AI & How It Can Have Major Impacts In Financial Services

Artificial Intelligence has moved well beyond chatbots, search algorithms, and self-driving cars. Today, it sits at the very heart of financial services, shaping how we budget, save, and invest. From apps that categorise your spending habits to robo-advisors that build and rebalance portfolios, AI in financial services is democratising access to tools once reserved for professional wealth managers.

But this is not just a story of innovation. Finance is one of the most regulated industries in the world, and for good reason: people’s livelihoods are at stake. The rise of AI brings both extraordinary opportunities and significant risks. Financial data is among the most sensitive information you hold. So, while AI can make life easier, it also demands a careful, informed approach.

In this blog, we’ll unpack:

• The benefits of AI in budgeting and investment

• The apps and platforms leading the way

• The risks around privacy, security, and regulation

• Practical best practices to use AI tools safely

Why AI Matters in Financial Services

The financial sector thrives on data. Every card swipe, investment trade, or standing order adds to an enormous pool of information. Humans alone cannot possibly process this volume at speed. AI, however, is designed for exactly that.

Here’s why AI in financial services has become indispensable:

• Automation: Tasks like categorising expenses, checking transactions, or rebalancing portfolios happen without human input.

• Accuracy: AI reduces the likelihood of human error in data entry, fraud detection, or report generation.

• Efficiency: Chatbots and automated services handle simple requests, freeing human staff for strategic work.

• Speed: AI processes thousands of data points in seconds, identifying trends or risks instantly.

• Innovation: With predictive analytics, AI uncovers opportunities for smarter savings, tailored investments, and even personalised financial planning.

For both individuals and organisations, these qualities turn AI from a novelty into a necessity.

How AI Enhances Budgeting

• Automatic categorisation: Apps analyse and tag spending automatically — groceries, utilities, entertainment.

• Recurring expense prediction: AI spots patterns like monthly subscriptions or bill cycles.

• Behavioural nudges: Some tools use AI-driven chatbots to highlight overspending and suggest practical adjustments.

  
  

Examples:

• Mint (US) simplifies budgeting with automated categorisation and reminders.

• Cleo (UK/US) uses a conversational AI assistant to make budgeting feel like chatting with a friend.

• YNAB (You Need A Budget) focuses on goal-based budgeting and debt payoff planning.

Instead of merely showing numbers, these apps interpret habits giving users actionable insights. For instance, Cleo might say: “You’ve spent 20% more on eating out this month compared to last. Want me to set a reminder to cut back?”. This small but subtle acknowledgement has huge positive psychological consequences, as we have seen from other notification reminders on our mobile devices on £ spent on coffee or screen times on phones.

AI in Savings: Automating Discipline

One of the biggest challenges in personal finance is consistent saving. AI makes this easier by moving money automatically, based on real-time cash flow.

What AI Saving Apps Do

• Forecast potential shortfalls before they occur.

• Calculate “safe-to-save” balances, protecting essential spending.

• Transfer spare change or surplus automatically into savings accounts.

Examples:

• Plum (UK/EU) analyses cash flow and makes micro-savings automatically.

• Digit (US) uses predictive algorithms to move small amounts into savings without user effort.

  
  

These platforms remove the friction of decision-making. You don’t have to remember to save; AI quietly does it for you. Over time, this builds financial resilience. Now… automatic saving Protocols have been around in banking systems for a long time, but these apps go further than that. They can track and automate savings pots and calculate which investment bank will produce better returns for the individual, levelling the playing field for clients in unfavourable savings accounts.

AI in Investment: The Rise of Robo-Advisors

So.. whilst budgeting and micro-investments sound great, perhaps the most exciting application of AI in financial services is the rise of robo-advisors, digital platforms that manage investments automatically.

How Robo-Advisors Work!

• Assess your risk tolerance and investment goals.

• Build and diversify portfolios based on algorithms.

• Rebalance investments automatically as markets shift.

• Incorporate tax strategies like tax-loss harvesting (in some platforms).

Examples:

• Nutmeg (UK) is FCA-regulated and offers diversified portfolios and retirement planning.

• Betterment (US) focuses on automated investing and tax efficiency.

• Wealthfront (US) combines investment management with financial planning tools.

I guess the main function that stands out to me is that robo-advisors democratise access. Once, professional portfolio management was reserved for high-net-worth clients. Today, someone with just £100 can access a diversified, risk-adjusted portfolio at a fraction of the traditional advisory cost. Below are a few extra applications that may suit your needs and specifications, with some additional points to help you evaluate your decision.

