‘My husband is much richer than me but I still wanted a prenup'
Foster, 56, was determined to sign the legal contract known as a prenup before walking down the aisle. In fact, she said she wouldn't get married without one.
'It was nothing to do with protecting my wealth. My husband is substantially better off than me. I wanted to go into the marriage with us knowing that we were doing it for love, not financial gain,' said Foster, a lawyer at the Milton Keynes firm MacIntyre Law.
'I also wanted to ensure our families didn't worry that they would lose out financially later in life if we separated. I didn't want his children, or mine, from previous relationships to fear that they would lose what their parents had worked for.'
Foster and her husband were ahead of their time. Prenuptial agreements are common in the United States, but were fairly rare in the UK, except among the ultra-wealthy. Now, though, lawyers say they are becoming more popular.
The number of divorces has fallen dramatically over the past 20 years, but the number of prenups has risen. Edwards Family Law, which specialises in divorce, said that there had been a 50 per cent surge in the number of prenups it dealt with last year, compared with 2023. They are particularly relevant in second marriages where you are more likely to have built up wealth before you met.
Without a prenup specifying otherwise, assets are often divided equally according to the 'sharing principle' unless there is good reason to do otherwise. However, a Supreme Court ruling on Tuesday (July 2) has suggested that this principle should not be applied to all assets accrued before the marriage.
The Supreme Court ruled in favour of Clive Standish, 72, who had transferred almost £78 million of assets to his ex-wife, Anna, 57, while they were married for tax planning purposes.
• Retired banker wins fight to keep majority of £80m 'gift' to wife
The assets had been accrued before his marriage and, during a lengthy divorce battle, he argued that they should not be considered as matrimonial assets, even though they had been held in her name while they were together — and the courts, eventually, agreed.
Vandana Chitroda from the law firm Broadfield said: 'It is likely that following this judgment, couples entering into pre and postnuptial agreements will be advised to ensure that non-matrimonial property is concisely defined.'
Charlotte Lanning from Edwards Family Law said that the growing use of prenups was probably a result of people getting married later. 'The average age at marriage is higher, so you may have already bought a house or set up a business. When everyone was getting married in their early twenties, they had not had a chance to build up any wealth yet.
'The prevalence of second marriages plays a part too. If you've had a messy divorce and lost half your assets, you want to preserve what you have left.'
Prenups are not legally binding in the UK, but an important Supreme Court decision in 2010 gave them more clout. A judge ruled that courts should take such agreements into account, provided that they were entered into freely by both parties; that there was 'full and frank' disclosure of their assets; that each party had independent legal advice and the agreement was not unfair.
They typically outline how you would divide your assets in the event of a divorce and are often used to protect inherited money, business ownership or inheritance for children. At the time of divorce, a court will consider the prenup in the context that it was made and the effect it would have on the couple if it were enforced.
'If the agreement only provided you with £100,000 but you had been living in a £2 million house, the court is unlikely to think that was fair,' Lanning said. 'You might have agreed not to take any spousal maintenance, but if you had since been in an accident and were unable to work, then the court would probably rule that you are entitled to some financial support.'
In the end, Foster's prenup was relatively straightforward. They agreed that what each had accrued before the marriage would remain their own, and that there would be no ongoing legal ties such as maintenance payments if they were to divorce.
Anything they accrued after the marriage would be shared equally if they separated. Foster said that this did not include any inheritances, which would be kept separate from their joint finances.
• Read more money advice and tips on investing from our experts
Foster said: 'We are very straight down the line and didn't want to muddy the waters. Having been divorced before, we knew that it's always a possibility. It's good to talk about these things from a place of love, rather than bitterness or unfairness.
'The way I see it is, If I go and buy a new car, I'm going to insure that car. That's not because I want to crash it or I'm planning to crash it, but because I want to be protected. It's a similar thing for me here.'
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Telegraph
35 minutes ago
- Telegraph
‘Should I pay my final salary pension into a Sipp to drop a tax band?'
However, when the lifetime allowance was abolished, anyone who already had fixed protection before March 15 2023 (the date of the Budget announcement) could resume paying into or getting pension contributions and keep their protections. So, although one part of the rules relaxing means you might be able to start paying in again, the tax relief rules might still get in the way of you achieving your goal of minimising your higher-rate tax, or mitigating it completely. Tax relief and earnings You can claim tax relief on the greater of 100pc of your UK earnings, or £3,600 in any tax year. For most personal pensions, including Sipps, contributions are typically taken from pay after tax and topped up by basic-rate (20pc) tax relief returned from the Government, which is paid directly into the pension. If an individual paid in £4,000, this would be topped up by £1,000 basic-rate relief, meaning £5,000 goes into their Sipp. A UK resident with no earnings can pay in up to £2,880 a year, which is topped up by pension tax relief to £3,600. You've mentioned that you receive income from a final salary pension scheme. Although pension income is taxed under income tax in the same way as salary or bonuses earned in your working life, it is not classed as earnings when calculating how much you can pay into a pension for tax relief. The same goes for investment income like dividends, savings income or property rental income, even if you are paying higher rates of tax on these sources. I'm afraid that unless you have earnings from another source, your ability to make pension contributions for tax relief is going to be very limited, meaning your strategy is unlikely to work. The full list of what counts as relevant UK earnings can be found in the Government's pension tax manual. Your question does raise some important points about how pension tax relief works, and the interaction with the personal savings allowance, so I've taken the chance to explain this below and it should help if you do have some earnings after all. The second lever the Government uses to limit the value of tax relief is the annual allowance. This allowance is £60,000 per tax year for most people, and applies to the total gross pension contributions made by or for you across all your pension schemes. So your own payments, the automatic government top-up and any employer contributions. The allowance is lower for people with very high incomes (usually £200,000 plus), or those who have already