AI in finance: opportunities, risks and what’s next

Artificial intelligence is impacting nearly every space, but few sectors are as poised for complete transformation as the finance industry. The potential to use AI for everything from fraud detection and compliance to delivering personalised customer experiences means leaders face a once-in-a-generation opportunity to redefine their organisations.

Consumers see these changes on the horizon; 76% believe AI will be standard in the financial services industry in the next five years, as per our Connected Financial Services Report .

This illustrates two things: Customer expectations are increasing, and finance leaders need to move quickly to turn AI into a competitive advantage.

The positive news is that businesses that adapt now and win the race will be in a prime position to build trust with customers, drive operational efficiency, and future-proof their growth. In this guide, we’ll show you six AI applications in finance, discuss some of the challenges of AI, and create a roadmap to help you implement with confidence.

Financial institutions are facing pressures that make AI adoption a strategic necessity rather than a nice-to-have. Here are some of the reasons the shift is becoming non-negotiable:

• Rising fraud risks: Fraudsters are becoming increasingly sophisticated, meaning traditional detection methods are continually falling short.
• Regulatory pressures: Increasingly complex regulations require more precise reporting and risk modelling, with heavier penalties for noncompliance.
• Fintech: Agile fintech companies are reshaping the industry with better personalisation and faster innovation cycles, challenging traditional institutions.
• Customer expectations: Up to 60% of consumers expect a given financial services task to be fully automated; the benchmarks for customer engagement are moving.

AI can be helpful in each of these areas. For instance, it supports anomaly detection in real time, reducing fraud risks. It generates business-ready insights, facilitating smarter decision-making. It can also power service solutions and recommendation engines, helping organisations deliver better experiences to customers.

And it can do all of this in less time, giving finance experts more time to work on the tasks that drive genuine value.

In essence, incorporating AI is increasingly essential for any finance business that wants to lead in an industry being redefined by technology. Leaders need to adapt now or risk falling behind.

So, what options are available to you? We’re going to cover six innovative applications of AI that finance businesses can use to spearhead their digital transformation efforts:

1. Fraud detection and risk management
2. Personalised banking
3. Credit scoring and financial inclusion
4. Algorithmic trading and investment management
5. Workflow automation and compliance
6. Forecasting and financial modelling

Let’s take a look at each.

The total cost of card fraud in Australia hit $913 million in 2024. That number represents a 20% increase and illustrates a growing threat to consumers and financial institutions.

AI can offer a solution here by monitoring transactions and leveraging predictive analytics to identify suspicious behaviours before they become a problem. And it can do all of this in real time, at a scale and speed that was previously impossible.

For instance, you could apply AI-powered monitoring to have your model:

• Monitor transactions for unusual activity, such as sudden high-value transfers or atypical login behaviour
• Assess the likelihood of fraud in loan applications or insurance claims so teams can prioritise the highest risk cases efficiently
• Detect anomalies in operational processes to reduce financial exposure and compliance errors

Aside from the technical benefits, AI also gives financial organisations the tools to investigate claims faster. With 77% of consumers reporting that they’re interested in AI that prevents and detects fraud, it’s a prime area for building customer trust. Investing in AI both safeguards your assets and gives customers confidence that your security can protect theirs.

Customers today want personalised experiences. That’s true for any industry, but expectations are even higher in the finance sector because of the need for complete trust.

AI personalisation makes this kind of hyper-tailored banking possible by helping financial institutions deliver customised offers and services at scale. This enhances the customer experience while saving time that reps can use to prioritise complex cases.

For instance, you could apply AI technologies to:

• Analyse customer habits and preferences at scale to suggest targeted product offers, such as credit cards, loans or investment products
• Identify spending patterns to provide proactive financial advice and potential savings opportunities
• Deliver personalised recommendations for wealth management or insurance coverage based on a customer’s risk appetite and goals

In addition to delivering personalised experiences, AI can also help to speed up services for customers, a common sticking point when competing with leaner fintechs.

For example, RBC Wealth Management partnered with Salesforce to cut onboarding times from weeks to just 24 minutes on average, improving customer satisfaction and freeing up time for reps.

In the past, traditional scoring models that rely on fairly narrow financial histories have limited access to credit. This leaves underserved communities excluded; it can also hurt traditional lenders in competition with more agile fintechs. That’s where AI comes in.

AI can process massive datasets at speed, which gives financial institutions the ability to include alternative data in their loan criteria without worrying about it bringing assessment processes to a halt. Think payment histories for utilities, rent, and mobile bills, or patterns in everyday transactions that show financial reliability, even without a traditional credit record.

The benefits here are clear. AI models have been shown to increase loan approval rates , supporting financial inclusion. In particular, credit unions that implemented one AI model experienced a 40% increase in credit approvals for women and people of colour .

Markets are moving faster than ever, which makes it increasingly hard for manual financial analysis to keep pace with the complexities of global trading.

AI gives financial institutions a way to process massive datasets in real time. It can identify trends and subtle patterns that aren’t visible to human analysts and, as a result, provide trading opportunities that may not have been visible. For instance, the right AI tools could:

• Analyse historical market trends to identify patterns faster
• Track real-time market signals and sentiments to find new opportunities proactively
• Automatically rebalance portfolios to minimise risk exposure
• Run predictive models to test investments in different market scenarios

This opens the door to faster insights, more opportunities for profitability, and more astute risk management. It should come as no surprise that 91% of asset managers are already using AI or planning to use it in 2025.

