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.
• Usage‑based, context‑aware pricing becomes the default for AV risk.
• Tiered liability frameworks allocate risk across OEM, operator, and mixed control.
• Data‑first claims with standardized black‑box evidence compress cycle time.
• Cyber and recall coverage become integral to embedded bundles.
• Germany sets standards for evidence, safety KPIs, and OEM‑insurer contracts.
• EU harmonization accelerates cross‑border robotaxi and fleet coverage.
• Pricing models integrate ODD adherence, weather, road class, and software risk.
• Winners expose pricing/claims APIs and integrate with OTA risk management.

Premium growth reflects commercialization of AV pilots and OEM‑embedded cover for supervised automation features. Germany’s share is outsized early due to OEM concentration and stringent certification pathways; rest‑of‑Europe gains scale via robotaxi and logistics programs. By 2030, usage‑based policies dominate, with exposure priced per mile/minute/task and adjusted for ODD adherence, weather regimes, and road class. Share outcomes hinge on OEM distribution, fleet penetration, and regulatory clarity around manufacturer liability in automated mode.
Vendors capturing share pair transparent pricing APIs with explainable risk factors and audit‑ready evidence packs reducing friction for regulators and courts while improving reinsurer confidence.Market Analysis

Commercial fleets lead absolute premiums as autonomous features improve safety and utilization, enabling lower per‑mile pricing with strong telematics. Robotaxi operators scale as cities issue ODD‑bounded permits; pricing incorporates exposure to dense urban scenarios and downtime risk. OEM‑embedded cover grows with factory activation for L2+/L3 features bundled into service contracts. Last‑mile logistics expands with e‑commerce density and micro‑hub operations. Personal AV (L3/L4) remains smaller but accelerates as driver‑assist transitions to limited automation on motorways.
Buyer criteria: predictable pricing with clear ODD boundaries, audit‑ready incident packs, and parametric options for severe weather. Insurers prioritize sensor quality, data provenance, and OTA responsiveness.
• Liability allocation codifies manufacturer/system responsibility in automated mode.
• Usage‑based pricing integrates ODD, weather, map confidence, and software risk.
• Claims move to data‑first adjudication with standardized black‑box evidence.
• Cyber, recall, and downtime riders attach broadly to embedded bundles.
• OTA‑aware pricing updates limits/deductibles after software changes.
• Reinsurance structures adapt to correlated software/cyber events.
• EU harmonization (type approval, product liability) lowers multi‑country friction.
• Safety KPIs (safe disengagements, incident severity) become rating inputs.
• Commercial Fleets: Highest absolute premiums; strong telematics and utilization data; multi‑coverage bundles.
• Robotaxi Operators: Urban exposure; requires incident data packs and parametric downtime cover.
• OEM‑Embedded: Bound at activation; tied to service/maintenance contracts and OTA pipelines.
• Last‑Mile Logistics: Dense stops; priced by task/time windows; integrates depot micro‑hub risk.
• Personal AV (L3/L4): Early stage; motorway ODD with supervised modes; premium add‑ons for cyber/recall.
Success factors: evidence standards, OTA responsiveness, and transparent pricing rules.

Germany leads (~28%) given OEM density and certification pathways; the UK (~16%) and France (~15%) follow with robotaxi and fleet pilots; Nordics (~10%) benefit from high ADAS uptake and digital infrastructure; Italy (~9%) and Spain (~8%) scale with logistics and motorway ODD; Benelux (~7%) and CEE/Others (~7%) contribute through corridor programs and cross‑border logistics. Geography influences liability interpretation and data‑sharing rules, but harmonized EU guidance reduces fragmentation in policy language and claims evidence.
The field spans (1) OEM‑insurer partnerships; (2) fleet/robotaxi platform policies; (3) specialty carriers for cyber/product liability; and (4) claims/telematics vendors. Differentiation: explainable pricing, evidence standards, OTA responsiveness, and reinsurer trust. Expect consolidation around platforms that bundle usage‑based pricing, liability orchestration, and automated claims—with APIs consumable by OEMs and fleet orchestrators.
The market for composable core banking systems in Asia-Pacific and India is expected to grow significantly, reaching $4.8 billion by 2025, with a CAGR of 19% from 2025 to 2030. This growth is driven by the increasing demand for modular, flexible banking architectures that enable financial institutions to improve operational efficiency, enhance customer experiences, and accelerate digital transformation.
API ecosystems will be a key enabler of this growth, as financial institutions adopt open banking models and integrate with third-party fintech solutions. The demand for composable architectures will be especially high among banks and fintech firms looking to differentiate their offerings and adapt quickly to changing market conditions.
Market Growth Projection (2025-2030):

