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Understanding PEPs: Definition, Types & Risk Levels According to FATF

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Tookitaki
12 Oct 2021
7 min
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The term "Politically Exposed Person" or PEP often comes up in conversations around anti-money laundering and combating the financing of terrorism (AML/CFT). But what exactly does it mean, and why should you care? When it comes to understanding what is a pep, it is essential to comprehend that these individuals possess great power, influence, and consequently, a higher propensity to engage in illicit activities such as bribery or money laundering

In this comprehensive guide, we'll explore the intricate world of PEPs, as outlined by the Financial Action Task Force (FATF), the global money laundering and terrorist financing watchdog, and shed light on the significance of PEP screening in financial institutions.

What is a PEP and PEP according to FATF

A Politically Exposed Person (PEP) is an individual who has been entrusted with a prominent public function, either domestically or internationally. Due to their position and influence, PEPs are at a higher risk of being involved in bribery, corruption, or money laundering. The Financial Action Task Force (FATF) provides a detailed framework to understand the definition and types of PEPs, which serves as a global standard for nations and organizations alike.

Examples of PEP

PEPs are not just confined to politicians. They can also include senior government officials, judicial authorities, military officers, and even high-ranking members of state-owned enterprises. For instance, a mayor of a large city, a general in the army, or a CEO of a government-owned oil company could all be considered PEPs.

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PEPs, as per the FATF classification, embody individuals who currently serve or previously held a significant public function in a country. The high-risk nature of these roles is often associated with an enhanced likelihood of their involvement in financial crimes. This susceptibility stems from their ability to influence decisions and control resources, which can potentially be exploited for personal gains. The following categories encapsulate the diverse roles that a PEP may hold:

  • Government Roles: High-ranking officials in either the legislative, executive, or judiciary branches of government. This can range from members of parliament and supreme court judges to ambassadors and diplomats.
  • Organizational Roles: Individuals holding prominent positions in governmental commercial enterprises or political parties. This could include board members of a central bank, party leaders, or high-ranking military officials.
  • Associations: Close associates, either through social or professional connections, to a PEP. This could encompass family members, close relatives, or individuals holding beneficial ownership of a legal entity in which the government is a stakeholder.

Types of PEP Defined by FATF

Bearing in mind the broad scope of what is a PEP, the FATF has further divided PEPs into three primary categories, namely Foreign, Domestic, and International Organization PEPs.

  • Foreign PEPs: These are individuals who hold or have held prominent public positions in a foreign country. The risk associated with foreign PEPs is generally higher due to the challenges in obtaining accurate and timely data about these individuals.
  • Domestic PEPs: These refer to individuals who hold or have held significant public functions within their home country. While they also pose a risk, it is generally lower than that of their foreign counterparts due to better access to information.
  • International Organization PEPs: These are individuals who hold or have held a high-ranking position in an international organization. The risk associated with these PEPs can vary depending on factors such as the organization's transparency, the individual's role, and the level of oversight exercised.
HOW FATF CLASSIFIES PEPs

PEP Risk Levels

Understanding the PEP definition is only the first step in managing financial crime risks. The subsequent step involves a detailed risk assessment, which is crucial for regulated corporations dealing with PEPs. 

Risk associated with PEPs is generally assessed on multiple factors including the corruption level of the country they originate from, the nature of their role, and their access to significant financial resources. It's a tiered approach, ranging from low to high risk, and the scrutiny applied varies accordingly. The FATF outlines four levels of risk for PEPs:

  • Low-level risk: This encompasses supranational or international business officials and senior functionaries, as well as members of local, state, district, and urban assemblies.
  • Medium/low-level risk: This category includes top officials of government boards and state-owned enterprises such as heads of judiciaries, banks, military, law enforcement, and high-ranked civil servants in state agencies and religious organizations.
  • Medium/high-level risk: This segment includes individuals who are members of the government, parliament, judiciary, banks, law enforcement, military, and prominent political parties.
  • High-level risk: This is the highest risk category and includes heads of state or government, senior politicians, judicial or military officials, senior executives of state-owned corporations, and important party officials.