Manual compliance processes like AML checks and KYC verifications are slow, prone to errors, and frustrating to customers, who often have to share the burden of a disjointed onboarding experience with staff who are struggling to keep up with requests.

The key issue? Forty-seven per cent of customers say they have to repeat or re-explain information to different representatives when communicating with financial institutions (Connected Financial Services Report ). Slow, manual processes aren’t just a time sink; they actively harm the quality of your service.

AI offers a solution by automating regulatory compliance and basic checks. For instance, tools like Agentforce can handle digital labour like:

• Verifying data against predefined rules to automate KYC and AML checks
• Flagging unusual transactions for manual review
• Compiling and verifying documentation to simplify audit preparation

This speeds up compliance and ensures customers aren’t kept waiting for basic checks to finish. It also takes the burden off of frontline teams, giving them more time to focus on providing exceptional service.

Reacting to shifts after they happen isn’t sufficient when operating in such a volatile market. Financial institutions need to look forward just as much as they look back.

Sixty-three per cent of banks agree that AI improves strategic planning and forecasting. By analysing massive datasets in real time, it can identify trends, evaluate outcomes under different market scenarios and run simulations to guide the next best decision. In essence, it’s become the industry’s crystal ball.

All of this transforms forecasting from a reactive task into a proactive strategy. It lets organisations better anticipate risks and altogether make smarter decisions faster than those that are still relying on legacy methods.

As you might imagine, AI adoption isn’t a cake walk. To ensure a smooth integration, financial institutions still need to manage several challenges.

The good news is that all of these challenges are avoidable with some careful planning. Let’s get into each of the issues and then explain how to mitigate them.

• Algorithmic bias and fairness: AI models learn from historical data, which can inadvertently lead to biases. Institutions need to test their models proactively and refine them constantly to ensure fair outcomes for all customers.
• Black box AI and explainability: AI models are incredibly complex, which can make it difficult to remain transparent with customers and compliant with regulations. For this reason, financial institutions need to prioritise explainable, responsible AI that clearly shows how decisions are made and why outcomes are justified.
• Data protection and privacy: As ever, data privacy and security are top concerns. Financial institutions handle sensitive data, so they need to be confident that their AI models are secure and ethical. Before you establish your AI, be sure you have a strong foundation of security protocols and ethical practices in place.
• Regulatory uncertainty: Rules around AI are still evolving as the Australian Government discusses the best way to navigate its transformative powers. Finance leaders need to stay ahead by monitoring updates closely and documenting their AI decision-making to align with current and expected requirements.

While AI does present its challenges, we’re also seeing how AI solutions can help keep businesses on the right side of trust and regulations. For instance, Salesforce AI gives businesses the tools for explainable and compliant AI deployment, helping organisations mitigate risks while getting the full benefits of AI automation and data analytics.

As you’ve seen throughout this article, AI has progressed from being an experimental tool to an urgent priority for finance businesses.

The key to success isn’t rushing in with flashy tools but in building clear, trusted frameworks that can scale and mitigate risks. Here’s a practical roadmap for executives looking to take the leap with confidence.

1. Define your AI priorities: What are you hoping to achieve? Set out clear goals to identify the areas where AI can deliver the most value. Maybe fraud detection is your biggest concern. Or you can focus on customer personalisation. It’s better to start with one area that you perfect first before expanding outward with time.
2. Build your data foundation: AI is only as good as its data. Create a single source of truth with the help of tools like Salesforce Data Cloud. This solution will unify all of your information from your systems, databases and CRMs in one place, giving you a complete view of your customers and business.
3. Pilot quick wins: From there, smart small to show the value of your AI systems early. Aside from propelling your early successes, these quick wins will also help get customers and teams on board as you move toward more complex AI use cases.
4. Establish governance and test guardrails: Create clear policies for ethical AI and compliance to protect your organisation. You should also invest heavily in model transparency and explainable AI. Ultimately, your customers need to be able to understand what your AI does and the outcomes it delivers.
5. Scale with workforce reskilling and ROI tracking: As you start to ramp up your AI efforts, remember to train teams so they’re familiar with each tool you introduce. You should also measure progress to prove successes. Tools like CRM Analytics and Tableau can help you gather data and present it in accessible visual formats.

Ultimately, winning the AI race starts with a commitment to digital transformation and long-term strategy, not just tools.

From fraud detection to hyper-personalised banking and smarter forecasting, the message is clear: AI maturity is no longer optional for financial businesses. But implementing successfully requires a strategy built on unified data, strong ethics and tools that you can trust.

Salesforce Financial Services Cloud is designed to help institutions adopt AI for finance responsibly and at scale. Our solution will give you a 360-degree view of your customers while keeping your AI compliant and transparent. This helps you make smarter decisions and future-proof with confidence in one of the world’s fastest-growing sectors.

Financial Services Cloud also integrates with the rest of the Salesforce ecosystem to deliver extensible solutions tailored to every stage of your CRM and AI journey:

• Data Cloud for unified, real-time customer data for valuable insights
• Agentforce for AI-powered digital labour that automates core processes
• CRM Analytics + Tableau for advanced insights and ROI tracking
• MuleSoft for seamless integration between legacy and modern systems

Together, these tools create a trusted foundation for AI governance, innovation and growth in the financial space. Try Financial Services Cloud for free today.