What is Intercompany Accounting?
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What is Intercompany Accounting?
Intercompany accounting stands for the processing and accounting of inter-company/internal financial activities and events that cross legal entities, branches, or national borders. This may include (but is not limited to) the sales of products and services, fee sharing, royalties, cost allocations, and financing activities. Intercompany accounting is a broader segment than accounting – it extends into various functions, which include finance, tax, and treasury. According to the accounting firm, Grant Thornton LLP, intercompany transactions account for 30-40% of the global economy, which amounts to almost $40 trillion annually, and is further ranked as the ‘5th most common cause of corporate financial restatements’.
A 3-Step Approach to Intercompany Accounting
The transactions are important for many reasons, such as compliance with local tax codes, accurate reporting, regulations, good governance in general, and accounting rules. Financial institutions that need to improve their intercompany accounting can use this 3-step approach to intercompany accounting to improve their performance:
- Establish Standards, Policies, and Procedures: The foremost step to improve intercompany accounting is to establish a consistent process that can help identify, authorize, and clear the intercompany transactions. Although it would be easier to go with automation as the initial step, since the manual processes serve as an issue (they do not have consistent standards), chances are that attempting to automate the intercompany accounting will turn into a failure.
The policies and procedures are meant to include a list of what products and services are supposed to be provided between subsidiaries, along with transfer pricing for each, and the level of authorization needed for any transaction. Some other specifications may include a list of designated intercompany accounts, rules to identify and complete transactions, and a schedule that has specific deadlines to clear the balances every month.
- Automate the processes: According to a survey by Deloitte on ‘Intercompany Accounting & Process Management’, 54% of the companies still rely on manual intercompany processing, 47% only have ad hoc netting capabilities, while 30% report a significant out-of-balance position. After the policies and procedures are integrated and followed, the next step is to go for automation. The reason behind this is that keeping up with thousands of transactions by using spreadsheets is an inefficient method – one that only increases the risk of having errors. Further, in the case of companies that have subsidiaries in various countries, it becomes even more challenging to keep track. Alongside this, dealing with the currency exchange rates, the local tax codes, and the different rules for accounting can make it impossible to complete the process on time.
Yet, not all accounting solutions can manage intercompany transactions. There is software designed for emerging companies, which does not typically support multiple business entities. This can be a critical limitation, as it makes identifying and matching the transactions between various subsidiaries a manual process.
The minimum requirement from the software is that it should be able to tag intercompany purchase orders and sales orders when they are created, and link them automatically. This will help the accounting team, as they will no longer have to search amongst thousands of transaction entries to find the matching pairs. The revenue and expenses of intercompany transactions should be removed automatically from consolidated financial statements, specifically during the closing process. Another requirement from the software system is that it should also include intercompany netting functionality, which not only saves time and effort during the settlement process, but also saves money by reducing the number of invoices that need to be generated, plus payments that have to be processed every month.
- Centralize: It is mainly the corporate accounting staff’s job to manage intercompany accounting, which means that most things get done as part of the closing procedure. Yet, as the accounting team has other responsibilities, it isn’t ideal to wait until the end of the month, as it would extend the close cycle. On its own, the intercompany elimination can add days to the procedure if it’s not automated, which has an impact on the timings of the reports. The added pressure to close the books at the earliest may also increase the risk of errors.
So, centralizing the intercompany accounting serves as one of the best practices, either under a select person, or, in case there is a larger volume of people, a group of individuals under the supervision of the corporate controller. While dedicating resources to manage an activity that isn’t categorized as strategic could be a bit hard to explain, the efficiencies that companies gain, along with the improved supervision of this process, eventually pays its dividends. Managing the process centrally requires visibility into all intercompany transactions, which is difficult for companies that rely on multiple, differing accounting systems. So, in case one truly wants to control the process, it’s difficult to manage the business with different subsidiaries on a single accounting platform.
Types of Intercompany Transactions
The three main types of intercompany transactions include: downstream, upstream, and lateral. Let’s understand how each of these intercompany transactions is recorded in the respective unit’s books. Also, their impact, and how to adjust the financials that are consolidated.
- Downstream Transaction: This type of transaction flows from the parent company, down to a subsidiary. With this transaction, the parent company records it with the applicable profit or loss. The transaction is made transparent and can be viewed by the parent company and its stakeholders, but not to the subsidiaries. For example, a downstream transaction would be the parent company selling an asset or inventory to a subsidiary.
- Upstream Transaction: This type of transaction is the reverse of downstream and flows from the subsidiary to the parent entity. For an upstream transaction, the subsidiary will record the transaction along with related profit or loss. An example would be when a subsidiary might transfer an executive to the parent company for a time period, charging the parent company by the hour for the executive’s services. For such a case, the majority and minority interest stakeholders can share the profit/loss, as they share ownership of the subsidiary.
- Lateral Transaction: This transaction occurs between two subsidiaries within the same parent organization. The subsidiary/subsidiaries record their lateral transaction along with profit and loss, which is similar to accounting for an upstream transaction. For example, when one subsidiary provides IT services to another, with a fee.
Intercompany Transactions Accounting Importance
Intercompany transactions are of great importance, as they can help to greatly improve the flow of finances and assets. Studies on transfer pricing help to ensure that the intercompany transfer pricing falls within reach of total pricing in order to avoid any unnecessary audits.
Such intercompany transactions accounting can help with keeping records for resolving tax disputes, mainly in the countries/jurisdictions where the markets are upcoming and new, and where there is little to no regulation governing the related parties’ transactions. The following are a few areas that are affected by the use of intercompany transactions accounting:
- Loan participation
- Sales and transfer of assets
- Dividends
- Insurance policies
- Transactions that have member banks and affiliates
- The management and service fees
What is an Intercompany Transaction?
Intercompany transactions happen when the unit of a legal entity makes a transaction with another unit of the same entity. There are many international companies that take advantage of intercompany transfer pricing or other related party transactions. This is to influence IC-DISC, promote improved transaction taxes, and, effectively, enhance efficiency within the financial institution. The transactions are essential to maximizing the allocation of income and deduction. Here are a few examples of such transactions:
- Between two departments
- Between two subsidiaries
- Between the parent company and subsidiary
- Between two divisions
There are two basic categories of intercompany transactions: direct and indirect intercompany transactions.
- Direct Intercompany Transactions: These transactions may happen from intercompany transactions between two different units within the same company entity. They can aid in notes payable and receivable, and also interest expense and revenues.