accessed a pension using a flexible income option, like pension drawdown. How pension tax relief works I've mentioned that schemes like Sipps automatically top up money you pay in personally by 20pc tax relief. So, for every £800 you save, £1,000 in total ends up in the pension pot. But people who pay more than 20pc tax on their earnings can claim more relief by contacting HMRC directly. This includes Scottish taxpayers paying intermediate tax (21pc) and above, and higher-rate (40pc) and additional-rate (45pc) taxpayers in the rest of the UK. The amount of extra tax relief on offer will depend on someone's earnings, and how much falls into those higher rates of tax. As you've pointed out, making a pension contribution and claiming that extra tax relief has the effect of expanding the basic-rate band, bringing all or some earnings out of higher rates of tax. For people earning between £100,000 and £125,140, the tax-free personal allowance is gradually removed, resulting in an effective tax rate of 60pc – or up to 67.5pc in Scotland. It's possible to use pension contributions to reduce taxable income, claw back personal allowance and boost retirement savings, if someone's UK earnings allow. Personal savings allowance You also mentioned the personal savings allowance. If someone can move out of a higher tax band thanks to pension contributions, it can help them get more of their savings interest tax-free. This is because the amount of personal savings allowance someone has depends on your tax rate. Basic-rate taxpayers get a £1,000 allowance, but the allowance is halved to £500 for higher-rate taxpayers and lost completely for those paying additional-rate income tax. How to claim extra tax relief The Government has recently introduced an online service to help claim extra tax relief, which makes things much easier for those who don't usually have to complete a tax return and avoids getting stuck on the telephone to the taxman. People who already complete a tax return will need to include their total gross (personal) pension contributions on the pension pages of their return to claim relief. Most of the above on pension contributions also applies to charity donations made under gift aid, except that pension income received does count as income on which gift aid can be claimed. So, if you don't have UK earnings and still want to reduce your marginal rate of tax, you could consider your favourite charity. I hope this has helped to answer your question, even though it might not be the tax outcome you were hoping for. With best wishes, – Charlene
The Guardian
36 minutes ago
- The Guardian
Across Europe, the financial sector has pushed up house prices. It's a political timebomb
'The housing crisis is now as big a threat to the EU as Russia,' Jaume Collboni, the mayor of Barcelona, recently declared. 'We're running the risk of having the working and middle classes conclude that their democracies are incapable of solving their biggest problem.' It is not hard to see where Collboni is coming from. From Dublin to Milan, residents routinely find half of their incomes swallowed up by rent, and home ownership is unthinkable for most. Major cities are witnessing spiralling house prices and some have jaw-dropping year-on-year median rent increases of more than 10%. People are being pushed into ever more precarious and cramped conditions and homelessness is rapidly rising. As Collboni asserts, housing lies at the heart of surging political disfranchisement across mainland Europe. The crisis is fuelling the far right – linked, for example, to the support for Alternative für Deutschland in Germany and the recent victory of the Dutch anti-Islam Freedom party. Housing has become a primary engine of inequality, reinforcing divisions between the asset-haves and have-nots and disproportionately affecting minority groups. Far from offering security and safety, for many in Europe housing is now a primary cause of suffering and despair. But not everyone is suffering. At the same time it is robbing normal people of a comfortable and dignified life, the housing crisis is lining the pockets of a small number of individuals and institutions. Across Europe in recent decades the same story has unfolded, albeit in very different ways: power has shifted to those who profit from housing, and away from those who live in it. The most striking manifestation of this shift is the large-scale ownership and control of homes by financial institutions, particularly since the 2008 global financial crisis. In 2023, $1.7tn of global real estate was managed by institutional investors such as private equity firms, insurance companies, hedge funds, banks and pension funds, up from $385bn in 2008. Spurred by loose monetary policy, these actors consider Europe's housing a particularly lucrative and secure 'asset class'. Purchases of residential property in the euro area by institutional investors tripled over the past decade. As a London-based asset manager puts it: 'Real estate investors with exposure to European residential assets are the cats that got the cream,' with housing generating 'stronger risk-adjusted returns than any other sector'. The scale of institutional ownership in certain places is staggering. In Ireland, nearly half of all units delivered since 2017 were purchased by investment funds. Across Sweden, the share of private rental apartments with institutional investors as landlords has swelled to 24%. In Berlin, €40bn of housing assets are now in institutional portfolios, 10% of the total housing stock. In the four largest Dutch cities, a quarter of homes for sale in recent years were purchased by investors. Even in Vienna, a city widely heralded for its vast, subsidised housing stock, institutional players are now invested in every 10th housing unit and 42% of new private rental homes. Not all investors are the same. But when the aim is to make money from housing it can mean only one thing: prices go up. As Leilani Farha, a former UN special rapporteur, points out, investment funds have a 'fiduciary duty' to maximise returns to shareholders, which often include the pension funds on which ordinary people rely. They therefore do all they can to increase prices and reduce expenditure, including via 'renoviction' (using refurbishment as an excuse to hike rents), under-maintenance and the introduction of punitive fees. When the private equity giant Blackstone acquired and renovated homes across Stockholm, it increased rents on some of the homes by up to 50%, the economic geographer Brett Christophers found. 