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Enhancing Transaction Monitoring Process in Banks

Tookitaki

9 min

read

In the rapidly evolving world of banking, transaction monitoring has become a critical component. It's a key part of risk management and compliance programs in financial institutions.

The primary goal of transaction monitoring is to identify suspicious transactions. These could indicate potential money laundering or terrorist financing activities. It's a complex task that requires sophisticated systems and strategies.

However, the landscape of financial crime is constantly changing. New methods of fraud and other financial crimes are emerging, posing challenges for financial crime investigators. Staying updated on the latest trends and technologies in transaction monitoring is crucial.

This article aims to provide comprehensive insights into enhancing transaction monitoring systems. It will delve into the latest trends, technologies, and best practices in the field. The focus will be on how these can be effectively implemented within financial institutions.

Whether you're a financial crime investigator, a compliance officer, or an anti-money laundering specialist, this article is for you. It's also for anyone interested in the latest developments in financial crime detection and prevention.

By the end of this article, you'll have a deeper understanding of transaction monitoring in banking. You'll also be equipped with actionable strategies to enhance your institution's transaction monitoring capabilities.

The Imperative of Transaction Monitoring in Modern Banking

In the modern banking landscape, transaction monitoring is no longer optional but a necessity. The increasing digitization of financial services has led to a surge in the volume and complexity of financial transactions.

This digital transformation has brought many benefits. It has made banking more convenient and accessible for customers. However, it has also opened up new avenues for financial crimes. Fraudsters are becoming more sophisticated, exploiting the anonymity and speed of digital transactions to carry out illicit activities.

Transaction monitoring plays a crucial role in detecting and preventing these activities. It involves analyzing patterns and trends in transfers, deposits, and withdrawals. By doing so, it can identify suspicious transactions that deviate from normal patterns. These could be indicative of money laundering, terrorist financing, or other financial crimes.

Here are some key reasons why transaction monitoring is imperative in modern banking:

Compliance with regulations: Financial institutions are required to comply with Anti-Money Laundering (AML) regulations, which include transaction monitoring requirements. Non-compliance can result in hefty fines and reputational damage.
Risk management: Transaction monitoring helps banks manage their risk by identifying potential threats and taking appropriate action.
Customer trust: By detecting and preventing financial crimes, banks can protect their customers and maintain their trust.
Operational efficiency: Advanced transaction monitoring systems can automate the detection of suspicious transactions, reducing the workload on the compliance team.
Competitive advantage: Banks that excel in transaction monitoring can differentiate themselves in the market, attracting customers who value security and integrity.

In the face of evolving financial crimes, transaction monitoring is a vital tool for banks. It's a key part of their defense against fraud and other financial crimes. It's also a critical component of their risk management and compliance programs.

Understanding the Regulatory Landscape: FATF and AML Regulations

The regulatory landscape for transaction monitoring is shaped by several key players and regulations. At the forefront is the Financial Action Task Force (FATF). This inter-governmental body sets international standards for combating money laundering and terrorist financing. Its recommendations are widely adopted by countries and financial institutions worldwide.

FATF's guidelines emphasize a risk-based approach to transaction monitoring. This means that banks should prioritize resources on higher-risk areas. These could be customers, products, or geographical regions that are more likely to be involved in financial crimes. By doing so, banks can enhance the effectiveness of their transaction monitoring efforts.

In addition to FATF, banks must also comply with local and regional Anti-Money Laundering (AML) regulations. These regulations often include specific requirements for transaction monitoring. For example, they may require banks to report suspicious transactions to the relevant authorities. Non-compliance with these regulations can result in severe penalties, including fines and sanctions.

Here are some key aspects of AML regulations that relate to transaction monitoring:

Customer Due Diligence (CDD): Banks must verify the identity of their customers and understand their normal transaction behaviour.
Suspicious Transaction Reporting (STR): Banks must report transactions that are suspected of being related to money laundering or terrorist financing.
Record-keeping: Banks must keep records of all transactions for a certain period, typically five years.
Risk assessments: Banks must conduct regular risk assessments to identify and mitigate their exposure to money laundering and terrorist financing risks.