- Indirect Intercompany Transactions: These transactions occur when the unit of an entity obtains the debt/assets issued to another company that is unrelated, with the help of another unit in the original parent company. Such transactions can help various economic factors, including the elimination of interest expense on the retired debt, create gain or loss for early debt retirement, or remove the investment in interest and bond revenue.
Intercompany Accounting Best Practices
In a survey conducted in 2016 by Deloitte, which included over 4,000 accounting professionals, nearly 80% experienced challenges related to intercompany accounting. The issue was around differing software systems within and across financial institute units and divisions, intercompany settlement processes, management of complex legal agreements, transfer pricing compliance, and FX exposure. With issues such as multiple stakeholders, large transaction volumes, complicated entity agreements, and increased regulatory scrutiny, it’s clear that intercompany accounting requires a structured, end-to-end process. Here are some of the intercompany accounting best practices:
Streamline and Optimize the Process with Technology
It is counted as intercompany accounting best practices to have technology-enabled coordination and orchestration streamline intercompany accounting across the entire financial institution. Automation removes the burden of having to identify counterparties across various ERP systems. The integrated workflows ensure that tasks are completed in the correct order and in the most efficient timeframes, with the removal of any additional managers, who would waste their time chasing the completion of this task.
With automation, users can collaborate more easily and resources are deployed more efficiently. The employees who were previously occupied by keeping the data moving are freed to perform tasks of higher-value. With this, the result is faster resolution, along with timely and accurate elimination of intercompany transactions, cost savings, reduced cycle times, and an accelerated closing.
Streamline the Intercompany Process with a Single View
The elimination of intercompany transactions as a collaborative process requires the counterparties to have full visibility of their respective balances, along with the differences between them, and the underlying transactions. In an intragroup trade, too, counterparties need shared access to a common view of their intercompany positions.
With KPI monitoring, there is an overview of intercompany accounting status, which highlights potential delays in real-time and in a visual manner. The dashboards and alerts allow for companies to manage their progress in real-time, giving accounting professionals an overview of tasks that haven’t yet started or finished. With this visibility, team leaders can review bottlenecks by task, individual, cost center, as well as entity.
Eliminate Intercompany Mismatches Early on in the Process
In order to minimize delays around the agreement of intercompany differences, one needs to start the process prior to usual in the reporting cycle. By viewing intercompany mismatches this early on in the reporting cycle, individual companies can take remedial action and correct their positions before the consolidation is attempted.
The direct integration with the ERP systems allows financial institutes to extract invoice details to help reconcile differences in a more detailed manner. After resolving the differences, adjustments can be posted directly into ERP systems through the process, without manually posting reconciling journal entries. This is why automation effectively turns the intercompany process into a preliminary close, well in advance of the normal reporting cycle, every month.
Manage Intercompany Risk
One can eliminate endless standalone spreadsheets, which are typically used by individuals to manage intercompany accounting, by using an automated system that gives companies one version of the truth, along with an audit trail of activities detailing when and by whom they were completed. The workflows give the company employees ownership of every activity and eliminate the interdependencies of these tasks.
Financial institutes are able to orchestrate and monitor intercompany accounting as a fundamental part of their internal controls. The role-based security, aligned with the company’s underlying applications, maintains the integrity of roles and access. At the same time, one can attach or store procedures and policy documents in task list items, which are made immediately available to the people performing the intercompany tasks.
Devise Bullet-Proof Centralized Governance and Policies
For effective intercompany accounting, standard global policies are required to govern critical areas, such as data or charts of accounts, transfer pricing, and allocation methods. Companies may establish a center of excellence with joint supervision from accounting, tax, and treasury. It serves as a resource to address global process standardization and issues related to intercompany accounting. Having a single company-wide process would mean that companies adhere to best practices and give all finance stakeholders immediate visibility of issues, tasks, and bottlenecks that need escalation or remediation. This can help financial institutes benchmark their performance, address underlying issues, and facilitate post-close reviews. Further, it would help them to subsequently streamline activities in order to encourage a continuous process improvement and accelerate the close.
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Singapore’s Secret Weapon Against Dirty Money? Smarter AML Investigation Tools
In the fight against financial crime, investigation tools can make or break your compliance operations.
With Singapore facing growing threats from money mule syndicates, trade-based laundering, and cyber-enabled fraud, the need for precise and efficient anti-money laundering (AML) investigations has never been more urgent. In this blog, we explore how AML investigation tools are evolving to help compliance teams in Singapore accelerate detection, reduce false positives, and stay audit-ready.
What Are AML Investigation Tools?
AML investigation tools are technology solutions that assist compliance teams in detecting, analysing, documenting, and reporting suspicious financial activity. These tools bridge the gap between alert generation and action — providing context, workflow, and intelligence to identify real risk from noise.
These tools can be:
- Standalone modules within AML software
- Integrated into broader case management systems
- Powered by AI, machine learning, or rules-based engines
Why They Matter in the Singapore Context
Singapore’s financial services sector faces increasing pressure from regulators, counterparties, and the public to uphold world-class compliance standards. Investigation tools help institutions:
- Quickly triage and resolve alerts from transaction monitoring or screening systems
- Understand customer behaviour and transactional context
- Collaborate across teams for efficient case resolution
- Document decisions in a regulator-ready audit trail
Key Capabilities of Modern AML Investigation Tools
1. Alert Contextualisation
Investigators need context around each alert:
- Who is the customer?
- What’s their risk rating?
- Has this activity occurred before?
- What other products do they use?
Good tools aggregate this data into a single view to save time and prevent errors.
2. Visualisation of Transaction Patterns
Network graphs and timelines show links between accounts, beneficiaries, and geographies. These help spot circular payments, layering, or collusion.
3. Narrative Generation
AI-generated case narratives can summarise key findings and explain the decision to escalate or dismiss an alert. This saves time and ensures consistency in reporting.
4. Investigator Workflow
Assign tasks, track time-to-resolution, and route high-risk alerts to senior reviewers — all within the system.
5. Integration with STR Filing
Once an alert is confirmed as suspicious, the system should auto-fill suspicious transaction report (STR) templates for MAS submission.
Common Challenges Without Proper Tools
Many institutions still struggle with manual or legacy investigation processes:
- Copy-pasting between systems and spreadsheets
- Investigating the same customer multiple times due to siloed alerts
- Missing deadlines for STR filing
- Poor audit trails, leading to compliance risk
In high-volume environments like Singapore’s fintech hubs or retail banks, these inefficiencies create operational drag.
