7 key ways AI can help catch fraud in banking

In 2023-24, an estimated 14% of people aged 15 and older experienced at least one type of personal fraud, such as card fraud, scams, and identity theft. With these incidents on the rise, it’s clear there’s an urgent problem to address.

One issue is that most payments are now instant, leaving little time for manual review. Artificial intelligence (AI) is another concern, with hackers using it to automate sophisticated, large-scale attacks. This means traditional fraud detection tools are struggling to keep pace with the scope and complexity of attacks, often reacting too slowly to predict or prevent threats.

The good news? AI is also a formidable opponent on the other side of the battle. It can be employed as a proactive, adaptable security solution. The right AI tool can analyse vast quantities of data to spot anomalies, identify fraud patterns, and stop suspicious transactions before they ever materialise.

We’re going to look at seven ways AI is transforming banking fraud detection, along with real-world applications and challenges. We’ll also explore how platforms like Salesforce can empower financial institutions to stay ahead of fraud and deliver exceptional services to their customers.

This type of AI uses machine learning (ML) to find suspicious activity by analysing huge amounts of transaction data, customer behaviour, and device signals.

It’s a bit like an always-on radar system that constantly scans incoming activity for unusual patterns and anomalies. Here’s the process:

• Data collection: The bank continuously gathers data (such as transaction records, login activity, device fingerprints, and location information) to feed into the AI model.
• Real-time analysis: The AI model processes the raw information and extracts insights such as spending frequency, transaction sizes relative to history, and device trust scores.
• Behavioural profiling: AI learns what ‘normal’ looks like for individual customers or accounts (for example, how much they usually spend, where they’re based, and which devices they prefer).
• AI scoring: By comparing each customer’s baseline to the incoming data, the AI model checks for anomalies, like sudden high-value transfers or rapid location changes.
• The response: Transactions considered high risk are flagged, triggering automated holds, additional verification requirements, or escalations to fraud teams.
• Continuous improvement: The AI model learns from confirmations and false positives, re-tuning its anomaly detection criteria to become more accurate over time.

This adaptive, rapid approach helps banks catch subtle threats faster, reduce false positives, and prevent fraud more consistently than they ever could manually.

With the basics out of the way, let’s get down to details. Here are the seven core ways AI in fraud prevention can support the banking industry.

• Detecting anomalies in real-time
• Strengthening compliance
• Predicting risk
• Finding complex patterns
• Building behavioural biometrics
• Preventing fraud proactively
• Enhancing customer experiences and trust 

Let’s get into each of these methods and their benefits one by one.

AI can build a detailed understanding of what’s ‘normal’ for every customer and then scan transactions in milliseconds to find irregularities instantly. Think of out-of-the-ordinary data such as:

• Cards being used in another continent
• Logins from a new device
• Unusually large withdrawals
• Rapid microtransactions across multiple accounts
• Sudden changes in merchant types

If AI detects that any of these behaviours don’t line up with the customer’s baseline, it can flag or block the action immediately. Rather than reacting to threats after they occur, banks can now stop fraud in its tracks proactively.

This benefit is why 77% of consumers are interested in banks using AI technologies that prevent and detect fraud . We’ve also seen companies like PayPal put this approach to use , improving real-time fraud detection by 10% without impacting the customer experience.

The key foundation here is unified data, as this will ultimately be the information that helps AI detect anomalies with precision. Salesforce Data Cloud can aggregate all of your customer, transactional, and behavioural data in one place, in less time, creating a single source of truth for scalable fraud prevention and detection solutions.

Know Your Customer (KYC) and Anti-Money Laundering (AML) checks are essential. However, they’re also resource-intensive and prone to false positives when handled manually.

AI can help banks automate these processes, freeing up time while improving the overall accuracy of checks. For instance, it can:

• Detect anomalies in ID verification in real time
• Flag suspicious cross-border transfers
• Cross-reference customer data against global watchlists instantly

Aside from the time saved, this supports compliance with KYC/AML mandates, preventing regulatory penalties. And with 84% of customers claiming they would switch banks if they felt their information was mishandled , it’s clear that showing you’re committed to fraud detection is also a driver of trust.

Many financial institutions are already putting this kind of compliance automation into practice. As one example, Expert Wealth uses Financial Services Cloud and Agentforce to speed up regulatory workflows and reduce administrative burden. As a result, the finance brand has increased onboarding efficiency by 30%, freeing up time that staff can use to focus on client support.

AI can take fraud detection beyond a simple ‘yes/no’ check by assigning a probability score to every transaction. Predictive risk scoring works by analysing behavioural, device-based, and historical patterns and then calculating the likelihood that the activity is fraudulent.

For instance, it could flag a high-risk loan application based on inconsistencies in the data or monitor micro-transactions that indicate money laundering. The system will then compare this to a customer’s normal baseline to score each case based on how risky it is.

This helps analysts prioritise the highest-risk cases rather than sifting through false positives and low-stakes transactions, saving time and improving all-around decision-making. As a bonus, tools like CRM Analytics can make this AI use case even more powerful by delivering predictive dashboards that can surface easy-to-read risk scores and trends in real time.

Tackling fraud one case at a time may be the best immediate approach, but it isn’t a long-term solution. With global fraud losses surpassing $485 billion in 2023, banks need tools that go beyond case-by-case reviews if they want to prevent rather than just react to financial crime.