Composable core banking systems are poised to replace traditional legacy systems as financial institutions seek to improve efficiency, scalability, and adaptability. The integration of API ecosystems into banking platforms allows for greater flexibility, enabling banks to quickly respond to customer demands and market changes.
The adoption of modular banking solutions is expected to grow rapidly, with 40% of banks in Asia-Pacific and India adopting these systems by 2025. These systems offer better integration with third-party fintech services, allowing banks to enhance their service offerings while reducing operational costs and reliance on outdated infrastructure.
API Adoption Rate in Composable Banking (2025-2030):

Several trends are shaping the future of composable core banking architectures, including the rise of open banking and the increasing integration of third-party fintech solutions. API ecosystems enable faster service delivery, greater customization, and enhanced customer satisfaction.
Moreover, banks are increasingly adopting cloud-based solutions and microservices architectures, which provide scalability and resilience. By 2025, 40% of banks in India and Asia-Pacific will have moved to composable platforms, allowing for greater flexibility in their service offerings and operational models.
The primary adopters of composable core banking systems are large banks and fintechs that are looking to modernize their legacy infrastructure and offer more customized services. These organizations are leveraging API ecosystems to integrate third-party solutions and create more innovative, customer-centric services.
Smaller banks and credit unions are slower to adopt composable architectures, but they are beginning to explore these solutions as the cost of technology decreases and the benefits of modular systems become more evident.
In Asia-Pacific, India and China are leading the adoption of composable core banking systems, driven by large-scale digital transformation initiatives and fintech innovation. These countries are implementing regulatory frameworks that encourage open banking and API-driven services.
Southeast Asia is seeing slower adoption due to varying levels of technological infrastructure, but growth is expected to accelerate as fintech hubs like Singapore and Hong Kong push for more flexible, modular banking systems.
Composable Banking Adoption Across Asia-Pacific Regions (2025):

The competitive landscape for composable core banking in Asia-Pacific and India is dominated by new BaaS players like Railsbank, Finix, and Synapse, which are offering highly customizable banking platforms that allow for faster innovation.
Traditional banking giants like JPMorgan Chase, HSBC, and Standard Chartered are also entering the space, leveraging their established infrastructure to offer composable solutions to their clients. The competition is expected to increase, as both fintech startups and traditional banks aim to capture market share in the rapidly evolving market.
• The Generative AI wealth management market in the UK and EU is projected to grow from $15.2 billion in 2025 to $38.7 billion by 2030, with a CAGR of 20.2%.
• HNWIs (High Net-Worth Individuals) and millennials are the primary adopters of AI-driven wealth management, with 60% of clients projected to rely on AI-powered solutions by 2030.
• By 2025, 80% of wealth management firms in the UK will use AI for customer journey mapping and personalized financial advice, increasing to 95% by 2030.
• The EU market is expected to experience 22.5% CAGR due to the growing demand for AI-driven financial solutions, especially in digital-first nations like Estonia and Finland.
• Regulatory compliance, particularly with GDPR and MiFID II, is becoming a key concern for firms, with 50% of firms using AI tools for real-time compliance by 2030.
• AI-driven investment platforms are gaining ground, with 30% of firms adopting AI tools for client engagement by 2025, expected to rise to 50% by 2030.
• By 2030, 25% of all wealth management clients in the UK and EU will exclusively rely on AI-driven platforms for wealth management services.
• Firms are increasing their R&D budgets, with 20-30% of the annual R&D spend dedicated to AI innovations by 2025.
The generative AI-driven wealth management market in the UK and EU is expected to grow substantially from $15.2 billion in 2025 to $38.7 billion by 2030, representing a CAGR of 20.2%. This growth is primarily driven by the increased adoption of AI technologies for hyper-personalized financial advice and customer journey mapping. The UK is expected to hold 30% of the market share in 2025, with growth to 32% by 2030, while the EU accounts for 60% in 2025, with continued growth due to a higher number of digital-first markets such as Estonia and Finland. HNWIs and millennials are the key demographic groups fueling this demand, as they seek tailored financial advice driven by AI insights. In addition, AI solutions will provide wealth managers with the ability to deliver highly personalized strategies, improve client engagement, and stay compliant with GDPR and MiFID II regulations. By 2030, the demand for AI-driven platforms in wealth management is expected to increase by 60%, particularly as wealth grows and digitalization continues to permeate the sector.