Red Flags to Watch Out for PEPs by FATF

Recognizing the potential risks associated with PEPs, the FATF has highlighted several red flags that can indicate suspicious activity. These indicators act as warning signals for possible financial abuse and can help corporations detect and control potential illegal activities involving PEPs. Here are some key red flags outlined by the FATF:

  • Unusual Wealth: A drastic and unexplained increase in a PEP's wealth can be a significant red flag.
  • Offshore Accounts: Frequent use of offshore accounts without a logical or apparent reason.
  • Shell Companies: Involvement in operations through shell companies that lack transparency.
  • Identity Concealment: PEPs might attempt to hide their identities to evade scrutiny. This could involve assigning legal ownership to another individual, frequently interacting with intermediaries, or using corporate structures to obscure ownership.
  • Suspicious Behavior: This could include secrecy about the source of funds, providing false or insufficient information, eagerness to justify business dealings, denial of an entry visa, or frequent movement of funds across countries.
  • Company Position: The PEP's position within the company could also raise concerns. This could include having control over the company's funds, operations, policies, or anti-money laundering/terrorist financing mechanisms.
  • Industry: Certain industries are considered high-risk due to their nature and the potential for exploitation. This could include banking and finance, military and defense, businesses dealing with government agencies, construction, mining and extraction, and public goods provision.

Changes in PEP Status: An Evolving Landscape

The PEP landscape has witnessed several changes over the years, primarily in the definition and monitoring of PEPs. The term PEP was initially used to describe senior government officials and their immediate family members only. However, the definition has since been expanded to include individuals who hold prominent positions in international organizations, as well as their close associates. This change reflects the evolving nature of the global economy, where non-governmental organizations and international institutions wield significant power and influence.

The monitoring of PEPs has also evolved. Previously, self-disclosure was the primary method to identify a PEP, which was often ineffective, as some PEPs chose to hide their status or failed to disclose it accurately. Today, governments and financial institutions have access to sophisticated databases and screening tools, thanks to advanced AML compliance software, enhancing the ability to detect potential money laundering and corruption risks associated with PEPs.

Why PEP Screening is Important

Financial crimes pose a significant global concern, and organizations are obligated to comply with anti-money laundering regulations to combat such crimes. As part of this compliance, institutions must identify customers who may have a higher risk of being involved in financial crimes. PEP screening is a crucial process during account opening that helps identify high-risk customers and prevent financial crimes. Failure to adhere to these screening procedures can result in penalties from AML regulators for non-compliant organizations.

PEP screening is crucial because these individuals are at a higher risk of involvement in bribery, corruption, and money laundering due to their position and influence. Failure to conduct proper screening can result in heavy fines for the institution and reputational damage. More importantly, it can facilitate financial crimes that have societal impacts.

How Tookitaki Can Help

As an award-winning regulatory technology (RegTech) company, we are revolutionising financial crime detection and prevention for banks and fintechs with our cutting-edge solutions. We provide an end-to-end, AI-powered AML compliance platform, named the Anti-Money Laundering Suite (AMLS), with modular solutions that help financial institutions deal with the ever-changing financial crime landscape.

Our Smart Screening solution provides accurate screening of names and transactions across many languages and a continuous monitoring framework for comprehensive risk management. Our powerful name-matching engine screens and prioritises all name search hits, helping to achieve 80% precision and 90% recall levels in screening programmes of financial institutions.

The features of our Smart Screening solution include:

  • Advanced machine learning engine that powers  50+ name-matching techniques
  • Comprehensive matching enabled by the use of multiple attributes i.e; name, address, gender, date of birth, incorporation and more
  • Individual language models to improve accuracy across 18+ languages and 10 different scripts
  • Built-in transliteration engine for effective cross-lingual matching
  • Scalable to support massive watchlist data

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Final Thoughts

In order to mitigate the risks associated with PEPs, it is imperative for financial institutions to implement robust PEP screening processes within their compliance framework. By doing so, they not only shield themselves from potential involvement in illicit activities but also safeguard their reputation and actively contribute to the global fight against financial crime.

Tookitaki's innovative Smart Screening solution offers precise screening of customers and transactions against sanctions, PEPs, Adverse Media, and various watchlists in real-time across over 22 languages. With an impressive 90% accuracy rate, this cutting-edge technology utilizes 12 advanced name-matching techniques on 7 customer attributes, incorporating a multi-stage matching mechanism and cross-lingual matching capabilities. To explore more about the capabilities of Tookitaki's screening solution, schedule a consultation session by clicking the link below.

