How modern technology can help improve the reconciliation process?
Modern technologies such as artificial intelligence and machine learning have proven to improve the efficiency and effectiveness of business processes across companies of all sizes. These technologies are able to reshape businesses by automating, streamlining and increasing productivity as well as by improving the quality of products or services. They can reduce the time of work, reduce costs, simplify tasks, speed up business production, enhance marketing and provide better customer service. For businesses across industries, it is now imperative to innovate and adopt modern technology to stay relevant in the market amid heightened competition.
In the world of finance and accounting, the reconciliation process is of prime importance. Having a proper reconciliation process is vital for the continuity of any business. Through this article, we will understand how modern technology can revamp the existing reconciliation methods to make the key process more efficient.
What is reconciliation? Why it is important?
In accounting, reconciliation is defined as the process of matching that two sets of records to find out if they are in agreement. For accounting professionals, it is important to ensure that the money leaving your account is the same as the actual money spent so that balances of two account statements match at the end of a particular calculation period. Certain differences in accounts occur because of the timing of payments and deposits and they can be easily rectified. However, there are also certain situations of unexplained or mysterious discrepancies that require serious efforts to rectify. These discrepancies can be indicators of fraud or cooking the books, and serious investigation may be needed to figure out the truth behind them.
According to the Generally Accepted Accounting Principles (GAAP), carrying out proper account reconciliation will provide accuracy and consistency in financial accounts. The process is necessary to ensure that all cash outlays and inlays match between cash flow statements and income statements. It is imperative for companies to reconcile their accounts to prevent errors in the balance sheets, check for fraud, and avoid negative opinions from auditors.
The major benefits of account reconciliation in business accounting are:
- It helps avoid balance sheet errors and other accounting mistakes that can lead to serious ramifications.
- It can help against fraud (eg. fraudulent withdrawals from bank accounts) and ensure financial integrity during a bank reconciliation process.
- It helps understand the accounts better with clear details of incomes and expenses.
A robust and steady reconciliation process helps improve the accuracy of the financial reporting and allows the finance department of a business to publish financial reports with confidence.
What are the techniques used for reconciliation?
Here are some reconciliation methods followed by different types of businesses:
Manual reconciliation
It is the traditional way of account reconciliation with written accounts and dedicated staff. Today, this method is no longer feasible in today’s scenario due to the ever-increasing data volumes.
Spreadsheet reconciliation
This is done by using spreadsheet software solutions that have basic data arrangement and calculation features. This method is still used by a large number of organizations. Spreadsheets cannot manage the rapid handling of data as demanded by the regulations today. Spreadsheet reconciliation can consume up to four hours of an accountant’s time every day as he/she has to manually sum up the numbers and spend additional time in the mechanics of reconciliation.
Rules-based and Hosted Reconciliation Solutions
Through partially automating reconciliation processes, these software solutions could greatly reduce errors that came via manual processing. They could address matching of transactions more effectively with pre-set business matching rules and create cases around exceptions/breaks which need human intelligence to reconcile.
AI/Machine Learning-based Solutions
These reconciliation software solutions came into play to address the drawbacks of rules-based solutions. Mixing and matching certain attributes of data across multiple files will help match records. It is not manually possible to figure out attribute-mix and create that many rules. AI/Machine Learning can automatically identify attribute-mix/pattern and create rules for matching. They can also do exception handling, a key reconciliation process, which is completely manual today.
How technology can improve the modern reconciliation process
AI/machine learning-based solutions can make a paradigm shift in the reconciliation process as they are able to learn patterns from historical manual interventions and help detect breaks/exceptions automatically and resolve them in a faster manner. They can streamline and automate reconciliation processes across any line of business, dramatically enhance internal controls while enforcing standardization to improve the quality and accuracy of financial data. In addition, these solutions can help increase transparency in financial reporting.
Machine learning can help in reconciliation in the following ways:
- Connects to multiple data sources and bringing standardization in data requirement and quality
- Automatic pattern detection and matching
- Automatic break detection & resolution
- Records all activities for audit purpose
- Provides scalability and helps to streamline the reconciliation process
By employing these automated reconciliation software solutions, financial services can achieve unmatched operational efficiency improvements while ensuring compliance with the toughest of the regulations. They can revolutionize the reconciliation software industry and making processes more efficient and accurate.
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The Role of AML Software in Compliance

The Role of AML Software in Compliance


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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.

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.

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.

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.

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.

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.