'Green' retrofits in the name of sustainability are also an increasingly common tactic. The corporate capture of our homes has not sprung out of thin air. Decades of housing market privatisation, liberalisation and speculation have enabled the financial sector to tighten its grip on European households. From the 1980s in places such as Italy, Sweden and Germany, government-owned apartments were transferred en masse to the private market. In Berlin, for example, vast bundles of public housing were sold overnight to large corporations. In one single transaction, Deutsche Wohnen purchased 60,000 flats from the city in 2006 for €450m; just €7,500 per apartment. With the role of welfare states in housing provision dismantled, many countries reached for demand-side interventions such as liberalising mortgage credit. This fuelled widespread speculation, pushed up house prices and encouraged extreme levels of household indebtedness. The resulting financial crisis of 2008 provided fresh opportunities for investors. Countries such as Spain, Greece, Portugal and Ireland became a treasure trove of 'distressed' assets and mortgage debt that could be scooped up at bargain prices. Despite the widespread devastation caused by the crisis, Europe's dependence on the financial sector for housing solutions only intensified in the years that followed. As power has shifted to investors and speculators, and governments have become ever more reliant on them, so it has been withdrawn from residents. In order to incentivise or 'de-risk' private investment, governments across Europe have weakened tenant protections, slashed planning regulations and building standards, and offered special subsidies, grants and tax breaks for entities such as real estate investment trusts. One group in particular has borne the brunt of this: renters. Renters have seen their rents skyrocket, living conditions deteriorate and their security undermined. In Europe, some investment funds have directly driven the displacement of lower-income tenants and overseen disruptive evictions. Powerful financial actors have done a great job at framing themselves as the solution to, rather than the cause of, the prevailing crisis. They have incessantly pushed the now-dominant narrative that more real estate investment is a good thing because it will increase the supply of much-needed homes. Blackstone, for example, claims to play a 'positive role in addressing the chronic undersupply of housing across the continent'. But the evidence suggests that greater involvement of financial markets has not increased aggregate home ownership or housing supply, but instead inflated house prices and rents. The thing is, institutional investors aren't really into producing housing. It is directly against their interests to significantly increase supply. As one asset manager concedes, housing undersupply is bad for residents but 'supportive for cashflows'. Blackstone's president famously admitted that 'the big warning signs in real estate are capital and cranes'. In other words, they need shortages to keep prices high. Where corporate capital does produce new homes, they will of course be maximally profitable. Cities such as Manchester, Brussels and Warsaw have experienced a proliferation of high-margins housing products such as micro-apartments, build-to-rent and co-living. Designed with the explicit intention of optimising cashflows, these are both unaffordable and unsuitable for most households. Common Wealth, a thinktank focusing on ownership, found that the private equity-backed build-to-rent sector, which accounts for 30% of new homes in London, caters predominantly to high-earning single people. Families represent just 5% of build-to-rent tenants compared with a quarter of the private rental sector more broadly. These overpriced corporate appendages are a stark reminder of the market's inability to deliver homes that fit the needs and incomes of most people. While housing lies at the heart of political disillusionment today, it is for the same reason becoming a primary trigger for mobilisation across Europe. In October 2024, 150,000 protesters marched through the streets of Madrid demanding action. Some governments, including Denmark and the Netherlands, are introducing policies to deter speculators. But real estate capital continues to hold the power, so it continues to get its way – including by exploiting loopholes, and lobbying against policies that put profits at risk. In 2021, Berliners voted in favour of expropriating and socialising apartments owned by stock-listed landlords. But under pressure from the real estate lobby, politicians have stalled this motion. That same year Blackstone – Spain's largest landlord with 40,000 housing units – opposed plans to impose a 30% target for social housing in institutional portfolios. Struggles against the immense structural power of real-estate interests will be hard fought. In recent decades we have been living through an ever-intensifying social experiment. Can housing, a fundamental need for all human beings, be successfully delivered under the machinations of finance capitalism? The evidence now seems overwhelming: no. As investors have come to dominate, so the power of residents has been systematically undermined. We are left with a crisis of inconceivable proportions. While we can, and should, point the finger at corporate greed, we must remember that this is the system working precisely as it is set up to do. When profit is the prevailing force, housing provision invariably fails to align with social need – to generate the types of homes within the price ranges most desperately required. In the coming years, housing will occupy centre stage in European politics. Now is the time for fundamental structural changes that reclaim homes from the jaws of finance, re-empower residents and reinstate housing as a core priority for public provision. Tim White is a research fellow at Queen Mary University of London and the London School of Economics studying housing, cities and inequality
Finextra
38 minutes ago
- Finextra
AI Becomes the Banker: 21 Case Studies Transforming Digital Banking CX: By Alex Kreger
In banking, AI is no longer experimental—it became a cornerstone of strategy for improving how customers engage with their banks online and via mobile. Within this article, we analyze how AI's adoption in 2024 impacted key areas of digital CX, highlighting global trends as well as specific insights from major markets like the US and UK. We examine both quantitative gains— such as higher customer satisfaction scores, rising self-service usage and digital adoption rates—and qualitative developments, including more personalized services, smarter virtual assistants and greater accessibility in digital banking. Overall, banks that deployed AI at scale in 2024 reported significant improvements in digital channel usage and customer feedback. For example, Bank of America (US) announced its clients reached 26 billion digital interactions in 2024 (a 12% year-over-year increase), including 676 million interactions with its AI virtual assistant 'Erica.' In the UK, NatWest's AI assistant, 'Cora,' handled 11.2 million customer conversations in 2024, roughly equal to all interactions handled by the bank's call centers and branches. These examples illustrate the global trend: AI is enabling higher engagement on digital platforms and scaling up customer support without sacrificing quality. At the same time, a J.D. Power survey shows consumers are largely optimistic about AI's benefits: 72% agree AI will deliver easy, convenient self-service but also highlight areas to improve, such as building trust and ensuring human-like empathy in AI interactions. In the sections below, we break down the impact by five key areas of customer experience: personalization, self-service, customer engagement, support automation and accessibility. Source: J.D. Power Market Impact: Banking Industry Leads in AI Adoption A McKinsey survey indicates that AI adoption is now mainstream across industries. Overall, about 78% of organizations worldwide report using some form of AI in at least one business function in 2024, a huge increase from just 20% in 2017. Within this landscape, banking/financial services is a leading adopter of AI. IDC estimates the banking industry will invest about $31.3 billion in AI in 2024, making it the second-largest industry for AI spending (after software/IT services at $33 billion and ahead of retail at approximately $25 billion). Together, software, banking and