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.

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

1. Account Takeover Fraud: Detects unusual login and transaction activity in real time.
2. Authorised Push Payment (APP) Scams: Identifies high-risk transfers initiated under duress.
3. Mule Networks: Maps hidden links between accounts, devices, and transactions.
4. Sanctions Screening: Flags high-risk names or entities with contextual intelligence.
5. KYC/CDD Monitoring: Automates risk scoring of new and existing customers.
6. 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

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

The Future of Agentic AI in Compliance

1. Deeper Integration with Real-Time Payments: PayTo and other overlay services will require millisecond-level monitoring.
2. Countering AI-Powered Fraud: Criminals will use deepfakes and synthetic identities, making Agentic AI even more critical.
3. Shared Compliance Networks: Banks will collaborate more closely through federated learning.
4. AI-First Compliance Teams: Investigations will be led by AI copilots, with human oversight.
5. 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.

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

1. Account Takeover (ATO): AI detects unusual login behaviour, device changes, and suspicious transfers.
2. Authorised Push Payment (APP) Scams: Analyses transaction context and behavioural cues to flag high-risk payments.
3. Mule Account Networks: Identifies linked accounts moving funds in rapid succession.
4. Card-Not-Present Fraud: Flags unusual online purchase behaviour.
5. Business Email Compromise (BEC): Detects unusual payment instructions and new beneficiary activity.
6. 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

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

The Future of AI in Fraud Detection in Australia

1. Deeper PayTo Integration: AI will play a critical role in monitoring new overlay services.
2. Detection of Deepfake Scams: AI will need to counter AI-driven fraud tactics such as synthetic voice and video.
3. Shared Fraud Databases: Industry-wide collaboration will improve real-time detection.
4. AI-First Compliance Teams: Copilots like FinMate will become standard tools for investigators.
5. 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.