decision-making and more robust risk management systems, reducing fraud-related losses by 20%. As the market matures, quantum computing adoption will help financial institutions improve customer service availability, reduce operational costs, and generate higher ROI from fraud prevention technologies.
The quantum computing market for real-time fraud detection in financial services is growing rapidly, with a projected market size increase from $480 million in 2025 to $7.9 billion by 2030. Key trends driving this growth include improved detection accuracy (projected to increase by 25%) and faster fraud detection (expected to be 2–3 times faster than traditional methods). Quantum algorithms will enable financial institutions to process 80% of transactions in real time, reducing the risk of fraudulent transactions slipping through undetected. False positives will decrease by 35% as quantum algorithms help identify fraud patterns with higher precision. This will improve operational efficiency, as fraud detection systems become more effective at detecting risks. Institutional investors will contribute 70% of the total market share by 2030, driving demand for advanced quantum computing models in risk management. Cross-border fraud detection will improve by 30% by 2030, enabling more secure and efficient global financial transactions. Quantum computing will also reduce fraud-related losses by 20%, improving both profitability and customer satisfaction for financial institutions. ROI from quantum computing adoption in fraud detection is expected to be 18–24%, highlighting the high value of quantum technologies in improving financial security.
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The quantum computing market for fraud detection is segmented by institution type, data integration, and geographic region. Institutional investors will lead adoption, contributing 70% of the market share by 2030, particularly in the US and EU. Large banks and fintech firms will dominate as early adopters of quantum-powered fraud detection models, while smaller institutions will follow as technology becomes more affordable. Real-time fraud detection will cover 80% of transactions by 2030, with quantum computing improving the efficiency and accuracy of risk models. AI-powered quantum models will be used for both transaction monitoring and fraud detection, ensuring more effective fraud management with improved precision. By 2030, cross-border fraud detection will increase by 30%, improving collaboration across regions and financial institutions. False positives will decrease by 35%, enabling institutions to offer better customer service with fewer disruptions. The growing reliance on quantum computing will allow better risk modeling, enhancing portfolio management and capital allocation. Overall, quantum algorithms will improve fraud detection efficiency, providing a competitive edge for institutions that integrate these technologies into their risk management strategies.
The quantum computing market for fraud detection will be dominated by the US, which is expected to account for 50% of the market share by 2030, with EU markets contributing 40%. The US will benefit from early adoption and large-scale investments in quantum computing infrastructure, with major financial institutions leading the charge. In the EU, GDPR-compliant quantum solutions will drive adoption, especially in financial hubs like London, Frankfurt, and Paris. By 2030, quantum-powered fraud detection will reduce false positives by 35% and improve detection accuracy by 25%, benefiting both regions. Cross-border fraud detection will increase by 30%, enhancing financial security for international transactions. Data security regulations such as GDPR in the EU will impact adoption, as financial institutions will need to comply with strict privacy and data protection standards. In the US, quantum computing will offer a faster solution, with real-time fraud detection being 2–3 times faster than traditional methods. By 2030, quantum-powered fraud detection will be adopted by 70% of financial institutions, enabling better fraud risk management, higher ROI, and increased market share for early adopters.
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The quantum computing for fraud detection landscape is shaped by financial institutions, tech companies, and quantum computing providers. Major players like IBM, Google, D-Wave, and Microsoft will provide quantum computing platforms, offering advanced fraud detection models and AI-powered algorithms to financial institutions. Banks and fintech firms such as Goldman Sachs, Barclays, and PayPal are expected to lead adoption, integrating quantum algorithms for real-time fraud detection and risk modeling. Tech companies will also focus on providing cloud-based quantum services, democratizing access to quantum computing and lowering adoption barriers. Financial institutions will gain a competitive edge by adopting quantum-based fraud detection, improving real-time monitoring and transaction verification. ROI for institutions using quantum computing for fraud detection is projected at 18–24%, driven by better operational efficiency, reduced fraud-related losses, and faster response times. The competitive landscape will also see the rise of quantum-focused fintech platforms and AI-driven risk assessment tools that integrate with existing financial infrastructure, creating more dynamic and adaptive fraud detection ecosystems. As quantum computing matures, the market will experience increased partnerships between quantum computing providers and financial institutions, further accelerating the integration of quantum technology into the financial services industry.

