AI in fintech: how to build breakthrough mobile applications

Fintech today and tomorrow: key trends and figures

The world of finance is undergoing a rapid transformation driven by AI and blockchain. This is already happening: the fintech sector is expected to grow to $1.5 trillion by 2030, and in 2024 alone, investments reached $52 billion. These are not just impressive figures, but a real shift in how businesses and consumers use financial services.

What is driving this change

- Growing digital transactions: already 73% of banking operations take place online, increasing pressure for efficient and secure mobile applications. - AI: real-time personalisation and automation improve service quality and process efficiency. - Blockchain: beyond cryptocurrencies, it strengthens security and transparency, building trust in digital services.

Mobile applications are no longer an add-on, but the primary platform for access and engagement. They are accelerating fintech adoption by offering convenience and personalised experiences at users’ fingertips.

For leaders, staying up to date and agile in adaptation is essential. Rapid implementation of breakthrough technologies makes it possible to stay ahead of the market and seize new opportunities.

Mobile experiences customers expect

Users want more than just the ability to make a transfer. They expect personalised experiences that support their financial goals and help them make better decisions.

Figures that matter

- By 2025, digital banking will cover 80.4% of the US population, meaning a growing user base that requires more advanced applications. - 89% of consumers already use digital banking, encouraging further innovation. - 65% of millennials prefer personalised financial services, so personalisation is what determines market differentiation.

Features that increase engagement

- AI-driven budgeting: intelligent budgeting, savings goals and dynamic alerts. - Investment trackers: portfolio overview, insights and automated recommendations. - Secure payments: tokenisation, instant confirmations, card support and mobile wallets.

AI powering intelligent fintech applications

Imagine an application that understands your needs and can anticipate them. That is exactly how AI is transforming fintech: from customer service and advisory to security.

AI applications that make a difference

- Virtual assistants and chatbots: instant, personalised support 24/7 and lower operating costs. - Predictive analytics: identifying patterns and forecasting trends to support decisions on savings, investments and risk. - Fraud detection: identifying unusual behaviour in real time, reducing losses and strengthening trust. - Real-time personalisation: offers and interface tailored to the user's context and behaviour.

The scale of change in figures

- The value of AI in fintech is expected to reach $22.6 billion by 2025. - 60% of consumers in the US already use AI-powered fintech services.

User-centred design remains at the core: combining advanced technology with real customer needs is now a competitive advantage.

How to develop fintech applications securely and compliantly

Innovation without compliance and security is a risk no business can afford. Regulations vary by country and region, and their requirements are constantly evolving. Non-compliance means not only financial penalties, but also loss of credibility and trust.

Data security is an equally significant challenge. As many as 74% of users are concerned about application security, so protecting information must be a priority from day one.

Security and compliance best practices

- Compliance by design: mapping GDPR, PSD2, PCI DSS, SOC 2 requirements and continuous regulatory monitoring. - Secure SDLC: code reviews, SAST/DAST, threat modelling, dependency scanning and penetration testing. - Strong authentication: MFA, biometrics, risk-based auth, robust session management. - Data protection: encryption at rest and in transit, tokenisation, data minimisation and secure key management. - Fraud and AML: transaction monitoring, anomaly detection, KYC and explainable ML models. - Observability and incident readiness: full logging, SIEM, runbooks, backups and disaster recovery. - Secure API and third-party integrations: OAuth 2.0/OIDC, rate limiting, zero-trust and vendor due diligence.

Architecture and scalability

- Cloud-native: microservices, Kubernetes and autoscaling with the shared responsibility model in mind. - Event-driven architecture: handling payments and settlements with idempotency and resilience. - AI model governance: drift, bias and explainability monitoring, as well as human-in-the-loop for high-risk decisions. - Data quality: lineage, consent-based access control and privacy by default. - Performance and availability: short time-to-first-interaction, WCAG accessibility and offline modes wherever possible.

From strategy to delivery: what to do now

- Define business goals and KPI: activation, retention, NPS, acquisition cost, fraud metrics. - Build a data strategy and privacy by design, and choose priority AI use cases with clear ROI. - Organise a cross-functional team: product, engineering, compliance, security, data and design. - Take a mobile-first approach: choose the architecture (native, cross-platform), launch CI/CD and feature flags. - Test and learn faster: A/B experiments, product analytics and frequent iterations based on user feedback. - Plan certifications and audits from the start to shorten time to market and build trust. - Collaborate with ecosystem partners: banks, payment processors, KYC/AML providers.

Fintech is accelerating right now. Companies that combine AI, mobile and security today will not only keep up with change, but shape it.