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How Smart AML Software Helped Banks Slash Compliance Costs by 60%

Tookitaki

11 min

read

Banks are turning to intelligent AML software to reduce compliance costs without compromising on risk controls.

Faced with rising regulatory pressures, operational complexity, and legacy systems that no longer scale, financial institutions are under intense pressure to do more with less. But instead of cutting staff or accepting higher risk, many have discovered a smarter path forward: leveraging AI-powered AML tools to streamline monitoring, reduce false positives, and boost overall compliance efficiency.

In this article, we explore how leading banks have cut their AML compliance costs by up to 60%—and the key technologies, strategies, and implementation lessons behind these results.

How Transaction Monitoring Enhances Financial Security-3

The Rising Cost Crisis in AML Compliance

Financial institutions face an unprecedented financial burden as anti-money laundering (AML) compliance expenditures continue to soar. The total global cost of financial crime compliance has reached a staggering $275.13 billion annually, creating significant operational challenges for banks and financial institutions worldwide.

Current AML compliance expenditure statistics

The cost crisis in AML banking is evident in regional spending patterns. In the United States and Canada alone, financial crime compliance costs have reached $81.87 billion. This burden extends globally, with financial institutions in North America spending $87.24 billion, South America $20.13 billion, EMEA (Europe, Middle East, and Africa) $114.08 billion, and APAC (Asia-Pacific) $60.39 billion on compliance measures.

At the institutional level, the figures are equally concerning. Some banks spend up to $671.04 million each year improving and managing their Know-Your-Customer (KYC) and AML processes, while the average bank allocates approximately $64.42 million annually. In the UK, financial institutions spent £38.3 billion on financial crime compliance in 2023, marking a 12% increase from the previous year and a 32% rise since 2021.

Furthermore, nearly 99% of financial institutions have reported increases in their financial crime compliance costs, demonstrating the pervasive nature of this financial challenge across the banking sector.

Key factors driving compliance costs upward

Several interconnected factors are propelling AML compliance costs to unprecedented levels. Labor expenses represent the largest component, accounting for 41% of total compliance costs in Asia. Additionally, 72% of financial institutions have experienced higher labor costs for compliance staff over the past year.

Technology investments have also become a major expense driver. Approximately 79% of organizations have seen increases in technology costs related to compliance and KYC software in the past 12 months. Meanwhile, training and awareness programs for employees can cost up to $13,420.80 per employee.

Other significant factors include:

The rise of cryptocurrencies and digital payments requiring new compliance mechanisms
Emerging AI technologies being exploited for illicit financial activities
Growing dependency on expensive outsourcing due to talent shortages
Legacy systems dating back to the 1960s that require costly maintenance
Data management inefficiencies across disparate systems

Consequently, expenses related to compliance have surged by more than 60% compared to pre-financial crisis levels, placing immense pressure on banks' operational budgets.

The regulatory pressure on financial institutions

Financial institutions face mounting regulatory demands that directly impact compliance costs. About 44% of mid and large-sized financial institutions identify the escalation of financial crime regulations and regulatory expectations as the primary factor driving increases in compliance expenses.

AML regulations are changing faster than ever as regulators aim to stay ahead of increasingly sophisticated criminal methodologies. This regulatory evolution introduces additional obligations, requiring more time and resources from financial institutions.

The costs of non-compliance are severe. In the US, banks have been hit with nearly $32.21 billion in non-compliance fines since 2008. More recently, regulators issued a $56.37 million civil monetary penalty for compliance failures. In 2023 alone, penalties for failing to comply with AML, KYC, and other regulations totaled $8.86 billion, a 57% increase from the previous year.

Given that financial institutions must navigate various legal obligations in each jurisdiction they operate in, the complexity of compliance requirements continues to grow. The challenge of maintaining compliance while managing costs has become a critical strategic priority for banks around the world.

Identifying Major Cost Centres in AML Operations

Understanding the exact sources of AML compliance expenses allows financial institutions to target their cost-cutting efforts more effectively. Four major cost centres consistently drain resources in banking compliance operations, creating financial strain that smart software solutions can address.