Popular AI Finance Apps at a 'Glance'

Mint (US)

• Core features: Budgeting, bill reminders

• AI benefits: Automatically categorises spending and forecasts cash flow

• Risks: Shares data with third parties, limited investment functionality

Cleo (UK/US)

• Core features: a chatbot for budgeting, saving nudges, spending tips

• AI benefits: Engaging conversational assistant with personalised insights

• Risks: Advice may oversimplify complex finance; requires linking bank accounts

YNAB (Global)

• Core features: Goal-based budgeting, debt payoff planning

• AI benefits: Machine learning refines spending categories over time

• Risks: High subscription cost, manual learning curve, no investment support

Plum (UK/EU)

• Core features: Automated savings, investments, pension contributions

• AI benefits: Calculates “safe-to-save” transfers to maximise savings

• Risks: Investments still carry market risk; requires trust in automation

Digit (US)

• Core features: Automated micro-savings and bill payments

• AI benefits: Predictive algorithms move money without impacting essentials

• Risks: Fees may outweigh small savings, overdraft risk if predictions are off

Personal Capital (US)

• Core features: Transaction categorisation, financial reporting, budgeting insights

• AI benefits: Streamlines bill tracking and highlights cost-cutting areas

• Risks: Takes time to “learn” your habits before delivering full value

Betterment (US)

• Core features: Automated investing, tax-loss harvesting, retirement accounts

• AI benefits: Personalised portfolios with tax optimisation strategies

• Risks: Limited customisation for advanced investors, long-term reliance on automation

Wealthfront (US)

• Core features: Automated investment management, financial planning tools

• AI benefits: AI-powered forecasting and savings allocation

• Risks: Still exposed to market downturns, minimal access to human advisors

The Risks of AI in Financial Services

The benefits are clear, but so are the risks. Finance is not an area where mistakes can be brushed aside. We have written about this in another blog that you can find here with 'AI going terribly wrong'. The other underlying issue there is that the financial services (similar to what AI is becoming) is so intertwined with every fabric of society. It's borderless; the boundaries are now totally digitised and instantaneous, which means when an issue arises… it arises fast. Here are a few areas which I think need some serious consideration when integrating AI into the financial sector:

Data Sensitivity

Financial information is prime for cybercriminals. A compromised budgeting app can expose salary details, account numbers, and spending habits.

Third-Party Sharing

Many apps monetise through partnerships, sharing anonymised (or semi-anonymised) user data. Always read the privacy policy before linking bank accounts, as this is now an extremely profitable industry.

Explainability

Some AI-driven systems operate as “black boxes”. You may never know why your robo-advisor moved your portfolio in a certain direction. This lack of transparency can erode trust or, quite simply, just be pulling data sets from the wrong pool of information.

Regulation Gaps

Banks are highly regulated, but fintech startups often operate in less scrutinised spaces. If something goes wrong, your rights and protections may not be as strong. It's common for startups to use cutting-edge tech to get ahead of their established competitors but this also brings with it the 'grey areas' of technology compertition where certian, unestablished techniques and resources are implemented to ill effect. The result is almost always at the end users peril where the startups regulation issues negatively impact the early adopters.

Best Practices for Users

So for me….if you are considering adopting AI-powered budgeting or investment apps, keep these extremley important safeguards in mind:

1. Choose regulated apps — look for FCA (UK), SEC (US), or EU regulation.

2. Enable two-factor authentication (2FA) to protect against unauthorised access.

3. Review permissions regularly to ensure apps aren’t overreaching.

4. Confirm encryption — ensure the provider uses bank-level data security.

5. Check compliance with GDPR or equivalent standards for your region.

By following these steps, you can take advantage of innovation without exposing yourself to unnecessary risk. I am in no way suggesting this is a bullet proof approach but with the right protocols in place you can certianly mitigate future problems by following these steps.

Balancing Innovation with Responsibility

The promise of AI in financial services is empowerment. It offers clarity on spending, encourages discipline in saving, and provides access to investment opportunities that were previously out of reach. But trust is not automatic.

A simple framework to remember:

• If an app feels too intrusive, it probably is.

• If you can’t explain how it manages your money, ask questions.

• If transparency is missing, consider alternatives.

Innovation is only meaningful when paired with accountability!

As always, I encourage you and anyone who has the capacity in your business to spend at least one hour a day researching and interacting with these technologies as they develop. The progress is extraordinarily quick, and it's vital to not get left behind. If you are not sure where to start… then contact Projekt Rising!

About Projekt Rising

Find out more: Contact the Projekt Rising team to learn how mobile AI and automation tools can be adapted to your industry. Our experts can help you understand the practical uses of AI now and prepare for the more advanced capabilities on the horizon. Alternatively, please see our case studies to learn how we have helped many brands improve their time management and efficiency using our AI toolkit.