The market for generative AI in wealth management is being shaped by rapid advancements in AI-driven tools that enable firms to offer hyper-personalized financial advice. By 2025, the market is expected to see $2.5 billion invested in AI technologies within the UK, with projections to reach $8.3 billion by 2030. The EU is projected to receive $3.8 billion of investment by 2025, growing to $9.5 billion by 2030. AI-driven platforms are gaining traction, especially in wealth management services targeting HNWIs, who expect more customized and efficient financial solutions. The UK is at the forefront of these developments, with 80% of wealth management firms expected to integrate AI tools by 2025, a figure expected to increase to 95% by 2030. These technologies are not only revolutionizing customer engagement but are also streamlining the operational aspects of wealth management, such as risk assessment, portfolio management, and regulatory compliance. However, firms face challenges in meeting evolving regulatory requirements, necessitating the integration of AI-driven compliance tools for seamless adherence to regulations like GDPR and MiFID II.
As wealth management firms increasingly adopt Generative AI, key trends are emerging that will reshape the industry between 2025 and 2030. AI-powered tools are allowing firms to offer hyper-personalized financial advice, with 60% of wealth management clients expected to rely on AI-driven solutions by 2030. The rise in data analytics capabilities provided by AI models will allow wealth managers to achieve greater accuracy in predicting market trends, with 90% accuracy in identifying key financial opportunities. In the area of regulatory compliance, AI is expected to play a pivotal role, with 50% of wealth management firms utilizing AI-driven compliance tools to meet evolving requirements, including those set forth by GDPR and MiFID II. The use of Generative AI for customer journey mapping will allow firms to deliver more seamless and automated client experiences, significantly improving client satisfaction. Additionally, AI will enhance R&D in wealth management, with 20-30% of annual R&D budgets being directed towards developing cutting-edge AI technologies, enabling firms to maintain their competitive edge.

In terms of client segmentation, the HNWIs segment, which represents 45% of the wealth management market in 2025, is expected to see the most significant growth, with a CAGR of 15% between 2025 and 2030. This demographic seeks high levels of personalization and sophisticated AI-powered financial strategies for wealth preservation and growth. Millennials will represent 30% of the wealth management client base by 2030, up from 20% in 2025. Their preference for digital-first solutions and personalized financial services will drive demand for AI-driven platforms. The AI-driven solutions market will also expand significantly in the portfolio management and real estate investment sectors, with these products expected to account for 35% and 25% of total transactions, respectively. As firms embrace customer journey mapping, AI will help in targeting younger clients who prioritize tailored and seamless digital experiences. By 2025, 25% of wealth management clients in both the UK and EU are expected to exclusively rely on AI-driven platforms, with this number rising to 50% by 2030.
The adoption of Generative AI in wealth management is seeing diverse growth across the UK and EU regions. The UK leads the way, with 30% of the total market share in 2025, and it is expected to maintain 32% by 2030. Germany and France are also major contributors, accounting for 22% and 15% of the market, respectively. The EU will experience a CAGR of 22.5% from 2025 to 2030, with markets like Estonia, Finland, and Sweden showing strong adoption rates due to their digital-first approach. GDPR compliance remains a key challenge, and by 2030, 50% of wealth management firms in both regions will use AI-driven tools to ensure compliance. Customer journey mapping through AI tools is rapidly being adopted, with 80% of wealth managers in the UK and EU incorporating AI by 2025, which will rise to 95% by 2030. As wealth management becomes more digital, AI will play a central role in offering personalized services that meet both client needs and regulatory requirements.

