AI in Financial Services
We’re working with retail banks, private banks, insurers, and credit card companies to use predictive analytics and machine learning in financial services to streamline decision-making, reduce risk and enhance customer experience.
Our Clients in Financial Services
We’re rapidly advancing the use cases of AI in financial services through our work with numerous pioneering banks and institutions.
From using predictive analytics to optimize loan decisions to applying machine learning for fraud detection and prevention – the value you achieve from AI depends on your organization’s readiness to implement these solutions. We make sure you’re prepared – in terms of data foundations, leadership engagement, and the capability within your teams.
Extending the frontiers of performance with artificial intelligence in banking and finance
We’re working across the financial services sector to optimize operations, enhance customer experience, and elevate performance with data and AI. Retail banks, private banks, insurers, credit card companies – we’ve guided a wide variety of financial clients right from their first tentative steps in AI all the way to complex, boundary breaking applications.
There is a huge appetite for AI and machine learning in the financial services sector. And in many cases, they’ve built a sophisticated data team to help deliver it. We work with your teams to complement and enhance their data and AI work, so it can be developed and scaled to deliver value across the widest possible range of use cases.
AI Use cases in financial services
Understanding the right technologies and AI models to maximise value
While the widespread attention surrounding generative AI has dominated discussion, understanding the underlying technologies, their capabilities and practical applications is key to delivering impact beyond the hype.
Generative AI (Gen AI)
The financial services sector is poised to leverage generative AI to transform customer interactions while reducing cost and overheads.
Improved customer experience – Generative AI enables hyper-personalised customer interactions by analysing vast data points to predict preferences, tailor financial products, and provide real-time support. AI-driven chatbots and virtual assistants offer seamless, intuitive experiences that enhance customer satisfaction and loyalty.
Operational efficiency – Streamline processes and reduce costs by automating routine tasks such as document processing, claims management, and financial reporting. Generative AI can rapidly analyse unstructured data, generate insights, and support decision-making, freeing up human resources for higher-value strategic work.
Sales and marketing – Boost revenue generation with AI-driven content creation, campaign optimisation, and audience segmentation. Generative AI can analyse customer behaviour patterns to craft personalised marketing strategies, refine product recommendations, and deliver targeted messaging with greater precision and effectiveness.
Machine Learning (ML)
The financial services sector presents numerous opportunities for machine learning implementation, driven by complex data ecosystems, regulatory requirements, and the need for enhanced operational efficiency.
Risk management – Developing advanced predictive models for credit risk assessment, fraud detection, and potential financial misconduct. Machine learning initiatives can analyze vast datasets to identify complex patterns and anomalies with greater precision than traditional statistical methods.
Human x AI collaboration – Remove bottlenecks and speed up approvals through more nuanced, data-driven modeling. Machine learning enables comprehensive risk evaluation by incorporating alternative data sources alongside strategic human oversight.
Regulatory compliance – Enhancing monitoring and reporting capabilities through intelligent systems that can automatically detect potential compliance breaches, streamline reporting processes, and reduce manual intervention in complex regulatory environments.
Our impact with AI in fleet management
25%
Valuation accuracy improvement
Our AI-powered valuation platform for a global fleet management business improved accuracy across their 32 geographic markets.
€100 m
Additional asset value
Our AI-powered valuation platform for a global fleet management business improved accuracy across their 32 geographic markets.
75%
Cost reduction
Our AI-powered valuation platform for a global fleet management business improved accuracy across their 32 geographic markets.
Data & AI consulting for Financial Services
Create exponential value through new, AI powered revenue streams and business models. Link - Explore AI consulting services
Impact studies: AI in Banking
Finance
Building Data & AI capabilities to optimize customer journeys and adjust product offerings with ABN AMRO
ABN AMRO Bank N.V. is a leading Dutch bank, with over 22,500 employees and more than €400 billion in assets.
Read more
Finance
Rolling out an enterprise-wide data literacy program
Rabobank is the Netherlands' second largest bank, with over €600bn in assets and 43,000 employees across 38 countries.
Read moreFrequently Asked Questions
Implementing AI into your financial services organisation presents several challenges. Four issues consistently arise in our work with clients:
- Data quality and availability – data quality is crucial as AI and ML operations depend on accurate, well-structured data to deliver real commercial impact.
- Integration with legacy systems – integrating AI with older legacy systems can be both complicated and costly, sometimes requiring upgrades to your IT infrastructure.
- Lack of AI skills and talent – the shortage of data and AI expertise can slow progress. Investing in skills development and AI partnerships is key.
- High initial costs – starting will small pilot projects can help manage expenses, build internal buy-in and and demonstrate value and impact early on.
With nearly 20 years of real-world AI consulting experience, our team can support your enterprise in navigating these challenges. We focus on building your internal talent and developing a data and AI strategy to plug gaps and create scalable impact across your organisation.
This depends on your area of financial services and which function is driving AI implementation. Here are some areas where we’ve seen success:
AI in Banking – AI and machine learning can enhance fraud detection, personalises customer experiences, automates loan approvals, and streamlines compliance. Its importance lies in improving operational efficiency, reducing risks, and offering data-driven financial insights to stay competitive.
AI in Insurance – AI revolutionises insurance by automating claims processing, improving risk assessment, detecting fraud, and personalising policy recommendations. It ensures faster service, enhances customer satisfaction, and helps insurers maintain regulatory compliance while optimising operational costs.
AI in Leasing – AI optimises leasing operations by automating credit assessments, predicting asset depreciation, enhancing customer support, and streamlining contract management. Its importance lies in reducing manual effort, minimising risks, and improving decision-making with data-driven insights.
Take a look at our impact study into a global fleet management organisation here for illustrative examples.
Organizations frequently encounter challenges when implementing artificial intelligence by focusing on expansive, complex projects rather than strategic, incremental initiatives that demonstrate tangible value. Rewire has developed a methodical four-stage approach to ensure efficient and impactful AI integration:
- Strategic Opportunity Identification Systematically evaluate and prioritize potential AI applications by carefully analyzing strategic business challenges. This stage involves a comprehensive assessment that considers both the strategic importance of each opportunity and the technical feasibility of AI implementation. The goal is to select initiatives with the highest potential for meaningful business transformation.
- Comprehensive Solution Design Develop a robust implementation framework that anticipates scalability and fundamental business process changes.
- Minimum Viable Product (MVP) Development Rapidly prototype and test initial AI solutions to validate core hypotheses and demonstrate immediate value. This approach enables organizations to mitigate implementation risks while generating quick, measurable results.
- Strategic Solution Scaling Once an MVP proves its value, implement a deliberate expansion strategy. This involves refining and proliferating the solution across multiple service/product lines, geographic regions or operational contexts.
By following this structured methodology, organizations can transform AI adoption from a potential risk into a strategic advantage, ensuring careful use of financial and human resources while maintaining a clear path to commercial value.
Data quality, availability, and speed-to-deployment. These are the most common barriers to scalable AI programs that our data solutions overcome. Our experts in data infrastructure, data engineering and data governance work together to synchronize your data and break down silos to unlock AI opportunity.
Learn more about our Data Foundations program and how it provides an essential part of the AI puzzle.
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