AI is enhancing financial personalisation as banks and fintech companies use machine learning to deliver tailored products, recommendations, and experiences to individual customers. A 2024 report by Accenture found that 73% of banking customers expect personalised financial advice, yet only 22% feel their bank delivers it. Companies that close this gap see 20% to 30% higher customer lifetime value, according to McKinsey. Wealthfront, Betterment, Monzo, and Revolut are leading this shift by using AI to analyse spending patterns, income flows, and financial goals to provide individualised guidance.
How AI Personalisation Works in Finance
AI personalisation analyses individual financial behaviour to deliver relevant content, products, and advice. When a Monzo user receives a notification that their coffee spending is 30% higher than last month, that insight is generated by a machine learning model that tracks spending categories across millions of anonymised accounts and compares individual patterns to benchmarks. When Wealthfront recommends a specific portfolio allocation, it uses models that consider the user’s income, tax situation, risk tolerance, and financial goals.
Recommendation engines suggest financial products. A neobank might recommend a high-yield savings account to a user with growing cash balances, or suggest travel insurance to a user who recently booked international flights. These recommendations are generated by collaborative filtering models similar to those used by Netflix and Spotify. Fintech revenue growing at a 23% CAGR is partly driven by personalisation that increases product adoption and customer retention.
Applications Across Financial Services
Wealth management is the most advanced personalisation category. Robo-advisors like Wealthfront and Betterment manage more than $60 billion in combined assets using AI-driven portfolio construction. Wealthfront’s tax-loss harvesting algorithm saved its clients more than $2.6 billion in taxes over its lifetime, according to company data. Personalised advice was historically available only to high-net-worth clients paying 1% or more in advisory fees. AI makes it accessible to anyone with a smartphone.
Banking personalisation improves engagement. Monzo’s spending insights feature, which categorises transactions and tracks budgets automatically, is used by 70% of its customers, according to the company. N26 personalises its app interface based on usage patterns. Revolut offers personalised cashback deals from merchants based on individual spending history. These features drive engagement that translates to higher revenue per user.
Lending personalisation improves conversion. Rather than offering a single loan product, AI-powered lenders present personalised terms based on the borrower’s profile. A borrower might see different rates, terms, and loan amounts depending on their specific financial situation. SoFi and LendingClub use personalisation to increase loan acceptance rates while maintaining risk standards. Fintech companies capturing banking revenues are differentiated by personalisation quality.
Privacy and Ethical Considerations
Financial personalisation requires access to sensitive data. Customers must trust that their data is being used to benefit them, not to exploit vulnerabilities. Regulators are concerned about “dark patterns” where personalisation is used to push products that benefit the company more than the customer. The FCA in the UK has issued guidance on fair treatment in personalised financial products.
GDPR requires that customers can opt out of automated personalisation. Banks must be transparent about how customer data is used for recommendations. More than 30,000 fintech companies are navigating these requirements. The growth from 20 to over 300 fintech unicorns includes companies whose core product is personalised financial services. As AI models become more sophisticated and customers become more comfortable with data sharing, financial personalisation will become the standard expectation rather than a premium feature.