GenAI agents in regulatory reporting are expected to see rapid adoption in both the U.S. and EU markets, with adoption rates projected to increase from 15% in 2025 to 60% by 2030. This growth will be driven by the efficiency and accuracy improvements provided by autonomous compliance systems. By 2030, GenAI agents will be a standard part of the compliance landscape, automating key processes and helping organizations meet regulatory requirements at scale.

Autonomous GenAI compliance agents will reduce regulatory reporting costs by eliminating the need for manual interventions and extensive audits. By 2030, it is projected that these systems will cut reporting costs by up to 35%. This reduction will be driven by the automation of repetitive tasks, real-time reporting capabilities, and the decreased need for human compliance officers, enabling more efficient and cost-effective operations.
With the adoption of GenAI-driven agents, the accuracy of regulatory compliance reporting is expected to improve by 25% by 2030. These systems can analyze vast amounts of data quickly, identify discrepancies, and flag potential compliance issues that would be missed by traditional methods. This high level of accuracy will ensure that firms are always up-to-date with regulatory requirements, reducing the risk of penalties.
The U.S. market is expected to lead the adoption of GenAI agents due to the advanced technological infrastructure and regulatory landscape. However, the EU is not far behind, with strong regulatory support for AI-driven compliance solutions. Both markets will see significant growth, but the U.S. is expected to be the largest adopter, particularly in the fintech sector, as businesses increasingly rely on AI to streamline compliance processes.

The top benefits of implementing autonomous compliance reporting systems include reduced operational costs, improved reporting accuracy, faster turnaround times, and the ability to scale compliance operations without increasing headcount. By automating routine reporting tasks, financial institutions can focus on higher-value activities and improve compliance efficiency.
AI-powered agents can process large amounts of data in real-time, enabling faster and more accurate regulatory reporting. By 2030, it is expected that compliance reporting times will be reduced by 40%, allowing businesses to meet deadlines more effectively. These agents will automate data gathering, validation, and submission processes, reducing human error and ensuring that reports are always up to date.
Scaling GenAI agents for regulatory reporting involves overcoming technological challenges such as integrating AI with legacy systems, ensuring data privacy, and maintaining regulatory compliance across different jurisdictions. Furthermore, scaling AI-powered compliance solutions to handle the volume and complexity of global financial data will require significant investment in infrastructure and technology.

Regulatory bodies are expected to create frameworks that encourage the integration of GenAI agents into existing compliance structures. This may include issuing new guidelines for the use of AI in financial reporting, establishing protocols for data verification, and ensuring that AI systems are accountable for regulatory compliance. These efforts will be crucial to building trust in AI-driven compliance reporting systems.
The main risks associated with relying on GenAI-driven compliance agents include data security concerns, the potential for AI biases, and the challenge of ensuring the systems are constantly updated with the latest regulatory changes. There is also a risk that financial institutions may become overly reliant on AI, leading to a reduction in human oversight.
Financial institutions are crucial to driving the adoption of GenAI agents by adopting and integrating AI-driven solutions into their compliance processes. Their involvement will accelerate the development of AI-powered tools and create a competitive market for GenAI compliance solutions. These institutions will also need to work closely with regulators to ensure that AI systems meet legal and regulatory requirements.

