
How Financial Services Are Using AI in 2025
Explore how AI in finance is helping financial services improve efficiency, prevent fraud, and deliver smarter, more personalised client experiences.
Explore how AI in finance is helping financial services improve efficiency, prevent fraud, and deliver smarter, more personalised client experiences.
The financial services sector has always been an early adopter of emerging tech. For years, they’ve used new technology to detect fraud, build apps for money management, and monitor the stock market in real time.
Artificial Intelligence (AI) is the next evolution of financial services and perhaps the biggest shake-up since online banking. With AI as a key strategic partner, firms can focus on providing value to their clients in a myriad of ways, rather than processing routine requests.
Today, we’re going to look at how Australian-based financial services are using AI, where they are seeing success, and dive into data and insights from our Connected Financial Services Report, which surveyed more than 9,500 global consumers on AI, digital engagement, and customer loyalty.
Before we delve into how these applications work (and how finance teams are finding success), here are nine ways AI is already supporting the financial services industry.
Fraud is a significant, ongoing global risk, and no one is immune. In Australia, cybercriminals infiltrated multiple superannuation providers using stolen data and drained $500,000 .
Fraud tactics are always changing to sneak around protective measures, making it harder for traditional systems to keep up. Using AI, financial institutions can learn the behaviour of fraudsters in real time and step in to alert a human who can properly assess the situation.
Here’s an example of this shift: Four of the biggest banks in Australia adopted an AI tool called BioCatch Trust. This tool works by monitoring small behaviours like typing speed or mouse movement to spot risks before a transaction is approved.
Not only are AI technologies helping financial institutions get ahead of fraud, it’s a use of AI that consumers support. In our latest Connected Financial Services Report, we found that 77% of consumers are interested in AI that prevents and detects fraud.
Risk management has always been a top priority in financial services, but it’s never been simple. People aren’t spreadsheets. One rough year doesn’t mean someone is a long-term risk; a perfect credit score doesn’t guarantee future stability.
This is where AI can help, particularly for time-consuming and risk management-heavy tasks such as applying for home loans.
Recently, a representative from ANZ shared that they’ve started using AI to support the approvals process, already reducing some tasks from several hours to seconds.
We’re also seeing new tools emerge, like local SaaS platform LoanOptions.ai , which uses AI to create a more complete, real-time view of someone’s financial risk. With AI, credit scoring is no longer determined by a checklist of past repayments. The process can now take into account spending patterns, income consistency, and broader economic conditions.
Gerard Florian, ANZ Group Executive, Technology and Group Services, shared his experiences working to enhance bank processes using AI at the Agentforce Financial Services Summit. “There are a lot of different tasks and some particular areas to focus on,” he said, “and being mindful of the risks of getting a balanced conversation is helping us build momentum for what is probably the single biggest change program we will all go through over the next few years.”
We’ve all been on the receiving end of a frustrating service call. It’s even worse when you get disconnected and have to bring a new representative up to speed. We found that 47% of consumers often have to repeat or re-explain information to different support agents.
Now, with natural language tools like Salesforce’s Agentforce, representatives no longer need to scroll through unclear notes. Instead, a generative AI agent can surface the most relevant information using natural language understanding, and it can do this while your team is live on a call.
Expert Wealth, an Australian financial services company, uses Agentforce to transform client acquisition and onboarding. Agentforce helps advisors gather client insights to prepare for their first meeting, saving valuable time and helping advisors to better prepare for that all-important first client contact.
Employing Agentforce has reduced the touchpoints required to qualify leads and schedule meetings by 50% and also helped to increase the efficiency of onboarding by 30%, reducing the risk of leads dropping out of the process.
“Advisors can have more meaningful conversations with new clients because they know what the clients want ahead of time and can even share a draft preview of what they can achieve. This is extremely powerful and wasn’t possible until the second or third meeting in the past,” said Paris Bisley, Co-Founder and Financial Advisor, Expert Wealth.
We’re also seeing more organisations use AI to triage calls and escalate only the ones that need complex human support. One example of this is Team Medical, a medical supply company who are planning to reduce their call load by 25% using Agentforce.
Fast, accurate customer support remains key to delivering strong experiences for consumers. For every generation (with the exception of Gen Z), we found that it ranked as the number one place customers want to receive a personalised experience from their financial institution.
Tax management has never been the most exciting part of financial services. However, getting it wrong can have serious consequences for clients and businesses. That’s why more accounting professionals are turning to artificial intelligence tools like Accounting AI CRMs to support their mundane tasks and allow them more time to add value for their clients.
Even the Australian Taxation Office (ATO) is now using artificial intelligence to process large volumes of complex tax data to surface insights and support front-line staff. It’s also being used in myTax to provide real-time prompts and help Australians get answers to more complex tax questions. (These are the same questions that would have previously required a phone call.)
In the private sector, Deloitte partnered with AI platform Kortical to automate a core part of their tax process using machine learning. They reported a reduced processing time (five hours down to six minutes) and a 50x productivity boost while maintaining more than 90% accuracy.