Understanding the regulatory landscape is crucial for banks. It helps them design their transaction monitoring systems to comply with the relevant regulations. It also informs their risk assessments, guiding them on where to focus their monitoring efforts.

The Risk-Based Approach to Transaction Monitoring

The risk-based approach to transaction monitoring is a strategy that prioritizes resources based on the level of risk. This approach is recommended by the Financial Action Task Force (FATF) and is widely adopted by financial institutions worldwide. It allows banks to focus their efforts on areas where the risk of money laundering and terrorist financing is highest.

In a risk-based approach, banks first conduct a risk assessment. This involves identifying and assessing the money laundering and terrorist financing risks that they face. These risks can be associated with their customers, products, services, transactions, or geographical locations. The risk assessment informs the design and implementation of the bank's transaction monitoring system.

The risk-based approach is not a one-size-fits-all solution. It requires banks to tailor their transaction monitoring systems to their specific risk profile. For example, a bank with a high volume of cross-border transactions may need to implement more sophisticated monitoring techniques. On the other hand, a bank that primarily serves low-risk customers may be able to use a simpler system.

Here are some key steps in implementing a risk-based approach to transaction monitoring:

Risk Assessment: Identify and assess the money laundering and terrorist financing risks that the bank faces.
Risk Mitigation: Design and implement controls to mitigate the identified risks.
Risk Review: Regularly review and update the risk assessment and controls to ensure they remain effective.

The risk-based approach to transaction monitoring is a dynamic process. It requires continuous monitoring and updating to keep pace with changes in the risk landscape. This approach allows banks to stay ahead of the curve in the fight against financial crime.

Crafting a Customer Risk Profile: The Foundation of Effective Monitoring

Creating a customer risk profile is a crucial step in effective transaction monitoring. This profile is a comprehensive view of a customer's financial behaviour, including their transaction patterns, risk level, and potential red flags. It serves as a foundation for monitoring transactions and identifying suspicious activities.

The process of crafting a customer risk profile begins with customer due diligence. This involves collecting and verifying information about the customer, such as their identity, occupation, and source of funds. The bank also assesses the customer's risk level based on various factors, such as their geographical location, type of business, and transaction behavior.

Once the customer risk profile is established, it informs the transaction monitoring process. For example, a customer with a high-risk profile may trigger more frequent and detailed transaction reviews. On the other hand, a customer with a low-risk profile may require less intensive monitoring. This targeted approach helps banks to allocate their resources more efficiently.

In conclusion, a well-crafted customer risk profile is a powerful tool in transaction monitoring. It enables banks to understand their customers better, detect suspicious transactions more accurately, and ultimately, prevent financial crimes more effectively.

The Role of Artificial Intelligence in Transaction Monitoring

Artificial Intelligence (AI) is revolutionizing the field of transaction monitoring in banking. It offers advanced capabilities that can significantly enhance the efficiency and effectiveness of monitoring systems. AI can analyze vast amounts of data, identify complex patterns, and learn from past transactions to improve future detections.

One of the key applications of AI in transaction monitoring is machine learning. Machine learning algorithms can be trained to recognize patterns of fraudulent or suspicious transactions. Over time, these algorithms can learn and adapt, becoming more accurate in detecting potential financial crimes.

AI can also help to reduce false positives, a common challenge in transaction monitoring. By learning from past data, AI can distinguish between legitimate and suspicious transactions more accurately, reducing the number of false alarms. This can save significant time and resources for the compliance team.

Moreover, AI can enable real-time transaction monitoring. It can analyze transactions as they occur, providing immediate alerts of potential threats. This allows for quicker response and mitigation of risks.

Here are some ways AI can enhance transaction monitoring:

Improved detection accuracy through machine learning
Reduction of false positives
Real-time transaction monitoring
Enhanced efficiency by automating routine tasks

In conclusion, AI holds great promise for enhancing transaction monitoring in banking. By leveraging AI, banks can improve their ability to detect and prevent financial crimes, making the financial system safer for everyone.

Reducing False Positives: A Challenge for Financial Institutions

In the realm of transaction monitoring, false positives pose a significant challenge. These are alerts triggered by legitimate transactions that are mistakenly flagged as suspicious. False positives can consume valuable time and resources, as each alert must be investigated by the compliance team.