• Rapid Adoption: GenAI-powered compliance agents are expected to reach 60% market adoption by 2030 in US & EU markets.
• Significant Cost Reduction: Autonomous reporting will reduce compliance costs by 35% by 2030.
• Enhanced Compliance Accuracy: Accuracy in regulatory reporting will improve by 25% with GenAI-driven agents.
• AI in Financial Institutions: Financial institutions are leading the adoption of AI agents for regulatory compliance.
• Growing Market: The market for GenAI agents in regulatory compliance will grow to $25B by 2030.
Central Bank Digital Currencies (CBDCs) are redefining money in both the U.S. and Europe. The U.S. Digital Dollar and Digital Euro pilots are projected to impact over $12 trillion in retail and wholesale payments by 2030. By 2025, more than 20% of major U.S. banks and 18% of EU banks are expected to participate in pilot programs for CBDC integration. Adoption is driven by goals to reduce transaction costs, accelerate settlements, and enhance cross-border liquidity management, while ensuring privacy and regulatory compliance.
Early results show that CBDCs can cut settlement times from 2 days to near-instant, with a 15–20% reduction in cross-border transaction costs. Pilot programs in the EU report $0.35–$0.50 per transaction savings versus traditional payment rails, while the U.S. pilots anticipate $0.40–$0.60 per transaction savings once scaled nationally. Beyond cost, the introduction of CBDCs is expected to increase retail digital wallet adoption by 12–15% and drive deeper integration of fintech and banking services into central bank infrastructure.
CBDCs are no longer theoretical they are poised to reshape payment ecosystems, liquidity management, and regulatory oversight. Early participation in pilots offers banks and fintechs a strategic advantage in understanding policy implications, settlement mechanics, and new revenue opportunities.
5 Key Quantitative Takeaways (2025–2030):
Download the full report to explore pilot program insights, policy analysis, and projected impacts on retail and wholesale payment systems.
The market for Edge AI in real-time payments is projected to reach $7.4 billion by 2025, growing at a CAGR of 27% from 2025 to 2030. This growth is driven by the increasing demand for secure, low-latency transaction processing and the need for real-time fraud detection in global payment systems. By 2025, 35% of payment systems in Europe and the USA will have integrated Edge AI technology to optimize transaction monitoring and enhance fraud detection. As Edge AI improves fraud prevention and reduces processing costs, it will play an increasingly important role in the future of payment systems.
Market Growth Projection (2025-2030):

The Edge AI market for real-time payments is experiencing rapid growth, driven by its ability to enhance fraud detection, reduce processing costs, and optimize transaction efficiency. As the payment industry continues to evolve, payment providers in Europe and the USA are adopting Edge AI to deliver more secure, fast, and cost-efficient solutions.By 2025, 35% of payment processors will leverage Edge AI to improve fraud detection capabilities, enabling faster transaction processing times and improved customer experiences. This integration will contribute to €2 billion in annual savings for financial institutions in Europe and the USA.
Edge AI Adoption Rate in Real-Time Payments (2025-2030):

Several trends are driving the adoption of Edge AI in real-time payments, including the growing need for faster fraud detection, reduced latency, and cost optimization.
AI models are being increasingly integrated into payment systems to provide faster, more accurate fraud detection while enhancing customer experiences by reducing transaction delays. Additionally, cloud-native platforms are gaining popularity, allowing banks and financial institutions to scale Edge AI capabilities for real-time payments.
The primary adopters of Edge AI technology in real-time payments include financial institutions, payment processors, and fintech companies.
These entities are particularly interested in improving fraud detection capabilities, reducing processing costs, and enhancing transaction speeds for a better customer experience. By 2025, 35% of payment processors in North America and Europe will have integrated Edge AI into their real-time payment solutions.
The USA is the leading adopter of Edge AI in real-time payments, particularly in regions like California and New York, where fintech ecosystems are thriving.
Europe is also experiencing significant growth in Edge AI adoption, with countries like UK, Germany, and France making strides in implementing this technology in their payment infrastructures.
Edge AI Adoption in Real-Time Payments Across Regions (2025):

The competitive landscape for Edge AI in real-time payments is dominated by leading fintech and AI technology providers, such as Nvidia, IBM, and Google Cloud, which offer AI-driven solutions for fraud detection and payment optimization.New entrants, including AI-powered payment platforms, are also gaining market share by offering more affordable and flexible solutions tailored to the needs of payment processors and financial institutions.