The competitive landscape for Generative AI in wealth management in the UK and EU is characterized by a mix of traditional wealth management firms and fintech disruptors. Major players like Robo-advisors and AI-driven wealth management platforms are expected to hold 45% of the market share by 2025. However, traditional wealth management firms like HSBC, Barclays, and UBS are rapidly adopting AI tools, and by 2030, these firms are projected to capture 40% of the market. The competition will also increase as AI-driven platforms expand, with 30% of wealth managers expected to offer these services by 2025, rising to 50% by 2030. Regulatory compliance will become a major competitive advantage, as firms that can navigate GDPR, MiFID II, and other regulations using AI will enhance consumer trust. Additionally, R&D investment will be a key differentiator, with 20-30% of the annual R&D budget directed towards AI innovation. As Generative AI continues to mature, wealth management firms will leverage it to drive more personalized client experiences while ensuring compliance and optimizing their operations.
The quantum machine learning market for real-time fraud detection is expected to grow rapidly in the USA and EU, expanding from $450 million in 2025 to $9.4 billion by 2030, reflecting a CAGR of 69%. The major adoption drivers in both regions include financial institutions, banks, and insurance companies. By 2030, quantum machine learning models will process 80% of financial transactions in real-time, enabling faster and more accurate detection of fraudulent activities. False positives are expected to decrease by 35%, improving fraud detection accuracy and operational efficiency. Institutional investors will account for 75% of market share, leading the adoption due to the need for better fraud prevention and risk management strategies. The integration of quantum algorithms will enable 2–3 times faster fraud detection and will significantly reduce the time required to process large amounts of transaction data. The ROI for financial institutions adopting quantum machine learning is projected to reach 20–28% by 2030, driven by improved fraud detection capabilities, lower operational costs, and faster decision-making. However, the market faces significant implementation challenges, including high initial investment costs, integration complexity with existing systems, and the scarcity of quantum talent, which may slow adoption in the early years of the forecast period.
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The quantum machine learning market for fraud detection is poised for rapid growth in USA and EU. By 2030, quantum algorithms are expected to process 80% of financial transactions in real-time, enhancing fraud detection speed and accuracy. With false positives reduced by 35%, financial institutions will benefit from improved detection rates and customer service. The market will expand from $450 million in 2025 to $9.4 billion by 2030, with CAGR of 69%. Institutional investors will account for 75% of market share, with banks and fintechs leading the charge in implementing quantum-powered fraud detection models. Cross-border fraud detection will improve by 40%, as quantum computing offers enhanced global data-sharing capabilities. By 2030, quantum machine learning will offer more accurate and faster risk management solutions, improving the security of international financial transactions. Real-time fraud detection will be 2–3 times faster, while quantum-enhanced algorithms will improve fraud detection accuracy by 30% compared to classical models. ROI for financial institutions is projected to reach 20–28%, driven by faster fraud detection, lower false positives, and improved risk management. Despite these benefits, implementation challenges such as high upfront costs and the lack of quantum expertise in the market could delay widespread adoption.
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The quantum machine learning market for fraud detection is rapidly gaining traction, with projections for growth from $450 million in 2025 to $9.4 billion by 2030, CAGR 69%. Key trends include the increasing adoption of quantum-powered models for real-time fraud detection, which are expected to process 80% of financial transactions by 2030. Quantum algorithms will improve fraud detection accuracy by 30% and reduce false positives by 35%, enhancing customer trust and reducing operational inefficiencies. Real-time fraud detection will become 2–3 times faster, allowing financial institutions to respond quickly to threats. Institutional adoption of