Real-World Example: Account Takeover Fraud via Fintech Wallets
An e-wallet provider in Singapore noticed a spike in high-value foreign exchange transactions.
Upon investigation, the team found:
- Victim accounts were accessed via compromised emails
- Wallet balances were converted into EUR/GBP instantly
- Funds were moved to mule accounts and out to crypto exchanges
Using an investigation tool with network mapping and device fingerprinting, the compliance team:
- Identified shared mule accounts across multiple victims
- Escalated the case to the regulator within 24 hours
- Blocked future similar transactions using rule updates
Tookitaki’s FinCense: Investigation Reinvented
Tookitaki’s FinCense platform provides end-to-end investigation capabilities designed for Singapore’s regulatory and operational needs.
Features That Matter:
- FinMate: An AI copilot that analyses alerts, recommends actions, and drafts case narratives
- Smart Disposition: Automatically generates case summaries and flags key findings
- Unified Case Management: Investigators work from a single dashboard that integrates monitoring, screening, and risk scoring
- MAS-Ready Reporting: Customisable templates for local regulatory formats
- Federated Intelligence: Access 1,200+ community-driven typologies from the AFC Ecosystem to cross-check against ongoing cases
Results From Tookitaki Clients:
- 72% fewer false positives
- 3.5× faster resolution times
- STR submission cycles shortened by 60%
Regulatory Expectations from MAS
Under MAS guidelines, financial institutions must:
- Have effective alert management processes
- Ensure timely investigation and STR submission
- Maintain records of all investigations and decisions
- Demonstrate scenario tuning and effectiveness reviews
A modern AML investigation tool supports all these requirements, reducing operational and audit burden.
AML Investigation and Emerging Threats
1. Deepfake-Fuelled Impersonation
Tools must validate biometric data and voiceprints to flag synthetic identities.
2. Crypto Layering
Graph-based tracing of wallet addresses is increasingly vital as laundering moves to decentralised finance.
3. Mule Account Clusters
AI-based clustering tools can identify unusual movement patterns across otherwise low-risk individuals.
4. Instant Payments Risk
Real-time investigation support is needed for PayNow, FAST, and other instant channels.
How to Evaluate a Vendor
Ask these questions:
- Can your tool integrate with our current transaction monitoring system?
- How do you handle false positive reduction?
- Do you support scenario simulation and tuning?
- Is your audit trail MAS-compliant?
- Can we import scenarios from other institutions (e.g. AFC Ecosystem)?
Looking Ahead: The Future of AML Investigations
AML investigations are evolving from reactive tasks to intelligence-led workflows. Tools are getting:
- Agentic AI: Copilots like FinMate suggest next steps, reducing guesswork
- Community-Driven: Knowledge sharing through federated systems boosts preparedness
- More Visual: Risk maps, entity graphs, and timelines help understand complex flows
- Smarter Thresholds: ML-driven dynamic thresholds reduce alert fatigue
Conclusion: Investigation is Your Last Line of Defence
In an age of instant payments, cross-border fraud, and synthetic identities, the role of AML investigation tools is mission-critical. Compliance officers in Singapore must be equipped with solutions that go beyond flagging transactions — they must help resolve them fast and accurately.
Tookitaki’s FinCense, with its AI-first approach and regulatory alignment, is redefining how Singaporean institutions approach AML investigations. It’s not just about staying compliant. It’s about staying smart, swift, and one step ahead of financial crime.
Fraud Detection Software for Banks: Inside the Digital War Room
Every day in Australia, fraud teams fight a silent battle. This is the story of how they do it, and the software helping them win.
Prologue: The Alert That Shouldn’t Have Happened
It is 2:14 pm on a quiet Wednesday in Sydney.
A fraud investigator at a mid-sized Australian bank receives an alert:
Attempted transfer: 19,800 AUD — flagged as “possible mule routing”.
The transaction looks ordinary.
Local IP.
Registered device.
Customer active for years.
Nothing about it screams fraud.
But the software sees something the human eye cannot:
a subtle deviation in typing cadence, geolocation drift over the past month, and a behavioural mismatch in weekday spending patterns.
This is not the customer.
This is someone pretending to be them.
The transfer is blocked.
The account is frozen.
A customer is protected from losing their savings.
This is the new frontline of fraud detection in Australian banking.
A place where milliseconds matter.
Where algorithms, analysts, and behavioural intelligence work together in near real time.
And behind it all sits one critical layer: fraud detection software built for the world we live in now, not the world we used to live in.
Chapter 1: Why Fraud Detection Has Become a War Room Operation
Fraud has always existed, but digital banking has changed its scale, speed, and sophistication.
Australian banks are facing:
- Real-time scams through NPP
- Deepfake-assisted social engineering
- Mule networks recruiting on TikTok
- Synthetic IDs built from fragments of real citizens
- Remote access scams controlling customer devices
- Cross-border laundering through fintech rails
- Account takeover via phishing and malware
Fraud today is not one person trying their luck.
It is supply-chain crime.
And the only way banks can fight it is by transforming fraud detection into a dynamic, intelligence-led discipline supported by software that thinks, learns, adapts, and collaborates.
Chapter 2: What Modern Fraud Detection Software Really Does
Forget the outdated idea that fraud detection is simply about rules.
Modern software must:
- Learn behaviour
- Spot anomalies
- Detect device manipulation
- Understand transaction velocity
- Identify network relationships
- Analyse biometrics
- Flag mule-like patterns
- Predict risk, not just react to it
The best systems behave like digital detectives.
They observe.
They learn.
They connect dots humans cannot connect in real time.
Chapter 3: The Six Capabilities That Define Best-in-Class Fraud Detection Software
1. Behavioural Biometrics
Typing speed.
Mouse movement.
Pressure on mobile screens.
Session navigation patterns.
These signals reveal whether the person behind the device is the real customer or an impostor.
2. Device Intelligence
Device fingerprinting, jailbreak checks, emulator detection, and remote-access-trojan indicators now play a key role in catching account takeover attempts.
3. Network Link Analysis
Modern fraud does not occur in isolation.
Software must map:
- Shared devices
- Shared addresses
- Linked mule accounts
- Common beneficiaries
- Suspicious payment clusters
This is how syndicates are caught.
4. Real-Time Risk Scoring
Fraud cannot wait for batch jobs.
Software must analyse patterns as they happen and block or challenge the transaction instantly.