retail account for 38% of the global AI spend. Source: McKinsey Global Surveys on the state of AI The years 2023–2024 saw an explosion of interest in generative AI (GenAI)—AI that can produce human-like text, code, images, etc. And banking has been at the forefront of deploying generative AI compared to other sectors. Source: Insider Intelligence © August 2024 Digital Banking Report GenAI Seduced Wall Street Globally, generative AI usage by companies surged over the past year. By mid-2024, 71% of organizations reported using generative AI regularly in at least one business function, according to a McKinsey Global AI Survey. This suggests that across all industries, a majority have begun experimenting with or deploying GenAI. However, financial services firms are among the heaviest adopters. In a late 2024 cross-industry survey by SAS, banking and insurance executives reported the highest current usage of generative AI—60% in each sector are already using GenAI in some capacity, the top rate among industries. Banking was singled out as leading all industries in GenAI integration, with the most use cases deployed per organization (on average). Almost every bank is at least planning for GenAI. A global survey found that 98% of banking leaders either use generative AI now (60%) or plan to within two years (38%). This virtually unanimous interest is unique—other sectors showed lower current usage and more hesitancy. Banks also back up these plans with resources: 90% of banks surveyed have a dedicated GenAI budget for the coming year, indicating significant investment in AI talent and tools. First-Mover Advantage: Banking's AI Head-Start Banking (often grouped with finance/insurance) figures prominently among the industries with the highest AI adoption. Research by IBM found that about 50% of enterprises in financial services have actively deployed AI, the highest of any sector. By comparison, the next-highest was telecommunications, at 37%, with other sectors generally lower. Research from Boston Consulting Group likewise shows 35% of banking companies qualify as AI 'leaders' (having scaled AI with high impact), a proportion surpassed only by fintech (49%) and software/IT firms (46%) While detailed GenAI adoption rates for every industry vary by study, indicators show sectors like technology, finance and insurance are ahead of others like manufacturing or government in GenAI experimentation. For instance, a 2024 Statista study noted generative AI usage in financial services jumped to 52% of firms actively using it (up significantly from 2023). Another survey of AI-savvy firms found that industries that underwent early digital disruption (e.g. finance, tech) have been quicker to adopt GenAI as well. A recent survey from SAS underscores that financial institutions lead in integrating AI into operations. The study found nearly 98% of banks (and insurers) either already use or plan to use generative AI in the near term—the highest among industries. In contrast, sectors like healthcare and manufacturing show more cautious adoption. For example, based on a McKinsey survey in healthcare found that 85% of healthcare leaders reported their organizations were exploring or had already adopted generative AI capabilities, with 15% not yet having started to develop proofs of concept. This indicates significant interest in the sector. Overall, banking's head start in digital transformation has given it an edge in AI implementation, with analysts noting it is 'leading other industries' in realized AI use cases. In contrast, heavily regulated or traditionally offline sectors are moving more cautiously. That said, even sectors such as healthcare and retail are rapidly exploring GenAI for their use cases (e.g. clinical decision support in healthcare, and AI-assisted marketing content in retail). Proof in the Profits Banks report concrete gains from GenAI deployments, more so than many peers in other industries. In banking, early adopters say GenAI is already improving employee productivity and customer experience. For example, among banks that have implemented GenAI, 88% have seen improvements in risk management and compliance, and 85% report time/cost savings. These are significant positive outcomes. Banks also use GenAI in diverse departments: marketing is the most common area (47% of banks use GenAI there), followed by IT (39%), sales (36%), finance (35%) and customer service (24%)—all higher rates than cross-industry averages. Few other sectors have GenAI permeating so widely across functions. This broad applicability in banking (from automating fraud reviews to generating customer communications) underscores how financial firms are integrating GenAI into their core workflows more aggressively than most. Beyond Fraud: Use Cases Expands Banks initially applied AI in areas like fraud detection, risk management and credit analytics—core operations in which AI could immediately reduce losses and improve decisions. Indeed, 64% of finance leaders report using AI for fraud detection and risk management in their institutions. These risk-oriented uses remain vital, but as AI capabilities have matured, banks are expanding into customer-facing and efficiency-driven applications. According to a 2024 banking technology report, chatbots/virtual assistants and personalized marketing are now seen as the AI use cases with the greatest future value in banking (cited by 87% and 83% of surveyed banks, respectively), alongside back-office automation (74%). Traditional areas like fraud prevention (65%), credit underwriting (62%) and regulatory compliance (58%) are still heavily prioritized, reflecting that these were some of the first uses of AI in banking and continue to be critical for reducing losses. In summary, banks today leverage AI across a wide range of functions: from customer service chatbots to algorithmic trading, but they lead particularly in risk, fraud and personalization use cases. Transforming Digital Banking CX: 21 Real-World Case Studies UXDA has consistently led the way in merging AI with digital banking experiences. In 2016, our AI-powered super app concept gained global recognition. By 2020, we were the first to design a conversational banking chatbot driven by AI, and in 2024, we unveiled the industry's first AI-powered spatial banking concept tailored for Apple Vision Pro. We collected 21 real-world case studies showcasing how leading banks and fintech companies implemented AI in 2024 and 2025. Each example highlights a recent banking AI application project that significantly enhanced the user, customer or employee experience, outlining the company involved, the AI solution applied, its use case, the resulting impact or benefits, the year of implementation and a source for further exploration. In these case studies, you will see that across the global banking industry, AI has shifted from pilot to profit generator: intelligent tools now accelerate back-office work, raising employee productivity and shrinking response times, while smarter analytics empower advisors to deliver faster, better-informed guidance. On the customer side, AI assistants can handle 20 million chats around the clock—driving record engagement and a 150 percent jump in satisfaction scores even as call-center volumes fall by half. Fraud-fighting models double detection rates, block over $350 million in attempted theft for the case and slice scam losses by up to 50 percent, with one bank saving 17 percent more funds for clients and processing 250,000 security queries each month. Instantaneous