Manual review processes and their financial impact

Manual compliance processes severely impact operational efficiency and profitability. Tedious, repetitive tasks within customer onboarding and transaction monitoring consume valuable time for analysts and investigators in financial intelligence units. These labour-intensive processes require significant resources, particularly when handling complex ownership structures or identifying important business attributes.

Notably, manual processes that initially appear cost-effective often lead to unexpected expenses. Over time, banks must deploy additional resources, including external consultants, to overcome operational challenges. The opportunity costs become substantial—manual AML checks slow down customer onboarding, preventing institutions from scaling efficiently and directly impacting revenue.

False positive alert management costs

Perhaps the most significant operational drain comes from false positive alerts in transaction monitoring systems. Studies show that up to 95% of alerts generated by traditional monitoring systems are false positives, creating substantial noise that obscures truly suspicious activity. This inefficiency forces compliance teams to spend countless hours investigating legitimate transactions.

The financial impact is substantial. According to a 2021 survey, 79% of companies frequently have to rework data analytics projects due to poor data quality, wasting valuable time and resources. Additionally, 72% of financial institutions saw higher labour costs for compliance staff in the past year, partially attributable to false positive management.

Data management inefficiencies

Poor data quality represents a largely underestimated cost centre in AML compliance. Consultancy Gartner estimates that poor data quality costs businesses an average of SGD 17.31 million annually. In extreme cases, the cost can be catastrophic—one UK-based commercial bank was fined £56 million after experiencing system failure due to corrupted and incomplete data.

The problems primarily stem from:

Inconsistent data formats across disparate systems
Outdated databases lacking current customer information
Insufficient data-sharing mechanisms between departments
Siloed information that prevents holistic customer views

A survey found that 45% of respondents highlighted poor-quality, siloed data as a top barrier to financial crime risk detection. Without accurate and comprehensive data, financial institutions struggle to assess and mitigate risk properly, increasing the likelihood of regulatory penalties.

Staffing and training expenses

Labour represents the largest financial compliance expense, accounting for 41% of total costs in Asia. Between 2016 and 2023, the number of employee hours dedicated to complying with financial regulations surged by 61%, though total employee hours across the industry grew by only 20%.

From a personnel standpoint, even minimal AML compliance requires at least two dedicated employees—an analyst to handle monitoring and investigations and a director to oversee the process. These staff members need specialised qualifications, including CAMS certifications and an extensive background in financial crime regulations.

Furthermore, 70% of financial institutions faced rising compliance training expenses in the past year. This increase reflects the growing complexity of AML requirements and the need for specialised expertise to navigate evolving regulations effectively.

By identifying these major cost centers accurately, banks can strategically implement AML compliance software to address specific operational pain points rather than applying broad, ineffective solutions.

Smart Software Implementation Strategies

Effective implementation of smart AML solutions requires strategic planning to maximise cost reduction benefits. Financial institutions that approach software implementation systematically have reported up to 70% reduction in false positives and 50% shorter onboarding cycles, demonstrating the significant impact of proper execution.

Assessing your bank's specific compliance needs

Before selecting any software solution, banks must thoroughly evaluate their unique risk profile and compliance challenges. This assessment should align with the Financial Action Task Force (FATF) guidance that "a risk-based approach should be the cornerstone of an effective AML/CFT program".

First, map the risks identified in your institution's AML risk assessment against current transaction monitoring controls to identify potential gaps. This mapping process helps determine which scenarios are necessary to ensure adequate coverage of products and services. Subsequently, evaluate your data architecture to identify potential quality issues that could impact system performance—poor data quality costs businesses an average of SGD 17.31 million annually.

Finally, understand your transaction volumes and system requirements to ensure any solution can handle your operational scale without performance bottlenecks.