5. Cross-Channel Visibility
Fraud moves across onboarding, transfers, cards, wallets, and payments.
Detection must be omnichannel, not siloed.
6. Analyst Assistance
The best software does not overwhelm investigators.
It assists them by:
- Summarising evidence
- Highlighting anomalies
- Suggesting next steps
- Reducing noise
Fraud teams fight harder when the software fights with them.
Chapter 4: Inside an Australian Bank’s Digital Fraud Team
Picture this scene.
A fraud operations centre in Melbourne.
Multiple screens.
Live dashboards.
Analysts monitoring spikes in activity.
Suddenly, the software detects something:
A cluster of small transfers moving rapidly into multiple new accounts.
Amounts just below reporting thresholds.
Accounts opened within the last three weeks.
Behaviour consistent with mule recruitment.
This is not random.
This is an organised ring.
The fraud team begins tracing the pattern using network graphs visualised by the software.
Connections emerge.
A clear structure forms.
Multiple accounts tied to the same device.
Shared IP addresses across suburbs.
Within minutes, the team has identified a mule network operating across three states.
They block the accounts.
Freeze the funds.
Notify the authorities.
Prevent a chain of victims.
This is fraud detection software at its best:
Augmenting human instinct with machine intelligence.
Chapter 5: The Weaknesses of Old Fraud Detection Systems
Some Australian banks still rely on systems that:
- Use rigid rules
- Miss behavioural patterns
- Cannot detect deepfakes
- Struggle with NPP velocity
- Generate high false positives
- Cannot identify linked accounts
- Have no real-time capabilities
- Lack explainability for AUSTRAC or internal audit
These systems were designed for a slower era, when payments were not instantaneous and criminals did not use automation.
Old systems do not fail because they are old.
They fail because the world has changed.
Chapter 6: What Australian Banks Should Look For in Fraud Detection Software (A Modern Checklist)
1. Real-Time Analysis for NPP
Detection must be instant.
2. Behavioural Intelligence
Software should learn how customers normally behave and identify anomalies.
3. Mule Detection Algorithms
Australia is experiencing a surge in mule recruitment.
This is now essential.
4. Explainability
Banks must be able to justify fraud decisions to regulators and customers.
5. Cross-Channel Intelligence
Transfers, cards, NPP, mobile apps, and online banking must speak to each other.
6. Noise Reduction
Software must reduce false positives, not amplify them.
7. Analyst Enablement
Investigators should receive context, not clutter.
8. Scalability for Peak Fraud Events
Fraud often surges during crises, holidays, and scams going viral.
9. Localisation
Australian fraud patterns differ from other regions.
10. Resilience
APRA CPS 230 demands operational continuity and strong third-party governance.
Fraud software is now part of a bank’s resilience framework, not just its compliance toolkit.
Chapter 7: How Tookitaki Approaches Fraud Detection
Tookitaki’s approach to fraud detection is built around one core idea:
fraudsters behave like networks, not individuals.
FinCense analyses risk across relationships, devices, behaviours, and transactions to detect patterns traditional systems miss.
What makes it different:
1. A Behaviour-First Model
Instead of relying on static rules, the system understands customer behaviour over time.
This helps identify anomalies that signal account takeover or mule activity.
2. Investigation Intelligence
Tookitaki supports analysts with enriched context, visual evidence, and prioritised risks, reducing decision fatigue.
3. Multi-Channel Detection
Fraud does not stay in one place, and neither does the software.
It connects signals across payments, wallets, online banking, and transfers.
4. Designed for Both Large and Community Banks
Institutions such as Regional Australia Bank benefit from accurate detection without operational complexity.
5. Built for Real-Time Environments
FinCense supports high-velocity payments, enabling institutions to detect risk at NPP speed.
Tookitaki is not designed to overwhelm banks with rules.
It is designed to give them a clear picture of risk in a world where fraud changes daily.
Chapter 8: The Future of Fraud Detection in Australian Banking
1. Deepfake-Resistant Identity Verification
Banks will need technology that can detect video, voice, and biometric spoofing.
2. Agentic AI Assistants for Investigators
Fraud teams will have copilots that surface insights, summarise cases, and provide investigative recommendations.
3. Network-Wide Intelligence Sharing
Banks will fight fraud together, not alone, through federated learning and shared typology networks.
4. Real-Time Customer Protection
Banks will block suspicious payments before they leave the customer’s account.
5. Predictive Fraud Prevention
Systems will identify potential mule behaviour before the account becomes active.
Fraud detection will become proactive, not reactive.
Conclusion
Fraud detection software is no longer a technical add-on.
It is the digital armour protecting customers, banks, and the integrity of the financial system.
The frontline has shifted.
Criminals operate as organised networks, use automation, manipulate devices, and exploit real-time payments.
Banks need software built for this reality, not yesterday’s.
The right fraud detection solution gives banks something they cannot afford to lose:
time, clarity, and confidence.
Because in today’s Australian financial landscape, fraud moves fast.
Your software must move faster.
AML Software in Australia: The 7 Big Questions Every Bank Should Be Asking in 2025
Choosing AML software used to be a technical decision. In 2025, it has become one of the most strategic choices a bank can make.
Introduction
Australia’s financial sector is entering a defining moment. Instant payments, cross-border digital crime, APRA’s tightening expectations, AUSTRAC’s data scrutiny, and the rise of AI are forcing banks to rethink their entire compliance tech stack.
At the centre of this shift sits one critical question: what should AML software actually do in 2025?
This blog does not give you a shopping list or a vendor comparison.
Instead, it explores the seven big questions every Australian bank, neobank, and community-owned institution should be asking when evaluating AML software. These are the questions that uncover risk, expose limitations, and reveal whether a solution is built for the next decade, not the last.
Let’s get into them.
Question 1: Does the AML Software Understand Risk the Way Australia Defines It?
Most AML systems were designed with global rule sets that do not map neatly to Australian realities.
Australia has:
- Distinct PEP classifications
- Localised money mule typologies
- Syndicated fraud patterns unique to the region
- NPP-driven velocity in payment behaviour
- AUSTRAC expectations around ongoing due diligence
- APRA’s new focus on operational resilience
AML software must be calibrated to Australian behaviours, not anchored to American or European assumptions.
What to look for
- Localised risk models trained on Australian financial behaviour
- Models that recognise local account structures and payment patterns
- Typologies relevant to the region
- Adaptability to NPP and emerging scams affecting Australians
- Configurable rule logic for Australia’s regulatory environment
If software treats all markets the same, its risk understanding will always be one step behind Australian criminals.