See how Agentforce helps financial services teams resolve routine cases faster, surface accurate answers, and deliver customer support.
Upskilling is essential for financial services businesses. While a client might engage your services for the end of the financial year or tax guidance, there are other ways you can make your services more sticky. Upselling to existing customers can yield almost five to 25 times more profit than acquiring a new one. However, it's difficult to do this when you’re not sure what your client might actually want.
From insights gleaned in our latest report, 93% of financial services leaders said they aren’t getting enough value from their data. That’s where generative AI sales agents like Agentforce can use your customer data to surface insights and personalised product recommendations to help teams better meet client needs, when and where it matters most.
For example, if your AI agent alerted you that your client was about to receive a large tax return, you could proactively recommend a savings product, an investment account, or a superannuation contribution strategy tailored to your client’s financial goals.
Investing has long relied on algorithms and data analytics to determine the best returns. Using AI, investment teams are able to analyse large data sets, reduce bias, and – ultimately – make more informed decisions.
State Super , which manages $37 billion in assets for the NSW government, is now using AI to guide asset allocation and summarise economic historical data. One of these tools scans global reports for executive insights, while another applies reinforcement machine learning to adjust for equity and currency differences.
Portfolio management solutions platform LENSELL has also launched an AI-driven asset allocation service for financial professionals. In the past year, users saw an average CAGR increase of 5.32% using its optimised recommendations. This is a huge difference for their customers.
Compliance has always been one of the most resource-heavy parts of financial services. Sifting through pages of ever-changing guidelines and documentation is time-consuming and poses a significant risk if something’s missed.
That’s why financial services are eager to simplify the process and surface regulatory compliance risks earlier using predictive models. In its Financial Stability Review , the Reserve Bank of Australia highlighted that well-designed and properly tested artificial intelligence tools can enhance financial stability, thanks to AI’s ability to consistently apply rules and detect risks before they escalate.
We’re also seeing new players like ESGagent.ai , an AI-powered SaaS platform built to simplify ESG reporting. Instead of teams wrestling with frameworks, the new platform is using AI to automate the tedious parts of ESG reporting, such as intelligent data collection and gap checks. This tool will help companies stay compliant as regulations inevitably change.
Stay ahead of the curve with expert takes on AI, customer expectations, and the future of finance.
Everyone's financial situation is unique. Because of this, it can be difficult for consumers to find helpful, applicable advice. AI can support financial services firms deliver timely and relevant advice to clients.
We found in our research of 9,500 financial service consumers worldwide that:
To meet this demand, a new wave of AI tools is helping personalise advice at scale – not just for clients, but for financial services teams themselves. Platforms like Financial Services Cloud can analyse spending patterns, track financial goals, and use AI agents to quickly answer questions about their finances.
Accountants didn’t go to school to do tedious admin tasks, yet admin can take up a large portion of their day. AI can help do the busy work and allow financial service firms to provide more expert advisory services.
Tools like MuleSoft’s automation suite are helping financial teams cut manual admin by connecting data analytics across systems and automating repetitive tasks. With low-code tools, teams can automate parts of their workflow, such as document processing and onboarding.
Clients don’t like waiting around for routine requests, either. This is especially true for routine requests like getting an official financial statement. When we surveyed 9,500 consumers, we found that more than half of all consumers expect most tasks to be fully automated and able to be completed online.
On top of improving the client experience, it saves money to have AI do routine tasks while salaried employees focus on bigger clients or more complex requests.
Get insights from 9,500 consumers worldwide on how AI is reshaping trust, service, and expectations in the financial sector.
As the future of AI unfolds, it's already transforming financial services, helping firms detect fraud in real-time, deliver always-on customer support, simplify compliance, personalise advice, and cut back on office admin. Staying ahead means not only using AI systems today but also preparing for the future of AI.
Are you curious how Salesforce can support you in adopting and implementing AI? Explore Financial Services Cloud to unify your data management, automate your workflows, and deliver more personalised client experiences at scale.
For deeper insights, download the Connected Financial Services Report (2nd Edition) to see what 9,500 consumers say they expect from financial institutions in the age of AI.
If you’d like to see the latest innovations in AI, take a look at our Agentforce FINS Summit 2025 Keynote on-demand webinar or watch our past live streams on Salesforce+.
Gen AI is helping financial institutions reduce costs by automating manual business processes like document processing, customer onboarding, and compliance tasks. At the same time, tools like conversational AI and natural language processing are improving customer engagement through faster, more personalised interactions.
Examples of AI used in finance include deep learning models for credit decisions, predictive models for market forecasting, and AI systems that analyse large amounts of data to personalise investment management strategies. AI is also helping detect anomalies that may signal financial risk.
AI tools can analyse market conditions, assess historical data, and generate insights from customer data to support better financial planning and investment management. This helps firms proactively adapt to shifts in the financial markets and provide tailored advice to clients.