self-service resolves most issues in under two minutes, trims wait times by 40 percent and cuts repetitive contact by 55 percent, while algorithmic credit engines approve 44 percent more borrowers at 36 percent lower rates—proof that AI can widen access as well as convenience. Each of these cases exemplifies how banks and fintechs are leveraging various AI technologies—from large language models and voice recognition to machine learning analytics—to improve user experience, personalize services, fight fraud and streamline operations. The past year has seen rapid adoption of generative AI (like chatbots and assistants), as well as continued gains from predictive models in fraud detection and credit. These real implementations in 2024–2025 demonstrate tangible benefits, such as faster customer service, increased financial security, more inclusive products and higher customer satisfaction across the banking industry. In short, from faster payments (60 percent quicker) to safer transactions and $40 million profit bumps from automated support, AI is quietly rewriting banking's cost, risk and customer-experience equations in real time. 1. JPMorgan Chase (2024) AI Application: Employee productivity and research Experience Impacted: EX - Banking Employee Experience Internal ChatGPT-Like Research Assistant: The largest U.S. bank rolled out a generative AI assistant called 'LLM Suite' to ~50,000 employees in its asset and wealth management division. This AI tool helps bankers with writing research reports, generating investment ideas and summarizing documents, essentially doing work akin to a research analyst. Impact: It boosts employee productivity and idea generation, which can translate to faster, better service for clients. 2. Morgan Stanley (2023–2024) AI Application: Financial advisors support and research retrieval Experience Impacted: CX - Banking Customer Experience Advisor Knowledge Chatbot: Morgan Stanley partnered with OpenAI to deploy a GPT-4 powered chatbot for its financial advisors. This assistant (launched in late 2023) gives advisors instant access to the firm's vast research and intellectual capital. The assistant was adopted by 98% of Financial Advisor teams. Impact: Advisors get quick, precise answers from firm knowledge, enabling more informed and timely advice to clients. This generative AI tool has improved the quality and speed of client consultations. 3. Goldman Sachs (2024–2025) AI Application: Internal workflow automation Experience Impacted: EX - Banking Employee Experience 'GS AI Assistant' for Employees: The investment bank introduced an AI assistant to 10,000 employees with plans to expand to all staff. This generative AI tool can summarize and proofread emails, draft code and answer internal queries, operating within Goldman's secure environment. Impact: It has streamlined daily workflows,, e.g., reducing time spent on email and coding tasks, and is expected to improve employee productivity and response times. Goldman views it as augmenting staff efficiency rather than replacing humans. 4. Bank of America (2024) AI Application: Customer self-service assistant Experience Impacted: UX - Banking User Experience 'Erica' Virtual Assistant: BofA's AI-driven virtual financial assistant, Erica (launched in 2018) reached new milestones by 2024. Erica surpassed 2 billion client interactions in total, doubling from 1 billion just 18 months prior. Integrated in BofA's mobile app and online banking, Erica uses advanced NLP and predictive analytics (though not yet generative AI) to help 42+ million customers with tasks like bill payments, balance queries, budgeting tips and even telling jokes. Impact: Clients now engage with Erica about 2 million times per day, getting instant self-service help. This has improved customer satisfaction and digital adoption, acting as a 'personal concierge' that saves time and provides personalized insights (e.g., spending alerts, subscription monitoring). 5. Wells Fargo (2023–2024) AI Application: Customer virtual assistant Experience Impacted: UX - Banking User Experience 'Fargo' AI-Powered Assistant: In 2023, Wells Fargo launched 'Fargo,' a virtual assistant in its mobile app powered by Google's Dialogflow and PaLM 2 LLM. By late 2023, Fargo had handled 20 million interactions since launch and was on track for 100 million/year as capabilities expanded. Fargo can answer everyday banking questions via voice/text and execute tasks like transferring funds, paying bills, providing transaction details and more. Impact: Customers have a 'sticky' engagement (averaging 2.7 interactions per session) with Fargo for quick self-service. It runs 24/7, improving service availability, and new features (e.g., showing paycheck history and credit score changes) are being added through 2024 to enrich the personalized banking experience. Wells Fargo reports this generative AI assistant is speeding up customer service while also informing future AI deployments internally (Source: VentureBeat). 6. Citibank – Hong Kong (2024) AI Application: Personal finance management Experience Impacted: CX - Banking Customer Experience 'Wealth 360' AI Personal Finance Manager: Citi's Hong Kong division launched Wealth 360 in its mobile app in 2024, a digital wealth management and personal finance feature powered by AI and open banking. Developed with fintech partner Planto, it uses AI-driven data enrichment to provide customers with personalized insights, e.g., cash flow analysis, spending reports, budgeting tips and even carbon footprint tracking of purchases. The app aggregates data from multiple banks (via Hong Kong's open banking initiative) and uses AI to deliver over 20 tailored insights and recommendations per user. Impact: Wealth 360 offers a next-gen personal finance experience: customers get a holistic view of finances across accounts and receive proactive advice (including sustainability insights) to improve their financial health and decisions. This enhances CX by turning raw data into actionable, personalized guidance. 7. Royal Bank of Canada (2024) AI Application: Automated savings and financial guidance Experience Impacted: CX - Banking Customer Experience, UX - Banking User Experience NOMI Insights and Forecast: RBC has leveraged AI within its app through NOMI, a suite of smart money management tools. In 2024, RBC integrated NOMI Insights with its InvestEase platform to help clients save and invest more effectively. NOMI uses machine learning to analyze account activity and send timely, personalized notifications—for example, spotting when a customer has 'spare cash' and suggesting moving it to savings or investments. Other AI-driven features include NOMI Budgets (auto-categorizations and budget tracking) and NOMI Forecast (7-day cash flow predictions). Impact: These AI features act as a 'virtual financial coach,' nudging users toward better habits. RBC reports that NOMI's personalized alerts have helped customers stick to plans, contributing to improved savings rates and financial confidence. 