Question 2: Can the Software Move at the Speed of NPP?
The New Payments Platform changed everything.
What used to be processed in hours is now settled in seconds.
This means:
- Risk scoring must be real time
- Monitoring must be continuous
- Alerts must be triggered instantly
- Investigators need immediate context, not post-fact analysis
Legacy systems built for batch processing simply cannot keep up with the velocity or volatility of NPP transactions.
What to look for
- True real-time screening and monitoring
- Sub-second scoring
- Architecture built for high-volume environments
- Scalability without performance drops
- Real-time alert triaging
If AML software cannot respond before a payment settles, it is already too late.
Question 3: Does the Software Reduce False Positives in a Meaningful Way?
Every vendor claims they reduce false positives.
The real question is how and by how much.
In Australia, many banks spend up to 80 percent of their AML effort investigating low-value alerts. This creates fatigue, delays, and inconsistent decisions.
Modern AML software must:
- Prioritise alerts based on true behavioural risk
- Provide contextual information alongside flags
- Reduce noise without reducing sensitivity
- Identify relationships, patterns, and anomalies that rules alone miss
What to look for
- Documented false positive reduction numbers
- Behavioural analytics that distinguish typical from atypical activity
- Human-in-the-loop learning
- Explainable scoring logic
- Tiered risk categorisation
False positives drain resources.
Reducing them responsibly is a competitive advantage.
Question 4: How Does the Software Support Investigator Decision-Making?
Analysts are the heart of AML operations.
Software should not just alert them. It should empower them.
The most advanced AML platforms are moving toward investigator-centric design, helping analysts work faster, more consistently, and with greater clarity.
What to look for
- Clear narratives attached to alerts
- Visual network link analysis
- Relationship mapping
- Easy access to KYC, transaction history, and behaviour insights
- Tools that surface relevant context without manual digging
If AML software only generates alerts but does not explain them, it is not modern software. It is a data dump.
Question 5: Is the AML Software Explainable Enough for AUSTRAC?
AUSTRAC’s reviews increasingly require banks to justify their risk models and demonstrate why a decision was made.
AML software must show:
- Why an alert was generated
- What data was used
- What behavioural markers contributed
- How the system ranked or prioritised risk
- How changes over time affected decision logic
Explainability is now a regulatory requirement, not a bonus feature.
What to look for
- Decision logs
- Visual explanations
- Feature attribution for risk scoring
- Scenario narratives
- Governance dashboards
Opaque systems that cannot justify their reasoning leave institutions vulnerable during audits.
Question 6: How Well Does the AML Software Align With APRA’s CPS 230 Expectations?
Operational resilience is now a board-level mandate.
AML software sits inside the cluster of critical systems APRA expects institutions to govern closely.
This includes:
- Third-party risk oversight
- Business continuity
- Incident management
- Data quality controls
- Outsourcing governance
AML software is no longer evaluated only by compliance teams.
It must satisfy risk, technology, audit, and resilience requirements too.
What to look for
- Strong uptime track record
- Clear incident response procedures
- Transparent service level reporting
- Secure and compliant hosting
- Tested business continuity measures
- Clear vendor accountability and control frameworks
If AML software cannot meet CPS 230 expectations, it cannot meet modern banking expectations.
Question 7: Will the Software Still Be Relevant Five Years From Now?
This is the question few institutions ask, but the one that matters most.
AML software is not a one-year decision. It is a multi-year partnership.
To future-proof compliance, banks must look beyond features and evaluate adaptability.
What to look for
- A roadmap that includes new crime types
- AI models that learn responsibly
- Agentic support tools that help investigators
- Continuous updates without major uplift projects
- Collaborative intelligence capabilities
- Strong alignment with emerging AML trends in Australia
This is where vendors differentiate themselves.
Some provide tools.
A few provide evolution.
A Fresh Look at Tookitaki
Tookitaki has emerged as a preferred AML technology partner among several banks across Asia-Pacific, including institutions in Australia, because it focuses less on building features and more on building confidence.
Confidence that alerts are meaningful.
Confidence that the system is explainable.
Confidence that operations remain stable.
Confidence that investigators have support.
Confidence that intelligence keeps evolving.
Rather than positioning AML as a fixed set of rules, Tookitaki approaches it as a learning discipline.
Its platform, FinCense, helps Australian institutions strengthen:
- Real time monitoring capability
- Consistency in analyst decisions
- Model transparency for AUSTRAC
- Operational resilience for APRA expectations
- Adaptability to emerging typologies
- Scalability for both large and community institutions like Regional Australia Bank
This is AML software designed not only to detect crime, but to grow with the institution.
Conclusion
AML software in Australia is at a crossroads.
The era of legacy rules, static scenarios, and batch processing is ending.
Banks now face a new set of expectations driven by speed, transparency, resilience, and intelligence.
The seven questions in this guide cut through the noise. They help institutions evaluate AML software not as a product, but as a long-term strategic partner for risk management.
Australia’s financial sector is changing quickly.
The right AML software will help banks move confidently into that future.
The wrong one will hold them back.
Pro tip: The strongest AML systems are not just built on good software. They are built on systems that understand the world they operate in, and evolve alongside it.
Singapore’s Secret Weapon Against Dirty Money? Smarter AML Investigation Tools
In the fight against financial crime, investigation tools can make or break your compliance operations.
With Singapore facing growing threats from money mule syndicates, trade-based laundering, and cyber-enabled fraud, the need for precise and efficient anti-money laundering (AML) investigations has never been more urgent. In this blog, we explore how AML investigation tools are evolving to help compliance teams in Singapore accelerate detection, reduce false positives, and stay audit-ready.
What Are AML Investigation Tools?
AML investigation tools are technology solutions that assist compliance teams in detecting, analysing, documenting, and reporting suspicious financial activity. These tools bridge the gap between alert generation and action — providing context, workflow, and intelligence to identify real risk from noise.
These tools can be:
- Standalone modules within AML software
- Integrated into broader case management systems
- Powered by AI, machine learning, or rules-based engines
Why They Matter in the Singapore Context
Singapore’s financial services sector faces increasing pressure from regulators, counterparties, and the public to uphold world-class compliance standards. Investigation tools help institutions:
- Quickly triage and resolve alerts from transaction monitoring or screening systems
- Understand customer behaviour and transactional context
- Collaborate across teams for efficient case resolution
- Document decisions in a regulator-ready audit trail
Key Capabilities of Modern AML Investigation Tools
1. Alert Contextualisation
Investigators need context around each alert:
- Who is the customer?