The high rate of false positives in traditional, rules-based transaction monitoring systems can be attributed to their lack of sophistication. These systems often rely on simple, predefined rules, which can result in many legitimate transactions being flagged. This not only burdens the compliance team but also can lead to customer dissatisfaction due to delays or interruptions in their banking activities.

Advanced technologies like AI and machine learning can help reduce false positives. These technologies can learn from past transactions and improve their accuracy over time. They can distinguish between normal and suspicious transaction patterns more effectively, reducing the number of false alerts.

Key strategies to reduce false positives include:

Implementing advanced technologies like AI and machine learning
Regularly updating and refining the rules and parameters of the monitoring system
Training the compliance team to better understand and interpret the alerts
Conducting regular reviews and audits of the transaction monitoring system to identify areas for improvement

By reducing false positives, financial institutions can enhance the efficiency of their transaction monitoring systems and focus their resources on genuine threats.

The Evolution of Transaction Monitoring Systems: From Rules-Based to AI-Enhanced

Transaction monitoring systems have evolved significantly over the years. Initially, these systems were largely rules-based. They relied on predefined rules or criteria to flag potentially suspicious transactions. While this approach provided a basic level of monitoring, it had its limitations. It often resulted in a high number of false positives and lacked the ability to adapt to changing patterns of financial crime.

The advent of artificial intelligence (AI) and machine learning has revolutionized transaction monitoring. These technologies can analyze vast amounts of data and identify complex patterns that may indicate fraudulent activity. They can learn from past transactions and improve their accuracy over time, reducing the number of false positives.

AI-enhanced transaction monitoring systems offer several advantages over traditional rules-based systems:

They can analyze and learn from large volumes of data, improving their accuracy over time.
They can identify complex patterns and trends that may indicate fraudulent activity.
They can adapt to changing patterns of financial crime, making them more effective in detecting new types of fraud.
They can reduce the number of false positives, freeing up resources for the compliance team.

The integration of AI into transaction monitoring systems represents a significant step forward in the fight against financial crime. As these technologies continue to evolve, they will play an increasingly important role in detecting and preventing fraud and other financial crimes.

Real-Time Monitoring: The Future of Transaction Analysis

The future of transaction monitoring lies in real-time analysis. This approach allows financial institutions to detect and respond to suspicious activities as they occur. It provides immediate alerts, enabling quicker responses to potential threats.

Real-time monitoring is particularly effective in identifying and preventing fraud. It can detect unusual patterns of behavior as they emerge, rather than after the fact. This proactive approach can significantly reduce the risk of financial loss and reputational damage.

However, implementing real-time monitoring requires robust systems and advanced technologies. Financial institutions must invest in the necessary infrastructure and tools to support this level of analysis. Despite these challenges, the benefits of real-time monitoring make it a worthwhile investment for any financial institution committed to combating financial crime.

The Compliance Team's Role in Transaction Monitoring

The compliance team plays a pivotal role in transaction monitoring. They are responsible for ensuring that the institution's monitoring systems are up-to-date with regulatory requirements. This involves staying abreast of changes in Anti-Money Laundering (AML) regulations and implementing necessary adjustments to the monitoring systems.

In addition, the compliance team is tasked with conducting regular risk assessments. These assessments help to identify and prioritize high-risk areas, informing the transaction monitoring process. The team's insights are crucial in refining the institution's risk-based approach to transaction monitoring.

Moreover, the compliance team is instrumental in fostering a culture of compliance within the institution. They conduct training and awareness programs to equip staff with the knowledge and skills to recognize and report suspicious transactions. In this way, the compliance team enhances the effectiveness of transaction monitoring and contributes to the institution's overall efforts to combat financial crime.

Best Practices for Implementing Advanced Transaction Monitoring Solutions

Implementing advanced transaction monitoring solutions can significantly enhance a financial institution's ability to detect and prevent financial crimes. However, the process requires careful planning and execution. Here are some best practices to consider.

Firstly, financial institutions should adopt a risk-based approach to transaction monitoring. This involves prioritizing resources on higher-risk areas, as identified through regular risk assessments. A risk-based approach allows institutions to focus their efforts where they are most needed, enhancing the efficiency and effectiveness of their monitoring systems.