quantum machine learning is projected to account for 75% of market share by 2030, driven by higher accuracy, reduced fraud-related losses, and better decision-making. Cross-border fraud detection will improve by 40%, making quantum machine learning key to global transaction security. The ROI for financial institutions adopting these technologies is expected to be 20–28%, driven by faster fraud detection, improved risk management, and better operational efficiency. However, implementation challenges such as high investment costs, complex integration, and data privacy concerns may slow adoption in the early stages. Regulatory hurdles in the EU related to GDPR compliance will also affect market growth.
The quantum machine learning market for fraud detection in USA and EU is segmented by institution type, data sources, and technology adoption. By 2030, institutional investors will account for 75% of market share, particularly large banks, fintech companies, and insurers. Quantum algorithms will significantly improve fraud detection accuracy by 30% and reduce false positives by 35%, enhancing overall operational efficiency. Real-time fraud detection will be 2–3 times faster with the use of quantum computing, enabling quicker and more accurate detection of fraudulent activities. The cross-border fraud detection capabilities will improve by 40%, allowing for more secure and efficient monitoring of international transactions. Financial institutions will benefit from improved risk management, enhanced by quantum machine learning models, which will process 80% of transactions in real-time. By 2030, the expected ROI from quantum machine learning applications is 20–28%, driven by reduced operational costs, more precise fraud detection, and improved customer satisfaction. Despite these benefits, implementation challenges such as integration complexity, high initial costs, and the need for specialized talent may slow adoption. Data privacy regulations in the EU will further influence the pace of quantum adoption in fraud detection.
The US and EU markets will dominate the quantum machine learning for fraud detection sector, accounting for 75% of the market share by 2030. The US will continue to lead, capturing 50% of the market, due to early adoption and substantial investments in quantum technologies. The EU, driven by regulatory frameworks like GDPR, will contribute 25%, focusing on secure fraud detection in compliance with data protection laws. Real-time fraud detection will be 2–3 times faster with quantum machine learning, significantly improving fraud detection accuracy by 30% and reducing false positives by 35%. Cross-border fraud detection will increase by 40%, as quantum technologies allow better data integration and global transaction monitoring. By 2030, quantum models will process 80% of transactions in real-time, improving operational efficiency. Institutional investors will provide 75% of market inflows, propelling adoption of quantum computing technologies in both regions. ROI for quantum-powered fraud detection systems is projected to be 20–28%, driven by improved fraud prevention, higher accuracy, and faster detection rates. Regulatory concerns, such as GDPR compliance in the EU, will influence the adoption pace, particularly related to data privacy and security.
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The quantum machine learning for fraud detection market is highly competitive, with major quantum computing companies like IBM, Google, D-Wave, and Microsoft leading the way. These companies will provide financial institutions with quantum-powered platforms and AI algorithms for enhanced fraud detection and risk management. Banks, fintech firms, and insurance companies are expected to drive 75% of market adoption by 2030. Cross-border fraud detection will improve by 40%, making quantum-powered solutions key to global transaction security. The competitive edge in the market will be determined by the ability to integrate quantum technologies with existing systems, offer scalable solutions, and comply with data privacy regulations such as GDPR. Institutional investors will play a major role in the adoption of quantum machine learning, contributing 70% of the market share. ROI from quantum machine learning applications in fraud detection is expected to be 20–28% by 2030, driven by the improvement of fraud detection accuracy, reduced operational costs, and faster decision-making. Partnerships between tech firms and financial institutions will enable the widespread deployment of quantum technologies across US and EU financial sectors.