- What’s their risk rating?
- Has this activity occurred before?
- What other products do they use?
Good tools aggregate this data into a single view to save time and prevent errors.
2. Visualisation of Transaction Patterns
Network graphs and timelines show links between accounts, beneficiaries, and geographies. These help spot circular payments, layering, or collusion.
3. Narrative Generation
AI-generated case narratives can summarise key findings and explain the decision to escalate or dismiss an alert. This saves time and ensures consistency in reporting.
4. Investigator Workflow
Assign tasks, track time-to-resolution, and route high-risk alerts to senior reviewers — all within the system.
5. Integration with STR Filing
Once an alert is confirmed as suspicious, the system should auto-fill suspicious transaction report (STR) templates for MAS submission.
Common Challenges Without Proper Tools
Many institutions still struggle with manual or legacy investigation processes:
- Copy-pasting between systems and spreadsheets
- Investigating the same customer multiple times due to siloed alerts
- Missing deadlines for STR filing
- Poor audit trails, leading to compliance risk
In high-volume environments like Singapore’s fintech hubs or retail banks, these inefficiencies create operational drag.
Real-World Example: Account Takeover Fraud via Fintech Wallets
An e-wallet provider in Singapore noticed a spike in high-value foreign exchange transactions.
Upon investigation, the team found:
- Victim accounts were accessed via compromised emails
- Wallet balances were converted into EUR/GBP instantly
- Funds were moved to mule accounts and out to crypto exchanges
Using an investigation tool with network mapping and device fingerprinting, the compliance team:
- Identified shared mule accounts across multiple victims
- Escalated the case to the regulator within 24 hours
- Blocked future similar transactions using rule updates
Tookitaki’s FinCense: Investigation Reinvented
Tookitaki’s FinCense platform provides end-to-end investigation capabilities designed for Singapore’s regulatory and operational needs.
Features That Matter:
- FinMate: An AI copilot that analyses alerts, recommends actions, and drafts case narratives
- Smart Disposition: Automatically generates case summaries and flags key findings
- Unified Case Management: Investigators work from a single dashboard that integrates monitoring, screening, and risk scoring
- MAS-Ready Reporting: Customisable templates for local regulatory formats
- Federated Intelligence: Access 1,200+ community-driven typologies from the AFC Ecosystem to cross-check against ongoing cases
Results From Tookitaki Clients:
- 72% fewer false positives
- 3.5× faster resolution times
- STR submission cycles shortened by 60%
Regulatory Expectations from MAS
Under MAS guidelines, financial institutions must:
- Have effective alert management processes
- Ensure timely investigation and STR submission
- Maintain records of all investigations and decisions
- Demonstrate scenario tuning and effectiveness reviews
A modern AML investigation tool supports all these requirements, reducing operational and audit burden.
AML Investigation and Emerging Threats
1. Deepfake-Fuelled Impersonation
Tools must validate biometric data and voiceprints to flag synthetic identities.
2. Crypto Layering
Graph-based tracing of wallet addresses is increasingly vital as laundering moves to decentralised finance.
3. Mule Account Clusters
AI-based clustering tools can identify unusual movement patterns across otherwise low-risk individuals.
4. Instant Payments Risk
Real-time investigation support is needed for PayNow, FAST, and other instant channels.
How to Evaluate a Vendor
Ask these questions:
- Can your tool integrate with our current transaction monitoring system?
- How do you handle false positive reduction?
- Do you support scenario simulation and tuning?
- Is your audit trail MAS-compliant?
- Can we import scenarios from other institutions (e.g. AFC Ecosystem)?
Looking Ahead: The Future of AML Investigations
AML investigations are evolving from reactive tasks to intelligence-led workflows. Tools are getting:
- Agentic AI: Copilots like FinMate suggest next steps, reducing guesswork
- Community-Driven: Knowledge sharing through federated systems boosts preparedness
- More Visual: Risk maps, entity graphs, and timelines help understand complex flows
- Smarter Thresholds: ML-driven dynamic thresholds reduce alert fatigue
Conclusion: Investigation is Your Last Line of Defence
In an age of instant payments, cross-border fraud, and synthetic identities, the role of AML investigation tools is mission-critical. Compliance officers in Singapore must be equipped with solutions that go beyond flagging transactions — they must help resolve them fast and accurately.
Tookitaki’s FinCense, with its AI-first approach and regulatory alignment, is redefining how Singaporean institutions approach AML investigations. It’s not just about staying compliant. It’s about staying smart, swift, and one step ahead of financial crime.
Fraud Detection Software for Banks: Inside the Digital War Room
Every day in Australia, fraud teams fight a silent battle. This is the story of how they do it, and the software helping them win.
Prologue: The Alert That Shouldn’t Have Happened
It is 2:14 pm on a quiet Wednesday in Sydney.
A fraud investigator at a mid-sized Australian bank receives an alert:
Attempted transfer: 19,800 AUD — flagged as “possible mule routing”.
The transaction looks ordinary.
Local IP.
Registered device.
Customer active for years.
Nothing about it screams fraud.
But the software sees something the human eye cannot:
a subtle deviation in typing cadence, geolocation drift over the past month, and a behavioural mismatch in weekday spending patterns.
This is not the customer.
This is someone pretending to be them.
The transfer is blocked.
The account is frozen.
A customer is protected from losing their savings.
This is the new frontline of fraud detection in Australian banking.
A place where milliseconds matter.
Where algorithms, analysts, and behavioural intelligence work together in near real time.
And behind it all sits one critical layer: fraud detection software built for the world we live in now, not the world we used to live in.
Chapter 1: Why Fraud Detection Has Become a War Room Operation
Fraud has always existed, but digital banking has changed its scale, speed, and sophistication.
Australian banks are facing:
- Real-time scams through NPP
- Deepfake-assisted social engineering
- Mule networks recruiting on TikTok
- Synthetic IDs built from fragments of real citizens
- Remote access scams controlling customer devices
- Cross-border laundering through fintech rails
- Account takeover via phishing and malware
Fraud today is not one person trying their luck.
It is supply-chain crime.
And the only way banks can fight it is by transforming fraud detection into a dynamic, intelligence-led discipline supported by software that thinks, learns, adapts, and collaborates.
Chapter 2: What Modern Fraud Detection Software Really Does
Forget the outdated idea that fraud detection is simply about rules.