Secondly, institutions should leverage the power of artificial intelligence and machine learning. These technologies can analyze vast amounts of transaction data, identify complex patterns, and generate alerts for suspicious activities. By reducing the reliance on manual processes, AI and machine learning can significantly improve the speed and accuracy of transaction monitoring.

Thirdly, institutions should strive to reduce false positives. False positives can drain resources and lead to unnecessary investigations. Advanced analytics and machine learning algorithms can help to fine-tune the monitoring systems and reduce the incidence of false positives.

Lastly, institutions should ensure that their transaction monitoring systems are integrated with other financial crime prevention tools. This creates a more robust defense against financial crimes and allows for a more holistic view of the institution's risk landscape.

In conclusion, implementing advanced transaction monitoring solutions is a complex process that requires careful planning and execution. By following these best practices, financial institutions can enhance their ability to detect and prevent financial crimes, ensuring compliance with regulations and protecting their reputation.

Conclusion: Staying Ahead in the Fight Against Financial Crime

In the ever-evolving landscape of financial crime, staying ahead is a constant challenge for financial institutions. Transaction monitoring plays a crucial role in this fight, serving as a powerful tool to detect and prevent illicit activities.

By leveraging advanced technologies, adopting a risk-based approach, and continuously refining their systems, institutions can enhance their transaction monitoring capabilities. This not only ensures compliance with regulations but also contributes to the overall stability and integrity of the financial system. The fight against financial crime is a collective effort, and effective transaction monitoring is a critical part of this endeavour.

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Compliance Hubs

17 Sep 2025

6 min

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The Investigator’s Edge: Why AML Investigation Software Is a Must-Have for Singapore’s Banks

In the fight against financial crime, detection is only half the battle. The real work starts with the investigation.

Singapore’s financial institutions are facing unprecedented scrutiny when it comes to anti-money laundering (AML) compliance. As regulators raise the bar and criminals get smarter, the ability to investigate suspicious transactions swiftly and accurately is now a non-negotiable requirement. This is where AML investigation software plays a critical role.

In this blog, we explore why AML investigation software matters more than ever in Singapore, what features banks should look for, and how next-generation tools are transforming compliance teams from reactive units into proactive intelligence hubs.

Why Investigation Capabilities Matter in AML Compliance

When a transaction monitoring system flags an alert, it kicks off an entire chain of actions. Analysts must determine whether it's a false positive or a genuine case of money laundering. This requires gathering context, cross-referencing multiple systems, documenting findings, and preparing reports for auditors or regulators.

Doing all of this manually is not only time-consuming, but also increases the risk of human error and compliance gaps. For banks operating in Singapore's high-stakes environment, where MAS expects prompt and well-documented responses, this is a risk few can afford.

Key Challenges Faced by AML Investigators in Singapore

1. Alert Overload

Analysts are often overwhelmed by a high volume of alerts, many of which turn out to be false positives. This slows down investigations and increases backlogs.

2. Fragmented Data Sources

Information needed for a single investigation is typically spread across customer databases, transaction logs, sanctions lists, and case notes, making it difficult to form a complete picture quickly.

3. Manual Documentation

Writing investigation summaries and preparing Suspicious Transaction Reports (STRs) can take hours, reducing the time available for deeper analysis.

4. Audit and Regulatory Pressure

MAS and other regulators expect detailed, traceable justifications for every action taken. Missing documentation or inconsistent processes can lead to penalties.

What AML Investigation Software Does

AML investigation software is designed to streamline, standardise, and enhance the process of investigating suspicious activities. It bridges the gap between alert and action.

Core Functions Include:

Case creation and automated alert ingestion
Intelligent data aggregation from multiple systems
Risk scoring and prioritisation
Investigation checklists and audit trails
Natural language summaries for STR filing
Collaborative case review and escalation tools

Must-Have Features in AML Investigation Software

When evaluating solutions, Singaporean banks should look for these critical capabilities:

1. Smart Alert Triage

The system should help investigators prioritise high-risk alerts by assigning risk scores based on factors such as transaction patterns, customer profile, and historical activity.

2. Contextual Data Aggregation

A strong tool pulls in data from across the bank — including core banking systems, transaction logs, KYC platforms, and screening tools — to provide investigators with a consolidated view.