8. HSBC (2024) AI Application: Fraud detection, customer service, personalization Experience Impacted: CX - Banking Customer Experience AI-Powered Services and GenAI Sandbox: HSBC has been investing in AI at scale. Hundreds of AI use cases are in production across the bank's global operations. These include fraud detection and transaction monitoring systems, customer service chatbots and risk management models deployed over the past decade. For example, HSBC's AI can flag suspicious transactions and assist compliance teams in real time. In 2024, HSBC was selected for the Hong Kong Monetary Authority's Generative AI Sandbox, where it is developing new GenAI solutions. Planned use cases include GenAI chatbots for customer inquiries, AI that delivers tailored market insights to sales staff and AI to automate parts of fraud investigations. Impact: HSBC's broad AI adoption has already enhanced security and efficiency (the bank publicly shared that AI helps in areas from fraud prevention to better customer service). The 2024 GenAI initiatives aim to further improve CX by providing more personalized, instant support to customers and faster incident resolution, all while ensuring ethical and responsible AI use. 9. DBS Bank (2024) AI Application: Fraud prevention, customer service automation, personalization Experience Impacted: CX - Banking Customer Experience Enterprise-Wide AI (Fraud, Personalization & Service): Southeast Asia's largest bank, DBS, has 'industrialized' AI across its business. By late 2024, DBS had moved from 240 experimental projects to 20+ deployed AI use cases. One headline result: using AI in risk management led to a 17% increase in funds saved from scam/fraud attempts for customers. DBS built 100+ algorithms analyzing up to 15,000 data points per customer to offer hyper-personalized financial advice (for example, using behavioral and location data to tailor offers for parents or shoppers). It also rolled out a GenAI-powered 'CSO Assistant' to 500 customer service officers in Singapore, HK, Taiwan and India. This internal assistant transcribes and summarizes customer calls and suggests solutions, helping staff handle over 250,000 queries a month more efficiently. Impact: AI has boosted both operational efficiency and customer engagement at DBS. Customers benefit from reduced fraud losses, more relevant product recommendations and faster service (the CSO Assistant speeds up call resolution). Internally, DBS's employees are empowered by AI tools (including an internal 'DBS GPT' chatbot) to serve customers in a more personalized and productive way. 10. Mastercard (2024) AI Application: Fraud prevention and transaction security Experience Impacted: CX - Banking Customer Experience Generative AI for Fraud Detection: Mastercard deployed a new generative AI plus graph analytics system to combat payment card fraud. In mid-2024, the company announced that this AI approach doubled the detection rate for compromised cards before fraud occurs. By analyzing network patterns and using GenAI to identify subtle signals of card-testing or enumeration attacks, Mastercard's AI flags at-risk card credentials much earlier. Impact: This proactive detection prevents fraud before customers even realize an issue. It also means fewer false declines during purchases. Merchants see reduced fraud losses and legitimate transactions aren't erroneously blocked, thereby improving the checkout experience. 11. Visa (2024) AI Application: Fraud detection and transaction scoring Experience Impacted: CX - Banking Customer Experience AI Scam Disruption and Payment Security: Visa has scaled up its AI-driven security systems to protect the payments' ecosystem. By late 2024, Visa's AI and machine learning tools enabled it to block 85% more fraudulent transactions on Cyber Monday compared to the year prior, even as attempted fraud spiked by 200% (fraudsters are using AI, too). One notable 2024 rollout was Visa's Account Attack Intelligence (VAAI) Score, which uses generative AI to detect and score the likelihood of card enumeration attacks in real time. This helps banks stop bot-driven card testing (in which fraudsters rapidly guess card details) before fraud occurs. Impact: Thanks to AI, Visa reports it prevented over $350 million in fraud attempts in 2024. Cardholders benefit through safer transactions and fewer disruptions. – for example, more genuine purchases are approved, and there are fewer instances of cancelling cards due to fraud. Visa's AI defenses ultimately make digital payments more secure and seamless for millions of users. 12. NatWest (2024–2025) AI Application: Customer service chatbot Experience Impacted: CX - Banking Customer Experience 'Cora' Chatbot Enhancement with OpenAI: UK's banking group, NatWest, put AI at the core of its customer experience strategy. In 2024, it began collaborating with OpenAI to supercharge 'Cora,' its customer-facing virtual assistant, and an internal assistant 'Ask Archie.' The goal is to use GenAI to handle more complex inquiries and even streamline fraud reporting. Early results are impressive: NatWest says adding generative AI to Cora boosted customer satisfaction scores by 150% and significantly reduced how often a human agent needed to intervene. For example, more customers are able to resolve issues via Cora alone. Impact: Customers get faster, smarter self-service for support (Cora is available 24/7 and now more accurate and helpful), freeing up bank staff for harder cases. NatWest is also exploring using AI to let customers report suspected fraud via the chatbot (instead of phone), which would secure accounts faster and cut call wait times. This pioneering use of OpenAI tech in UK banking is expected to save customers time and enhance digital banking safety. 13. Revolut (2024) AI Application: Fraud detection (APP scams) Experience Impacted: CX - Banking Customer Experience AI Scam Detection for Payments: The global fintech Revolut (with 52+ million customers) launched an advanced AI-powered scam-prevention feature in its app in February 2024. This machine learning system monitors outgoing transfers and card payments in real time, and if it detects patterns indicative of an Authorized Push Payment scam (e.g., a customer being tricked into sending money), it will intervene and even decline the payment. The app then prompts the user with an 'intervention flow,' asking extra questions and showing educational warnings to 'break the spell' of scammers. It can also escalate to a human fraud specialist via chat. Impact: In testing, Revolut's AI feature reduced fraud losses from such scams by 30%. This directly protects customers from losing money and gives them an added layer of security without overly restricting genuine transactions. It also offers peace of mind: Revolut can allow legitimate payments to go through while blocking what is likely fraud, striking a balance between safety and user freedom. 14. Klarna (2024) AI Application: Customer support and shopping assistance Experience Impacted: CX - Banking Customer Experience Generative AI Customer Service and Shopping Assistant: The Swedish fintech Klarna (BNPL and shopping platform) deployed a gen AI assistant that, by early 2024, was handling two-thirds of all customer service chats for its 150 million users. Built on OpenAI's tech, this chatbot can manage multilingual customer inquiries about refunds, returns and orders, effectively doing the work of 700 full-time support agents. It also powers a conversational shopping feature (a ChatGPT plugin) that offers product recommendations. Impact: Within one month of launch, Klarna's AI assistant had 2.3 million conversations and achieved customer satisfaction on par with human agents. It resolved issues so efficiently that repeat inquiries dropped 25%, and average resolution time fell from 11 minutes to under 2 minutes. Available 24/7 in 35+ languages, this AI has greatly improved CX by cutting wait times and providing instant help. Klarna estimates a ~$40M annual profit uplift from this, due to support cost savings and happier customers driving more business. 