Modern software must:
- Learn behaviour
- Spot anomalies
- Detect device manipulation
- Understand transaction velocity
- Identify network relationships
- Analyse biometrics
- Flag mule-like patterns
- Predict risk, not just react to it
The best systems behave like digital detectives.
They observe.
They learn.
They connect dots humans cannot connect in real time.
Chapter 3: The Six Capabilities That Define Best-in-Class Fraud Detection Software
1. Behavioural Biometrics
Typing speed.
Mouse movement.
Pressure on mobile screens.
Session navigation patterns.
These signals reveal whether the person behind the device is the real customer or an impostor.
2. Device Intelligence
Device fingerprinting, jailbreak checks, emulator detection, and remote-access-trojan indicators now play a key role in catching account takeover attempts.
3. Network Link Analysis
Modern fraud does not occur in isolation.
Software must map:
- Shared devices
- Shared addresses
- Linked mule accounts
- Common beneficiaries
- Suspicious payment clusters
This is how syndicates are caught.
4. Real-Time Risk Scoring
Fraud cannot wait for batch jobs.
Software must analyse patterns as they happen and block or challenge the transaction instantly.
5. Cross-Channel Visibility
Fraud moves across onboarding, transfers, cards, wallets, and payments.
Detection must be omnichannel, not siloed.
6. Analyst Assistance
The best software does not overwhelm investigators.
It assists them by:
- Summarising evidence
- Highlighting anomalies
- Suggesting next steps
- Reducing noise
Fraud teams fight harder when the software fights with them.
Chapter 4: Inside an Australian Bank’s Digital Fraud Team
Picture this scene.
A fraud operations centre in Melbourne.
Multiple screens.
Live dashboards.
Analysts monitoring spikes in activity.
Suddenly, the software detects something:
A cluster of small transfers moving rapidly into multiple new accounts.
Amounts just below reporting thresholds.
Accounts opened within the last three weeks.
Behaviour consistent with mule recruitment.
This is not random.
This is an organised ring.
The fraud team begins tracing the pattern using network graphs visualised by the software.
Connections emerge.
A clear structure forms.
Multiple accounts tied to the same device.
Shared IP addresses across suburbs.
Within minutes, the team has identified a mule network operating across three states.
They block the accounts.
Freeze the funds.
Notify the authorities.
Prevent a chain of victims.
This is fraud detection software at its best:
Augmenting human instinct with machine intelligence.
Chapter 5: The Weaknesses of Old Fraud Detection Systems
Some Australian banks still rely on systems that:
- Use rigid rules
- Miss behavioural patterns
- Cannot detect deepfakes
- Struggle with NPP velocity
- Generate high false positives
- Cannot identify linked accounts
- Have no real-time capabilities
- Lack explainability for AUSTRAC or internal audit
These systems were designed for a slower era, when payments were not instantaneous and criminals did not use automation.
Old systems do not fail because they are old.
They fail because the world has changed.
Chapter 6: What Australian Banks Should Look For in Fraud Detection Software (A Modern Checklist)
1. Real-Time Analysis for NPP
Detection must be instant.
2. Behavioural Intelligence
Software should learn how customers normally behave and identify anomalies.
3. Mule Detection Algorithms
Australia is experiencing a surge in mule recruitment.
This is now essential.
4. Explainability
Banks must be able to justify fraud decisions to regulators and customers.
5. Cross-Channel Intelligence
Transfers, cards, NPP, mobile apps, and online banking must speak to each other.
6. Noise Reduction
Software must reduce false positives, not amplify them.
7. Analyst Enablement
Investigators should receive context, not clutter.
8. Scalability for Peak Fraud Events
Fraud often surges during crises, holidays, and scams going viral.
9. Localisation
Australian fraud patterns differ from other regions.
10. Resilience
APRA CPS 230 demands operational continuity and strong third-party governance.
Fraud software is now part of a bank’s resilience framework, not just its compliance toolkit.
Chapter 7: How Tookitaki Approaches Fraud Detection
Tookitaki’s approach to fraud detection is built around one core idea:
fraudsters behave like networks, not individuals.
FinCense analyses risk across relationships, devices, behaviours, and transactions to detect patterns traditional systems miss.
What makes it different:
1. A Behaviour-First Model
Instead of relying on static rules, the system understands customer behaviour over time.
This helps identify anomalies that signal account takeover or mule activity.
2. Investigation Intelligence
Tookitaki supports analysts with enriched context, visual evidence, and prioritised risks, reducing decision fatigue.
3. Multi-Channel Detection
Fraud does not stay in one place, and neither does the software.
It connects signals across payments, wallets, online banking, and transfers.
4. Designed for Both Large and Community Banks
Institutions such as Regional Australia Bank benefit from accurate detection without operational complexity.
5. Built for Real-Time Environments
FinCense supports high-velocity payments, enabling institutions to detect risk at NPP speed.
Tookitaki is not designed to overwhelm banks with rules.
It is designed to give them a clear picture of risk in a world where fraud changes daily.
Chapter 8: The Future of Fraud Detection in Australian Banking
1. Deepfake-Resistant Identity Verification
Banks will need technology that can detect video, voice, and biometric spoofing.
2. Agentic AI Assistants for Investigators
Fraud teams will have copilots that surface insights, summarise cases, and provide investigative recommendations.
3. Network-Wide Intelligence Sharing
Banks will fight fraud together, not alone, through federated learning and shared typology networks.
4. Real-Time Customer Protection
Banks will block suspicious payments before they leave the customer’s account.
5. Predictive Fraud Prevention
Systems will identify potential mule behaviour before the account becomes active.
Fraud detection will become proactive, not reactive.
Conclusion
Fraud detection software is no longer a technical add-on.
It is the digital armour protecting customers, banks, and the integrity of the financial system.
The frontline has shifted.
Criminals operate as organised networks, use automation, manipulate devices, and exploit real-time payments.
Banks need software built for this reality, not yesterday’s.
The right fraud detection solution gives banks something they cannot afford to lose:
time, clarity, and confidence.
Because in today’s Australian financial landscape, fraud moves fast.
Your software must move faster.
AML Software in Australia: The 7 Big Questions Every Bank Should Be Asking in 2025
Choosing AML software used to be a technical decision. In 2025, it has become one of the most strategic choices a bank can make.