Graph and network analysis uses AI to map relationships between accounts, devices, and entities to find fraud patterns that are too complex for humans to spot.

For instance, it can:

• Uncover large-scale money mule networks
• Detect synthetic identities created with fake or stolen information
• Highlight clusters of fresh accounts making coordinated transactions

This gives banks the tools to see how individual fractions fit into organised schemes and to provide more accurate data to the relevant authorities, all while making fraud teams more efficient and strengthening compliance.

Fraudsters may have no trouble stealing people’s information and accessing their banking, but they can’t copy how individuals interact with their devices.

Behavioural biometrics leverage AI to learn the habits that make customers unique, such as how fast they type, swipe, or move their mouse. These behavioural habits create what is effectively a digital fingerprint for each user. This means that, even if a fraudster gains access to an account, the AI can still flag unusual behaviour for review if it falls outside of the customer’s normal patterns.

While behavioural biometrics alone are unlikely to be incriminating evidence, they add another piece to the puzzle when detecting fraud. Remote access scams alone accounted for $106 million in losses in 2024 , and many of these are hard to spot with traditional checks. AI can detect these subtle behaviours in real time to add critical context for fraud teams.

Another benefit of this technology is that it’s an ‘invisible’ layer of security, meaning it doesn’t add an extra authentication hoop for customers to jump through. It helps to stop fraud without ever disrupting the user experience.

A major challenge with fraud is that it evolves so quickly. What worked against it yesterday won’t necessarily work today. This is why AI’s ability to adapt over time is so powerful.

AI models can continuously refine their approach based on confirmed cases and false positives, feeding this data back into their algorithms to identify similar patterns faster and more accurately with time.

This allows AI to spot things we’d usually miss, such as:

• New phishing scam techniques based on historic communication patterns
• Suspicious crypto transfers that mimic characteristics of previous scams
• New scam payment flows that fall outside of regular fraud patterns

Fraud detection is a constant game of cat and mouse that financial institutions can win only by using the right approach. AI gives banks the tools to combat fraud before it escalates. As an example, CommBank’s Truyu Gen AI Scam Checker has reduced losses by 76% by spotting emerging fraud techniques earlier than manual methods.

The key here is to choose the right trusted AI solution that can adapt and scale alongside your financial institution. Platforms like Agentforce empower your business with intelligent agents that slot into your preferred use case, evolve in real time, and learn from every new case.

Fraud prevention isn’t just about stopping financial losses. It’s also about instilling customer trust. In addition to preventing fraud from occurring in the first place, AI can improve the user experience and keep customers confident in their banking.

For example, when AI detects suspicious activity, it can send a push notification to the customer’s phone explaining the threat and how the bank is preventing it. It could also guide the customer through a quick verification process without requiring a call for support.

When integrated into customer service platforms like Service Cloud, AI also results in much faster user experiences. In financial services, 46% of service cases are now solved through AI , helping customers get the support they need in less time, while saving more time for representatives to prioritise high-value communications.

All of this maximises security behind the scenes while minimising the number of unnecessary steps and blocks, improving the overall experience, building trust, and increasing retention over time.

Conventional fraud detection often relies on rigid rule-based systems, which are too slow when dealing with today’s evolving threats and high transaction volumes.

By contrast, AI is faster, more adaptable, and more precise, and it provides a better experience to customers. While AI won’t ever replace human fraud analysts, it frees up their time so they can focus on complex investigations rather than routine checks.

AI is powerful, but it’s not perfect. Let’s discuss some of the main challenges of AI fraud detection systems in banking, along with the steps you can take to mitigate them.

• Hallucinations and errors: AI models can misinterpret data or overflag legitimate transactions, especially when trained on incomplete datasets. Ongoing testing and retraining will help to minimise this risk as time goes on. Equally, businesses need to maintain supervised learning at every stage for accurate, explainable, responsible AI.
• Bias: If the AI training data is biased, the AI model will be biased, too. This could lead to discrimination against certain customers, transaction types, or regions. To avoid this, banks need to unify a diverse range of datasets and introduce audits and fairness checks to ensure decisions are consistent and transparent.
• Compliance: The Australian Government routinely reviews its regulations around data protection and privacy in the age of AI. Over time, legislation will continue to tighten, especially in high-risk industries like finance. Banks need strong governance frameworks and trusted technology patterns to stay compliant and maintain trust.

Thankfully, these potential hurdles can be overcome by committing to data quality, maintaining strong governance, and working alongside the right partners.

Salesforce helps financial services firms navigate these concerns with built-in trust layers and ethical guardrails, helping businesses harness AI with confidence while maintaining customer trust.

Transaction fraud is growing increasingly sophisticated, but AI offers a scalable, adaptive line of defence. It can analyse millions of transactions simultaneously, providing instant insights and real-time alerts, to help financial institutions balance speed with compliance and trust.

The future of fraud detection isn’t only about blocking fraud; it’s also about creating trusted digital banking that keeps customer data secure and experiences seamless. For that, you need a reliable solution that can adapt to your needs without putting extra friction on your customers.

Salesforce Financial Services Cloud, along with Data Cloud and Agentforce, help banks unify data, deploy intelligent fraud prevention, and deliver service experiences that keep customers coming back, all grounded in groundbreaking, trusted AI.

Try Financial Services Cloud for free today to see how we can help you kickstart your AI fraud detection strategy, better serve your customers, and stay one step ahead of evolving threats.