15. bunq (2024) AI Application: Conversational banking assistant Experience Impacted: CX - Banking Customer Experience, UX - Banking User Experience 'Finn' – Europe's First Conversational Banking AI: Netherlands-based bunq, one of Europe's largest neobanks, upgraded its AI money assistant, 'Finn', in May 2024 to make it fully conversational. Finn (launched in late 2023) uses generative AI to answer customer questions about their accounts, spending habits and even provide travel recommendations based on other users' reviews. It essentially replaces the app's search function with a chatbot that remembers context and can handle follow-up questions. Impact: In its beta, Finn answered over 100,000 customer questions within a few months. Now, with conversation mode, it delivers personalized, context-rich answers much faster (twice the speed of before). It's also helping bunq's operations: Finn now fully resolves 40% of user support queries and assists in an additional 35% of all queries daily without human intervention. This means that 75% of bunq users get instant help for many issues, improving service experience, while the bank scales support efficiently as its user base (17 million across the EU and growing) expands. 16. Lunar (2024) AI Application: Voice banking assistant Experience Impacted: UX - Banking User Experience GenAI Voice Banking Assistant: The Danish fintech Lunar launched Europe's first GenAI-powered voice assistant for banking in October 2024. Unlike typical phone IVR systems, Lunar's solution uses a voice-native GPT-4 model to hold natural conversations. Customers can speak to it for 24/7 support with no wait times. It can handle both simple requests and complex queries (like guiding a PIN change or giving spending breakdowns) with the ability to understand interruptions and follow-ups. Impact: Still in beta, it's already creating a smoother, more intuitive call experience. Lunar's CTO noted it especially helps customers who might struggle with traditional digital interfaces, by being more 'patient and understanding' in handling queries. The bank expects this voice AI will eventually handle about 75% of all customer calls, dramatically reducing wait times and freeing human agents. Ultimately, Lunar's customers will get instant, personalized service by voice, making banking more accessible and human-like through AI. 17. Nubank (2023-2025) AI Application: Customer service and operational support Experience Impacted: CX - Banking Customer Experience Generative AI Virtual Assistant: In March 2025, Nubank, Latin America's largest digital bank, partnered with OpenAI to integrate advanced AI solutions across its operations. This collaboration uses OpenAI's models, such as GPT-4o and GPT-4o mini, to significantly enhance both customer experience and operational efficiency. Through this partnership, Nubank is improving various aspects of its services—from internal operations to real-time customer support. In a pilot phase in 2023, with its NuCommunity users, Nubank employed AI to optimize several customer-facing features. For instance, the AI-powered assistant helped manage customer service inquiries, while the Call Center Copilot assisted agents with providing quick and relevant responses by analyzing internal knowledge bases. The fraud detection system uses GPT-4o vision to detect fraudulent transactions by combining natural language processing with image recognition. Impact: The integration of AI tools has already led to faster decision-making among employees, with over 5,000 users accessing internal AI solutions. Additionally, 55% of Tier 1 inquiries are now handled autonomously by the AI assistant, improving customer satisfaction while reducing chat response times by 70%. This partnership is a critical step for Nubank in achieving its goal of offering personalized financial services and cutting-edge operational tools, all of which aim to deliver an improved customer experience and greater efficiency. 18. Pix via Nubank (2024) AI Application: Conversational payments Experience Impacted: CX - Banking Customer Experience, UX - Banking User Experience AI-Powered Pix Payments via WhatsApp: Nubank also applied generative AI to Brazil's instant payments system, Pix. In late 2024, it rolled out a feature for customers to send Pix payments using voice, text or even images through WhatsApp, with AI handling the interpretation and execution. From October to December 2024, a test was conducted using 2 million users. The AI can understand a message like 'Send R$50 to João' (even via voice note or a photo of a handwritten note) and then process that payment. Impact: This made transferring money even more convenient; users can simply talk or message instead of manually using the app. Thanks to AI optimizations, transaction processing time was cut by up to 60% without compromising security. For WhatsApp users, it removes the need to switch apps at all. By meeting customers in their preferred channels with AI, Nubank is enhancing UX through speed and simplicity, all while keeping payments fee-free and safe under Pix's guarantees. 19. Upstart (2024) AI Application: Loan underwriting and risk assessment Experience Impacted: CX - Banking Customer Experience AI Loan Underwriting Platform: The fintech Upstart has pioneered AI-based lending, and by 2024 its platform was adopted by 500+ banks and credit unions for personal and auto loans. Upstart's AI models evaluate credit risk more holistically than FICO scores. According to Upstart's results (2023), this led to 44% more loan approvals at 36% lower average APRs for the same risk level, compared to traditional models. Essentially, many borrowers who would be declined by legacy criteria can be approved by Upstart's model with affordable rates, because the AI finds subtle signals of creditworthiness. Impact: Upstart's lending AI has expanded access to credit for underserved populations (one study noted it approved 35% more Black borrowers and 46% more Hispanic borrowers than traditional methods) while maintaining or even improving loan performance. For consumers, the experience is also smoother. Over 80% of Upstart-powered loans are approved instantly with no human intervention, enabling nearly immediate decisions and funding. This case shows AI's power to make credit more inclusive and convenient. 20. Commonwealth Bank of Australia (2024) AI Application: Fraud prevention, customer service, security alerts Experience Impacted: CX - Banking Customer Experience Holistic GenAI Transformation: Commonwealth Bank of Australia (CBA), the nation's largest bank, shared a strategic update in November 2024, showcasing significant advancements from its recent AI deployments. As one of the first banks worldwide to embed generative AI in customer-facing features, CBA has reduced customer scam losses by 50% through AI-powered safety tools like NameCheck, which flags payee name mismatches, and CallerCheck, which verifies the authenticity of calls claiming to be from the bank. Additionally, GenAI-driven suspicious transaction alerts have led to a 30% reduction in customer-reported fraud cases by proactively notifying users of unusual activity. CBA's AI-enhanced in-app messaging and virtual assistant have also decreased call center wait times by 40% over the past year, enabling customers to self-serve or resolve issues more efficiently before contacting support. Impact: CBA's AI initiatives have delivered substantial customer experience improvements, with a 50% reduction in scam losses, a 30% drop in fraud cases and a 40% decrease in