Introduction
Australia’s financial sector is entering a defining moment. Instant payments, cross-border digital crime, APRA’s tightening expectations, AUSTRAC’s data scrutiny, and the rise of AI are forcing banks to rethink their entire compliance tech stack.
At the centre of this shift sits one critical question: what should AML software actually do in 2025?
This blog does not give you a shopping list or a vendor comparison.
Instead, it explores the seven big questions every Australian bank, neobank, and community-owned institution should be asking when evaluating AML software. These are the questions that uncover risk, expose limitations, and reveal whether a solution is built for the next decade, not the last.
Let’s get into them.
Question 1: Does the AML Software Understand Risk the Way Australia Defines It?
Most AML systems were designed with global rule sets that do not map neatly to Australian realities.
Australia has:
- Distinct PEP classifications
- Localised money mule typologies
- Syndicated fraud patterns unique to the region
- NPP-driven velocity in payment behaviour
- AUSTRAC expectations around ongoing due diligence
- APRA’s new focus on operational resilience
AML software must be calibrated to Australian behaviours, not anchored to American or European assumptions.
What to look for
- Localised risk models trained on Australian financial behaviour
- Models that recognise local account structures and payment patterns
- Typologies relevant to the region
- Adaptability to NPP and emerging scams affecting Australians
- Configurable rule logic for Australia’s regulatory environment
If software treats all markets the same, its risk understanding will always be one step behind Australian criminals.
Question 2: Can the Software Move at the Speed of NPP?
The New Payments Platform changed everything.
What used to be processed in hours is now settled in seconds.
This means:
- Risk scoring must be real time
- Monitoring must be continuous
- Alerts must be triggered instantly
- Investigators need immediate context, not post-fact analysis
Legacy systems built for batch processing simply cannot keep up with the velocity or volatility of NPP transactions.
What to look for
- True real-time screening and monitoring
- Sub-second scoring
- Architecture built for high-volume environments
- Scalability without performance drops
- Real-time alert triaging
If AML software cannot respond before a payment settles, it is already too late.
Question 3: Does the Software Reduce False Positives in a Meaningful Way?
Every vendor claims they reduce false positives.
The real question is how and by how much.
In Australia, many banks spend up to 80 percent of their AML effort investigating low-value alerts. This creates fatigue, delays, and inconsistent decisions.
Modern AML software must:
- Prioritise alerts based on true behavioural risk
- Provide contextual information alongside flags
- Reduce noise without reducing sensitivity
- Identify relationships, patterns, and anomalies that rules alone miss
What to look for
- Documented false positive reduction numbers
- Behavioural analytics that distinguish typical from atypical activity
- Human-in-the-loop learning
- Explainable scoring logic
- Tiered risk categorisation
False positives drain resources.
Reducing them responsibly is a competitive advantage.
Question 4: How Does the Software Support Investigator Decision-Making?
Analysts are the heart of AML operations.
Software should not just alert them. It should empower them.
The most advanced AML platforms are moving toward investigator-centric design, helping analysts work faster, more consistently, and with greater clarity.
What to look for
- Clear narratives attached to alerts
- Visual network link analysis
- Relationship mapping
- Easy access to KYC, transaction history, and behaviour insights
- Tools that surface relevant context without manual digging
If AML software only generates alerts but does not explain them, it is not modern software. It is a data dump.
Question 5: Is the AML Software Explainable Enough for AUSTRAC?
AUSTRAC’s reviews increasingly require banks to justify their risk models and demonstrate why a decision was made.
AML software must show:
- Why an alert was generated
- What data was used
- What behavioural markers contributed
- How the system ranked or prioritised risk
- How changes over time affected decision logic
Explainability is now a regulatory requirement, not a bonus feature.
What to look for
- Decision logs
- Visual explanations
- Feature attribution for risk scoring
- Scenario narratives
- Governance dashboards
Opaque systems that cannot justify their reasoning leave institutions vulnerable during audits.
Question 6: How Well Does the AML Software Align With APRA’s CPS 230 Expectations?
Operational resilience is now a board-level mandate.
AML software sits inside the cluster of critical systems APRA expects institutions to govern closely.
This includes:
- Third-party risk oversight
- Business continuity
- Incident management
- Data quality controls
- Outsourcing governance
AML software is no longer evaluated only by compliance teams.
It must satisfy risk, technology, audit, and resilience requirements too.
What to look for
- Strong uptime track record
- Clear incident response procedures
- Transparent service level reporting
- Secure and compliant hosting
- Tested business continuity measures
- Clear vendor accountability and control frameworks
If AML software cannot meet CPS 230 expectations, it cannot meet modern banking expectations.
Question 7: Will the Software Still Be Relevant Five Years From Now?
This is the question few institutions ask, but the one that matters most.
AML software is not a one-year decision. It is a multi-year partnership.
To future-proof compliance, banks must look beyond features and evaluate adaptability.
What to look for
- A roadmap that includes new crime types
- AI models that learn responsibly
- Agentic support tools that help investigators
- Continuous updates without major uplift projects
- Collaborative intelligence capabilities
- Strong alignment with emerging AML trends in Australia
This is where vendors differentiate themselves.
Some provide tools.
A few provide evolution.
A Fresh Look at Tookitaki
Tookitaki has emerged as a preferred AML technology partner among several banks across Asia-Pacific, including institutions in Australia, because it focuses less on building features and more on building confidence.
Confidence that alerts are meaningful.
Confidence that the system is explainable.
Confidence that operations remain stable.
Confidence that investigators have support.
Confidence that intelligence keeps evolving.
Rather than positioning AML as a fixed set of rules, Tookitaki approaches it as a learning discipline.
Its platform, FinCense, helps Australian institutions strengthen:
- Real time monitoring capability
- Consistency in analyst decisions
- Model transparency for AUSTRAC
- Operational resilience for APRA expectations
- Adaptability to emerging typologies
- Scalability for both large and community institutions like Regional Australia Bank
This is AML software designed not only to detect crime, but to grow with the institution.
Conclusion
AML software in Australia is at a crossroads.
The era of legacy rules, static scenarios, and batch processing is ending.
Banks now face a new set of expectations driven by speed, transparency, resilience, and intelligence.
The seven questions in this guide cut through the noise. They help institutions evaluate AML software not as a product, but as a long-term strategic partner for risk management.
Australia’s financial sector is changing quickly.
The right AML software will help banks move confidently into that future.
The wrong one will hold them back.
Pro tip: The strongest AML systems are not just built on good software. They are built on systems that understand the world they operate in, and evolve alongside it.