3. Natural Language Summarisation

Leading software uses AI to generate readable, regulator-friendly narratives that summarise key findings, reducing manual work and improving consistency.

4. Audit-Ready Case Management

Every step taken during an investigation should be logged and traceable, including decision-making, reviewer notes, and attached evidence.

5. Integration with STR Reporting Systems

The software should support direct integration with platforms such as GoAML, used in Singapore for suspicious transaction reporting.

How Tookitaki's FinCense Platform Elevates AML Investigations

Tookitaki’s FinCense platform is designed with Singapore’s regulatory expectations in mind and includes a specialised Smart Disposition Engine for AML investigations.

Key Features:

AI Copilot (FinMate)
Acts as an intelligent assistant that helps compliance teams assess red flags, suggest investigative steps, and provide context for alerts.
Smart Narration Engine
Automatically generates STR-ready summaries, saving hours of manual writing while ensuring consistency and auditability.
Unified View of Risk
Investigators can see customer profiles, transaction history, typologies triggered, and sanction screening results in one interface.
Scenario-Based Insight
Through integration with the AFC Ecosystem, the system maps alerts to real-world money laundering typologies relevant to the region.
Workflow Customisation
Investigation steps, user roles, and escalation logic can be tailored to the bank’s internal policies and team structure.

Benefits for Compliance Teams

By implementing AML investigation software like FinCense, banks in Singapore can achieve:

Up to 50 percent reduction in investigation time
Enhanced quality and consistency of STRs
Faster closure of true positives
Lower regulatory risk and better audit outcomes
Improved collaboration across compliance, risk, and operations

Checklist: Is Your Investigation Process Ready for 2025?

Ask these questions to evaluate your current system:

Are investigators manually pulling data from multiple systems?
Is there a standard template for documenting cases?
How long does it take to prepare an STR?
Can you trace every decision made during an investigation?
Are your analysts spending more time writing than investigating?

If any of these answers raise red flags, it may be time to upgrade.

Conclusion: Better Tools Build Stronger Compliance

AML investigation software is no longer a nice-to-have. It is a strategic enabler for banks to stay ahead of financial crime while meeting the rising expectations of regulators, auditors, and customers.

In Singapore's rapidly evolving compliance landscape, banks that invest in smart, AI-powered investigation tools will not only keep up. They will lead the way.

Ready to take your AML investigations to the next level? The future is intelligent, integrated, and investigator-first.

The Investigator’s Edge: Why AML Investigation Software Is a Must-Have for Singapore’s Banks

Compliance Hubs

17 Sep 2025

6 min

read

Agentic AI in Compliance: The Secret Weapon Against Financial Crime

Agentic AI is reshaping compliance in Australian banking, delivering real-time intelligence and smarter investigations.

Introduction

Compliance has always been a balancing act. Banks and fintechs must detect suspicious activity, meet regulatory requirements, and protect customers, all while keeping costs under control. In Australia, where AUSTRAC has stepped up enforcement and the New Payments Platform (NPP) enables real-time transfers, the pressure on compliance teams has never been greater.

Enter Agentic AI in compliance. Unlike traditional machine learning, Agentic AI operates as intelligent agents that perform specialised tasks within compliance workflows. It is transparent, explainable, and adaptive, making it a powerful tool for anti-money laundering (AML) and fraud prevention. For Australian institutions, Agentic AI is not just the future — it is fast becoming a necessity.

What is Agentic AI in Compliance?

Agentic AI refers to artificial intelligence models designed to act autonomously as agents within a broader system. In compliance, this means AI tools that:

Detect suspicious activity in real time.
Adapt to new typologies and fraud schemes.
Support investigators with case summaries and recommendations.
Automate reporting in regulator-ready formats.

Unlike black-box AI, Agentic AI is explainable, meaning every decision can be justified to regulators such as AUSTRAC.

Why Compliance Needs Agentic AI

1. Real-Time Payment Risks

With NPP and PayTo, funds can move across accounts in seconds. Legacy systems cannot keep up. Agentic AI enables millisecond-level monitoring.