call center wait times. CEO Matt Comyn emphasized that AI is pivotal to providing 'superior customer experiences,' enhancing both security and personalized service. The bank is also exploring AI automation for processes like business loan reviews to further save clients time. 21. Capital One (2024–2025) AI Application: Voice/text banking assistant Experience Impacted: CX - Banking Customer Experience, UX - Banking User Experience 'Eno' AI Assistant and Voice Banking: Capital One's Eno is an AI-powered chat and voice assistant that supports customers with tasks such as tracking spending and answering account-related questions. Originally launched as a text-based service, Eno expanded to voice interfaces and smart speakers. By 2024, Capital One reported that Eno reduced call center contact volumes by 50%, according to a case study by Redress Compliance. The assistant conversationally handles basic banking requests, provides insights like alerts for ending free trials or unusual charges and enables hands-free task execution. Impact: Eno's 24/7 availability has driven a 50% reduction in call center contacts, significantly improving service efficiency and consistency. Customers benefit from immediate responses, reducing wait times and frustration. Additionally, Eno enhances security through AI-driven account monitoring and background authentication. Serving Capital One's approximately 100 million customers, Eno demonstrates how an AI chatbot and voice assistant can scale customer support while delivering a human-like, seamless digital banking experience. Conclusion: from Hype to Habit Banks worldwide are leading the charge in AI adoption, leveraging the technology more extensively—and investing more heavily—than most other industries. By 2024, an overwhelming majority of banks have integrated AI into business functions—from back-office automation to customer-facing chatbots. This outpaces adoption in sectors such as manufacturing or healthcare, where AI use is growing but is not as ubiquitous. The advent of generative AI over the past year has only widened this gap, with banks quick to pilot GenAI for improved customer service, risk management and operational efficiency. Globally, financial institutions in regions like North America, Europe and Asia are all onboard the AI trend, though uptake is fastest in countries like India, China and the UAE, and somewhat slower in others due to regulatory or cultural factors. Both in the United States and the United Kingdom, 2024 can be seen as a breakthrough year for AI in banking. Virtually every major bank in every major market is now either using generative AI or actively planning to use it. They are motivated by clear benefits: better fraud detection, cost reductions, personalized customer experiences and by competitive pressure as fintech players and 'AI-native' firms disrupt traditional banking. Surveys show upwards of 90% of bank executives in the US and UK have greenlit AI projects or increased budgets for GenAI, demonstrating strong confidence in the technology. Going forward, the banking sector is expected to continue investing aggressively in AI capabilities. Analysts project exponential growth in AI spending by banks (e.g., global generative AI spending in banking could reach ~$85 billion by 2030). Key use cases like fraud detection, chatbots, credit scoring and algorithmic trading will become further enhanced by advanced AI. Banks will also likely collaborate with regulators to develop frameworks for responsible AI (as seen in the UK's surveys) and address challenges like bias and transparency. In sum, banking's early embrace of AI—now turbocharged by generative AI—is positioning the sector for significant transformation in the years ahead, outpacing many other industries in the AI adoption curve. 2024 was a pivotal year for AI in banking, as institutions worldwide moved from pilot projects to real deployments that tangibly improved the digital customer experience. Globally, banks observed that AI could boost both customer satisfaction and operational performance—a true win-win. By automating routine tasks and augmenting human employees, AI allowed banks to serve more customers with greater consistency. Quantitatively, we saw metrics trend in the right direction: higher Net Promoter Scores in markets in which digital CX was strong, record digital adoption rates (with active digital users exceeding 75%–80% at many banks) and significant increases in self-service transactions and chatbot interaction counts. One Capgemini study even linked AI-driven CX improvements to double-digit gains in satisfaction and revenue, underscoring the financial payoff of more satisfied customers. Qualitatively, customers in 2024 enjoyed more personalized, convenient and engaging banking services than ever before—from receiving tailored financial advice in-app to getting instant help from a friendly AI assistant at midnight. Use cases like generative AI chatbots went from novelty to mainstream, with major banks in the US and UK launching or refining such tools. Source: Capgemini, Shaping the AI-enabled customer experience for financial services Of course, the transition was not without challenges. Banks had to address customer trust and security concerns around AI. Surveys revealed that while people welcomed convenience, many remained cautious about AI handling their finances: over 57% of customers hesitated to use AI-generated financial advice, and a minority outright refused to use AI for banking tasks. Incidents of AI errors (or 'hallucinations') were taken seriously, and institutions implemented guardrails and testing to maintain accuracy. The human element also proved vital—leading banks found the best outcomes by combining AI efficiency with human empathy, ensuring that customers could seamlessly escalate to a person when needed. Regulatory and ethical considerations started to surface as well, prompting discussions on transparent AI algorithms and data privacy. Nonetheless, the overall impact in 2024 was clearly positive. Banks that embraced AI saw improved customer engagement, whether through more frequent app usage or deeper emotional connection (feeling that the bank 'understands me'). In markets like the US and UK, which we highlighted, AI became a key differentiator among competitors: those who leveraged it wisely reaped reputational gains, while those who lagged faced pressure to catch up. In conclusion, AI's adoption in banking during 2024 fundamentally elevated the digital customer experience. By delivering personalization at scale, enabling full-service banking at customers' fingertips, engaging users in proactive conversations, automating support and broadening accessibility, AI made banking more customer-centric than ever. The past year's advancements indicate that we are entering a new era of digital banking—one in which AI is embedded in every facet of the customer journey, often invisibly, making interactions smoother and more meaningful. As we move into 2025 and beyond, banks will likely build on this momentum. We can expect even smarter virtual assistants, more predictive financial wellness tools and further integration of AI into everyday banking operations. The competitive bar for digital CX has been raised: customers will gravitate toward banks that continue to innovate with AI in ways that genuinely improve their financial lives. The 'age of AI' in banking has truly begun, and 2024 will be remembered as the year that kick-started a transformation in how we all experience banking in the digital age.