Selecting the right AML software solution

When evaluating AML software options, focus on these essential capabilities:

Advanced analytics and AI: Solutions utilizing artificial intelligence reduce false positives by up to 70% while improving suspicious activity detection.
Integration capabilities: Ensure seamless connection with existing core systems, which prevents data silos and operational disruptions.
Customizability: Look for tools that can be tailored to your bank's specific requirements or vendors that include these requests in their product roadmap.
Regulatory compliance: Verify alignment with local and international AML regulations in all jurisdictions where your institution operates.
Scalability: Assess whether the solution can accommodate your growth trajectory without requiring expensive system overhauls.

Importantly, evaluate vendor expertise in financial crime prevention specifically—not just technology. This domain knowledge significantly impacts implementation success.

Phased implementation approach for minimal disruption

To minimize operational disruption, adopt a phased deployment strategy rather than attempting wholesale system replacement. Begin with a sandbox environment that enables immediate integration testing while ongoing work continues in other areas.

This "test and iterate" mindset allows implementation to start with ready deliverables while more complex components are developed. Throughout implementation, assign a dedicated implementation consultant who supports your team through go-live, ensuring continuity of service and prompt resolution of challenges.

Above all, recognise that implementation is not a one-time event. Establish processes for continuous optimisation as new risks emerge, enabling your team to quickly build and deploy new rules without lengthy support tickets. This approach ensures your AML program remains effective as criminal tactics evolve.

Process Optimisation Through Automation

Automation represents the cornerstone of cost-effective AML operations, with financial institutions achieving remarkable efficiency gains through process optimisation. Modern AML compliance software delivers proven results, reducing false positives by up to 60% while enabling compliance teams to focus on genuinely suspicious cases.

Streamlining customer due diligence workflows

Manual CDD processes create significant bottlenecks, with 48% of banks identifying customer due diligence regulations as their biggest challenge. In contrast to traditional approaches, automated CDD workflows deliver immediate benefits through enhanced precision and speed.

Smart software solutions streamline identity verification using biometrics, document scanning, and third-party verification tools. Moreover, these systems enable comprehensive risk profiling by analysing data from multiple external sources to create holistic customer risk profiles. As a result, institutions experience significantly faster compliance handling times over traditional methods while eliminating back-office support needs.

Automating suspicious activity reporting

SAR preparation traditionally consumes substantial resources through manual narrative construction and data entry. Indeed, AI-driven SAR automation transforms this process by generating precise reports with minimal human intervention.

Advanced systems like Tookitaki's FinCense speed up SAR creation by 70% through generative AI-crafted narratives. These platforms auto-populate mandatory fields and craft detailed narratives that align with law enforcement expectations. Correspondingly, financial institutions benefit from enhanced filing consistency while reducing human error.

Essential capabilities in automated SAR systems include:

Centralised data integration from disparate systems
Optical character recognition for document data extraction
Workflow management with clear deadlines to prevent bottlenecks

Enhancing transaction monitoring efficiency

AI-powered transaction monitoring represents the most impactful automation opportunity in AML operations. Traditional systems flag excessive false positives—up to 95% of alerts require investigation despite being legitimate transactions.

Machine learning models trained on historical data uncover complex patterns not detectable through rules-based systems alone. In fact, institutions implementing these solutions report false positive reductions of up to 85%, allowing compliance professionals to concentrate on genuinely risky transactions.

Real-time monitoring capabilities further enhance effectiveness by analyzing transactions as they occur, providing immediate alerts of potential threats. Obviously, this approach enables prompt intervention against suspicious activities while maintaining regulatory compliance.

Measuring ROI and Cost Reduction Results

Quantifying the financial benefits of AML software requires robust measurement frameworks and clear metrics. Successful financial institutions establish performance indicators that directly track cost reduction alongside compliance effectiveness.