2. Alert Overload

Traditional systems produce high false positives. Agentic AI reduces noise, allowing compliance teams to focus on genuine risks.

3. Evolving Typologies

From mule accounts to deepfake scams, criminals are innovating constantly. Agentic AI learns from new patterns and adapts automatically.

4. AUSTRAC Expectations

Regulators require transparency and effectiveness. Agentic AI provides explainable alerts, audit trails, and regulator-ready reports.

5. Rising Compliance Costs

Staffing costs are high in Australia’s compliance sector. AI reduces manual workload and increases investigator efficiency.

How Agentic AI Works in Compliance

1. Transaction Monitoring

Agentic AI reviews transactions in real time, assigning risk scores and flagging anomalies.

2. Behavioural Analytics

Tracks customer behaviour across logins, devices, and transactions to detect unusual activity.

3. Case Investigation

AI copilots summarise cases, suggest next steps, and draft Suspicious Matter Reports (SMRs).

4. Continuous Learning

Agentic AI adapts from investigator feedback and new data, improving accuracy over time.

5. Federated Intelligence

Through networks like the AFC Ecosystem, Agentic AI incorporates insights from global compliance experts without exposing sensitive data.

Use Cases of Agentic AI in Compliance

Account Takeover Fraud: Detects unusual login and transaction activity in real time.
Authorised Push Payment (APP) Scams: Identifies high-risk transfers initiated under duress.
Mule Networks: Maps hidden links between accounts, devices, and transactions.
Sanctions Screening: Flags high-risk names or entities with contextual intelligence.
KYC/CDD Monitoring: Automates risk scoring of new and existing customers.
Regulatory Reporting: Auto-generates SMRs, TTRs, and IFTIs in AUSTRAC-compliant formats.

Benefits of Agentic AI in Compliance

Real-Time Detection: Protects institutions from losses and reputational damage.
Reduced False Positives: Saves investigators time and reduces operational costs.
Explainability: Provides regulators with clear reasoning for alerts.
Efficiency: Automates routine investigation tasks.
Scalability: Works for both Tier-1 banks and smaller institutions.
Customer Trust: Demonstrates proactive protection against fraud.

Challenges in Deploying Agentic AI

Data Quality Issues: Poor data reduces AI accuracy.
Integration Complexity: Legacy systems make implementation difficult.
Skills Gap: Few compliance teams have in-house AI expertise.
Cost of Adoption: Smaller institutions may struggle with upfront costs.
Change Management: Teams need training to trust and use AI effectively.

Case Example: Community-Owned Banks Adopting Agentic AI

Community-owned banks such as Regional Australia Bank and Beyond Bank are showing how Agentic AI can be deployed effectively. By adopting advanced compliance platforms, they have reduced false positives, improved reporting, and enhanced their ability to detect mule networks in real time.

These banks prove that Agentic AI is not only for Tier-1 players. With the right platform, even mid-sized institutions can benefit from AI-driven compliance innovation.

Spotlight: Tookitaki’s FinCense

FinCense, Tookitaki’s compliance platform, integrates Agentic AI to deliver end-to-end compliance and fraud prevention.

Real-Time Monitoring: Detects suspicious activity across NPP, PayTo, remittance corridors, and crypto.
Agentic AI Models: Continuously adapt to new money laundering and fraud patterns.
Federated Intelligence: Draws from typologies contributed by the AFC Ecosystem.
FinMate AI Copilot: Summarises alerts, recommends next steps, and drafts regulator-ready reports.
AUSTRAC Compliance: Automates SMRs, TTRs, and IFTIs with complete audit trails.
Cross-Channel Coverage: Banking, wallets, cards, remittances, and crypto monitored under one system.

FinCense helps Australian institutions reduce compliance costs, meet AUSTRAC requirements, and strengthen customer trust.

Best Practices for Implementing Agentic AI

Start with Data Quality: Clean, reliable data ensures accurate AI outputs.
Adopt Explainable Models: Transparency is essential for AUSTRAC and internal stakeholders.
Integrate Across Channels: Cover NPP, cards, wallets, and crypto under one platform.
Pilot First: Begin with a small use case before scaling across the institution.
Train Investigators: Ensure teams are equipped to work with AI copilots.
Engage Regulators Early: Keep AUSTRAC informed about how AI is being used.

