Smarter Fraud Detection: What Canadian Lenders Need to Know

Abstract: AI agents can help financial institutions detect suspicious transactions, identify complex fraud patterns and automate parts of an investigation. By analysing transaction histories, device information and customer behaviour together, they can also help reduce false positives and strengthen identity verification. For Canadian lenders, their effectiveness depends on reliable data, appropriate permissions, clear oversight and compliance with applicable requirements.

We all know that fraud is becoming more complex as banking and financial transactions move online. Financial institutions and fintechs are well acquainted with the challenges of credit card scams, identity theft and account takeovers. The question is how AI agents can strengthen the systems already in place to protect customers and limit losses.

The FBI’s 2024 Internet Crime Report recorded more than US$16.6 billion in reported losses, a 33 per cent increase from 2023. The figures underline the importance of identifying suspicious activity quickly and preventing financial harm.

Traditional fraud detection relies on established rules and manual analysis. Both remain important, but they can struggle to identify unfamiliar fraud patterns and assess large transaction volumes in real time.

AI agents can strengthen these processes. They analyse data, identify abnormal behaviour and carry out tasks within defined permissions and oversight. Their role can extend beyond generating an alert to coordinating an investigation and taking authorised action.

What AI agents do

AI agents are software systems that analyse information, make decisions within specified limits and take action to achieve defined objectives. In financial services, they use machine learning, behavioural analysis, natural language processing and predictive analytics to evaluate transactions and help prevent fraud.

Consider a customer who usually makes small, local purchases but suddenly begins making large transactions abroad. An agent can assess the transaction history alongside location, device and behavioural information to determine whether the activity is suspicious. Depending on its permissions, it can request additional authentication or initiate other authorised security measures.

This ability to assess context and coordinate subsequent steps distinguishes agents from systems that simply issue alerts.

Detecting fraud in real time

Banks process thousands of transactions per second. Reviewing each one manually is impractical, while rule-based systems may miss suspicious activity that falls outside known patterns.

The Association for Financial Professionals’ 2025 Payments Fraud and Control Survey found that 79 per cent of organisations experienced attempted or actual payments fraud in 2024.

AI agents can assess transaction amounts, frequency, merchant information, devices and customer behaviour as payments occur. When they identify suspicious activity, they can escalate the case or prompt additional authentication.

For example, an account sending funds to several unfamiliar beneficiaries may warrant examination as a sequence of related transactions. Assessing those transfers together can reveal risks that are less apparent when each payment is reviewed separately.

Identifying complex fraud patterns

Fraudsters use combinations of genuine and stolen identities, legitimate accounts and fabricated accounts. Their changing methods make it difficult for institutions to keep pace.

AI agents can examine relationships between accounts, transactions and devices to expose connections between fraud actors and their tactics. Signals that initially appear unrelated may become suspicious when assessed together, helping institutions identify fraud rings and networks.

AI-enabled systems can flag these cases for further investigation, supporting efforts to resolve cases or interrupt fraud in progress. Learning algorithms can also incorporate feedback to improve detection, provided the models are properly tested.

By identifying patterns that earlier systems could not detect, agents can help institutions respond more quickly to evolving fraud methods.

Reducing false positives

False positives occur when legitimate transactions are classified as fraudulent. They can delay payments, disrupt customers and increase the workload for fraud teams.

A customer travelling abroad and making a large purchase, for example, may trigger an alert despite being the legitimate account holder. AI agents can assess contextual signals, including the device used, changes in location and previous spending habits, to help distinguish unusual customer behaviour from fraud.

Rather than relying on a single transaction, the system considers several signals and recommends an appropriate response. This can help institutions reduce unnecessary reviews while allowing legitimate payments to proceed.

The objective is to identify actual threats accurately while keeping unnecessary customer disruption to a minimum.

Strengthening identity verification and account security

Identity theft and account fraud remain significant problems in online banking. Criminals may use stolen personal information or false documents to gain access to an account. AI-generated deepfake audio and video add another challenge to identifying unauthorised access.

With appropriate data and training, AI agents can support identity verification, device analysis and behavioural monitoring. An agent might flag an unusual login, for instance, and assess it alongside subsequent activity.

If someone accesses an account, changes beneficiary details and attempts to transfer money, the agent can consider those events together. Depending on the assessed risk and its authorised permissions, it can initiate further verification.

Correlating these signals can help institutions assess potential threats more effectively.

What lenders should assess

Implementing AI agents also creates challenges that institutions must address:

• Data security and privacy. Fraud detection uses sensitive financial and personal information. Institutions need secure technology, appropriate access controls and sound data-handling practices.
• Accuracy and bias. AI can generate incorrect alerts or miss suspicious activity. Inadequate or incomplete data can also introduce bias.
• Transparency. Institutions need to understand why an agent flagged a transaction or recommended an action.
• Operational risk. Excessive permissions can allow an agent to disrupt legitimate transactions or take inappropriate action. Clear limits and human involvement in decision-making are essential.
• Compliance. AI-assisted fraud detection must comply with applicable financial regulations, privacy requirements and internal policies.

Putting agents to work

AI agents can support fraud detection by identifying suspicious transactions, connecting complex patterns, assessing false positives and automating parts of investigations. Their ability to analyse information from several sources can make fraud prevention more responsive to new threats.

They may also help strengthen identity verification and support compliance with anti-money-laundering requirements. For banks, fintech firms and payment service providers, implementing agents through AI agent development services can form part of a more sophisticated fraud prevention strategy.

Five key points

1. AI agents can combine detection, investigation and authorised action within defined limits.
2. Real-time analysis can assess related transactions together and identify complex fraud patterns.
3. Behavioural and contextual signals can help reduce false positives and customer disruption.
4. Combining identity, device and account activity can strengthen security assessments.
5. Effective implementation requires secure data, tested models, transparency, controlled permissions and human oversight.