Key performance indicators for AML cost efficiency

Financial institutions primarily track four critical KPIs to measure AML cost efficiency:

Compliance cost per transaction: The total AML costs divided by transaction volume, allowing comparison across products
Compliance cost percentage: AML expenses as a percentage of total company costs, providing perspective on relative financial impact
Compliance headcount ratio: The proportion of compliance staff to total employees, offering insight into resource allocation
Cost per alert: Total AML costs divided by investigated alerts, revealing investigation efficiency

These metrics help banks identify specific areas where AML compliance software delivers the greatest financial impact. Nonetheless, measuring ROI extends beyond simple cost tracking—banks must also monitor operational efficiency gains and risk reduction.

Before-and-after cost comparison methodology

Calculating accurate ROI requires a structured methodology. First, institutions must establish a baseline by documenting current AML expenditures across labour, technology, and external services. Following implementation, banks can apply standard ROI formulas: ROI = (Benefits - Costs) / Costs × 100

For a comprehensive analysis, institutions should include both direct savings and avoided costs. Therefore, the complete formula becomes:

Cost savings = (Fines avoided + Reputational damage avoided) - Implementation costs

Some institutions utilize more sophisticated calculations like Net Present Value (NPV) to account for future cash flows or Internal Rate of Return (IRR) to determine break-even points.

Real-world case studies of 60% cost reduction

Several financial institutions have documented substantial cost reductions through smart AML software implementation. Danske Bank implemented an AI-powered system that analysed customer data and transaction patterns in real-time, resulting in a 60% reduction in false positives. HSBC automated its compliance processes with AI, saving approximately SGD 536,832 annually while improving customer due diligence effectiveness.

Similarly, a global payment processor achieved a 70% reduction in false positives after implementing Tookitaki's solution, substantially improving compliance team efficiency. A traditional bank integrated the same technology and recorded over 50% false positive reduction, saving valuable investigative resources.

These results underscore how modern AML compliance software delivers measurable financial benefits while strengthening regulatory compliance position.

Conclusion

In conclusion, the landscape of AML compliance is rapidly evolving, and financial institutions need cutting-edge solutions to stay ahead. While smart AML compliance software has proven to be a game-changer for banks worldwide, Tookitaki's FinCense stands out as the best-in-class solution, revolutionising AML compliance for banks and fintechs alike.

As we've seen, financial institutions implementing advanced AML systems have achieved remarkable results, cutting compliance costs by up to 60% while strengthening their regulatory effectiveness. Real-world success stories from major banks like Danske Bank and HSBC demonstrate the substantial impact of automated compliance solutions. However, FinCense takes these benefits even further:

100% Risk Coverage: Leveraging Tookitaki's AFC Ecosystem, FinCense ensures comprehensive and up-to-date protection against financial crimes across all AML compliance scenarios.
50% Reduction in Compliance Operations Costs: FinCense's machine-learning capabilities significantly reduce false positives, allowing institutions to focus on material risks and drastically improve SLAs for compliance reporting (STRs).
Unmatched 90% Accuracy: FinCense's AI-driven AML solution provides real-time detection of suspicious activities with over 90% accuracy, surpassing industry standards.
Advanced Transaction Monitoring: By utilising the AFC Ecosystem, FinCense offers 100% coverage using the latest typologies from global experts. It can monitor billions of transactions in real-time, effectively mitigating fraud and money laundering risks.
Automated Workflows: FinCense streamlines key areas such as customer due diligence, suspicious activity reporting, and data management processes, aligning with the proven benefits of smart AML software implementation.

The evidence clearly points to smart software as the path forward for sustainable AML compliance, and FinCense is leading the charge. By choosing Tookitaki's FinCense, banks and fintechs can position themselves to handle growing regulatory demands while maintaining operational efficiency. FinCense not only promises but delivers on the dual goals of cost reduction and improved compliance effectiveness through its innovative, AI-powered approach.

In an era where financial institutions face mounting pressures, FinCense emerges as the solution that truly revolutionises AML compliance. Its efficient, accurate, and scalable AML solutions empower banks and fintechs to stay ahead of financial crimes while optimising their resources. With FinCense, the future of AML compliance is not just about meeting regulatory requirements – it's about exceeding them with unparalleled efficiency and accuracy.

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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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