The Future of Agentic AI in Compliance

Deeper Integration with Real-Time Payments: PayTo and other overlay services will require millisecond-level monitoring.
Countering AI-Powered Fraud: Criminals will use deepfakes and synthetic identities, making Agentic AI even more critical.
Shared Compliance Networks: Banks will collaborate more closely through federated learning.
AI-First Compliance Teams: Investigations will be led by AI copilots, with human oversight.
Sustainability of Compliance: Automation will help reduce the rising cost of compliance.

Conclusion

Agentic AI is not just a buzzword. It is redefining compliance in Australia by making fraud detection faster, investigations smarter, and reporting more transparent. For banks and fintechs facing AUSTRAC’s high expectations, Agentic AI offers a path to resilience and trust.

Community-owned banks like Regional Australia Bank and Beyond Bank demonstrate that adoption is possible for institutions of all sizes. Platforms like Tookitaki’s FinCense integrate Agentic AI to deliver compliance outcomes that go beyond regulatory checkboxes.

Pro tip: The future of compliance will belong to institutions that combine real-time monitoring, adaptive AI, and explainable reporting. Agentic AI is the foundation of that future.

Agentic AI in Compliance: The Secret Weapon Against Financial Crime

Compliance Hubs

16 Sep 2025

6 min

read

AI in Fraud Detection in Banking: Transforming Australia’s Fight Against Financial Crime

With fraud moving faster than ever, Australian banks are turning to AI to detect and prevent scams in real time.

Fraud is one of the biggest challenges facing banks today. In Australia, losses to scams exceeded AUD 3 billion in 2024, with criminals exploiting digital banking, instant payments, and cross-border channels. Legacy systems, built for batch monitoring, cannot keep up with the scale and speed of these threats.

This is why AI in fraud detection in banking is rapidly becoming a necessity. Artificial intelligence allows institutions to detect suspicious activity in real time, adapt to new fraud typologies, and reduce the burden on compliance teams. In this blog, we explore how AI is reshaping fraud detection in Australia, the benefits it brings, and how banks can implement it effectively.

Why Fraud Detection Needs AI

1. Speed of Real-Time Payments

The New Payments Platform (NPP) has transformed banking in Australia by enabling instant transfers. Unfortunately, it also allows fraudsters to move stolen funds before they can be recalled. AI is essential for monitoring and scoring transactions within milliseconds.

2. Evolving Typologies

From account takeover fraud to deepfake scams, criminals are constantly innovating. Static rules cannot keep up. AI models can detect unusual patterns that indicate new fraud techniques.

3. Rising Alert Volumes

Traditional systems flood investigators with false positives. AI reduces noise by distinguishing genuine risks from harmless anomalies.

4. AUSTRAC Expectations

Regulators demand effective monitoring and reporting under the AML/CTF Act 2006. AI provides transparency and scalability to meet these expectations.

How AI Works in Fraud Detection

1. Machine Learning Models

AI systems are trained on historical transaction data to identify suspicious behaviour. Unlike static rules, machine learning adapts over time.

2. Behavioural Analytics

AI monitors customer behaviour, such as login times, device usage, and transaction patterns, to flag unusual activity.

3. Anomaly Detection

AI identifies deviations from normal behaviour, such as sudden large transfers or new device access.

4. Natural Language Processing (NLP)

Used in screening communications or transaction details for suspicious intent.

5. Federated Learning

Allows banks to share insights on fraud patterns without exposing sensitive customer data.

Common Fraud Typologies Detected by AI

Account Takeover (ATO): AI detects unusual login behaviour, device changes, and suspicious transfers.
Authorised Push Payment (APP) Scams: Analyses transaction context and behavioural cues to flag high-risk payments.
Mule Account Networks: Identifies linked accounts moving funds in rapid succession.
Card-Not-Present Fraud: Flags unusual online purchase behaviour.
Business Email Compromise (BEC): Detects unusual payment instructions and new beneficiary activity.
Crypto Laundering: Monitors conversions between fiat and digital assets for anomalies.

Red Flags AI Helps Detect in Real Time

High-value transfers to new or suspicious beneficiaries.
Transactions inconsistent with customer profiles.
Multiple failed login attempts followed by success.
Rapid inflows and outflows with no account balance retention.
Sudden changes in customer details followed by large transfers.
Transfers to high-risk jurisdictions or exchanges.

Benefits of AI in Fraud Detection

1. Real-Time Monitoring

AI processes data instantly, essential for NPP and PayTo transactions.

2. Reduction in False Positives

Adaptive models cut down on irrelevant alerts, saving investigators’ time.

3. Faster Investigations

AI copilots summarise cases and recommend next steps, reducing investigation times.

4. Scalability

AI can handle increasing transaction volumes without needing large compliance teams.

5. Improved Regulatory Alignment

Explainable AI ensures alerts can be justified to AUSTRAC and other regulators.

6. Enhanced Customer Trust

Customers are more likely to trust banks that prevent fraud proactively.

Challenges in Deploying AI

Data Quality Issues: AI is only as good as the data it learns from.
Integration with Legacy Systems: Many banks still rely on outdated infrastructure.
Skills Shortages: Australia faces a lack of experienced data scientists and AML specialists.
Explainability Concerns: Black-box models may not meet AUSTRAC’s transparency expectations.
Cost of Implementation: High initial investment can be a barrier for smaller institutions.

Case Example: Community-Owned Banks Using AI

Community-owned banks like Regional Australia Bank and Beyond Bank are adopting AI-powered compliance platforms to strengthen fraud detection. These institutions demonstrate that advanced fraud prevention is not only for Tier-1 banks. By leveraging AI, they reduce false positives, detect mule networks, and meet AUSTRAC’s expectations, all while operating efficiently.

Spotlight: Tookitaki’s FinCense

FinCense, Tookitaki’s compliance platform, integrates AI at its core to deliver advanced fraud detection capabilities for Australian institutions.

Real-Time Monitoring: Detects suspicious activity across NPP, PayTo, and cross-border corridors.
Agentic AI: Learns from evolving fraud patterns and continuously improves accuracy.
Federated Intelligence: Accesses real-world typologies from the AFC Ecosystem.
FinMate AI Copilot: Summarises cases, recommends next steps, and drafts regulator-ready reports.
AUSTRAC Compliance: Generates Suspicious Matter Reports (SMRs) and maintains audit trails.
Cross-Channel Protection: Covers banking, cards, wallets, remittances, and crypto.

FinCense empowers banks to fight fraud proactively, cut compliance costs, and build customer trust.

Best Practices for Implementing AI in Fraud Detection

Start with Data Quality: Clean, structured data is the foundation of effective AI.
Adopt Explainable AI: Ensure every alert can be justified to regulators.
Integrate Across Channels: Cover all payment types, from NPP to crypto.
Train Staff on AI Tools: Empower investigators to use AI effectively.
Pilot and Scale Gradually: Start small, refine models, then scale across the enterprise.
Collaborate with Peers: Share insights through federated learning for stronger defences.

The Future of AI in Fraud Detection in Australia

Deeper PayTo Integration: AI will play a critical role in monitoring new overlay services.
Detection of Deepfake Scams: AI will need to counter AI-driven fraud tactics such as synthetic voice and video.
Shared Fraud Databases: Industry-wide collaboration will improve real-time detection.
AI-First Compliance Teams: Copilots like FinMate will become standard tools for investigators.
Balance Between Security and Experience: AI will enable strong fraud prevention with minimal customer friction.

Conclusion

AI is transforming fraud detection in banking, particularly in Australia where real-time payments and evolving scams create unprecedented risks. By adopting AI-powered platforms, banks can detect threats earlier, reduce false positives, and ensure AUSTRAC compliance.

Community-owned banks like Regional Australia Bank and Beyond Bank prove that even mid-sized institutions can lead in AI-driven compliance innovation. For all financial institutions, the path forward is clear: embrace AI not just as a tool, but as a cornerstone of fraud detection and customer trust.

Pro tip: The most effective AI in fraud detection is transparent, adaptive, and integrated into the entire compliance workflow. Anything less leaves banks one step behind fraudsters.

AI in Fraud Detection in Banking: Transforming Australia’s Fight Against Financial Crime

Blogs

17 Sep 2025

6 min

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