Frequently Asked Questions (FAQs)

What is a PEP according to FATF?

A PEP, according to FATF, is an individual who is or has been entrusted with a prominent public function, making them a higher risk for involvement in bribery and corruption.

What are some examples of PEPs?

Examples include politicians, high-ranking military officials, and senior executives in state-owned corporations.

Why is PEP screening important?

PEP screening is crucial for mitigating the risk of financial crimes like money laundering and corruption, which could result in severe penalties and reputational damage for the financial institution involved.

What are the types of PEPs defined by FATF?

FATF defines several types of PEPs including domestic, foreign, and those in international organisations.

What are some red flags to watch for in PEPs?

Red flags include sudden wealth accumulation, frequent use of offshore accounts, and involvement with shell companies.

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Smarter Investigations: The Rise of AML Investigation Tools in Australia

In the battle against financial crime, the right AML investigation tools turn data overload into actionable intelligence.

Australian compliance teams face a constant challenge — growing transaction volumes, increasingly sophisticated money laundering techniques, and tighter AUSTRAC scrutiny. In this environment, AML investigation tools aren’t just nice-to-have — they’re essential for turning endless alerts into fast, confident decisions.

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Why AML Investigations Are Getting Harder in Australia

1. Explosion of Transaction Data

With the New Payments Platform (NPP) and cross-border corridors, institutions must monitor millions of transactions daily.

2. More Complex Typologies

From mule networks to shell companies, layering techniques are harder to detect with static rules alone.

3. Regulatory Expectations

AUSTRAC demands timely and accurate Suspicious Matter Reports (SMRs). Delays or incomplete investigations can lead to penalties and reputational damage.

4. Resource Constraints

Skilled AML investigators are in short supply. Teams must do more with fewer people — making efficiency critical.

What Are AML Investigation Tools?

AML investigation tools are specialised software platforms that help compliance teams analyse suspicious activity, prioritise cases, and document findings for regulators.

They typically include features such as:

  • Alert triage and prioritisation
  • Transaction visualisation
  • Entity and relationship mapping
  • Case management workflows
  • Automated reporting capabilities

Key Features of Effective AML Investigation Tools

1. Integrated Case Management

Centralise all alerts, documents, and investigator notes in one platform.

2. Entity Resolution & Network Analysis

Link accounts, devices, and counterparties to uncover hidden connections in laundering networks.

3. Transaction Visualisation

Graph-based displays make it easier to trace fund flows and identify suspicious patterns.

4. AI-Powered Insights

Machine learning models suggest likely outcomes, surface overlooked anomalies, and flag high-risk entities faster.

5. Workflow Automation

Automate repetitive steps like KYC refresh requests, sanctions re-checks, and document retrieval.

6. Regulator-Ready Reporting

Generate Suspicious Matter Reports (SMRs) and audit logs that meet AUSTRAC’s requirements.

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Why These Tools Matter in Australia’s Compliance Landscape

  • Speed: Fraud and laundering through NPP happen in seconds — investigations need to move just as fast.
  • Accuracy: AI-driven tools reduce false positives, ensuring analysts focus on real threats.
  • Compliance Assurance: Detailed audit trails prove that due diligence was carried out thoroughly.

Use Cases in Australia

Case 1: Cross-Border Layering Detection

An Australian bank flagged multiple small transfers to different ASEAN countries. The AML investigation tool mapped the network, revealing links to a known mule syndicate.

Case 2: Crypto Exchange Investigations

AML tools traced a high-value Bitcoin-to-fiat conversion back to an account flagged in a sanctions database, enabling rapid SMR submission.

Advanced Capabilities to Look For

Federated Intelligence

Access anonymised typologies and red flags from a network of institutions to spot emerging threats faster.

Embedded AI Copilot

Assist investigators in summarising cases, recommending next steps, and even drafting SMRs.

Scenario Simulation

Test detection scenarios against historical data before deploying them live.

Spotlight: Tookitaki’s FinCense and FinMate

FinCense integrates investigation workflows directly into its AML platform, while FinMate, Tookitaki’s AI investigation copilot, supercharges analyst productivity.

  • Automated Summaries: Generates natural language case narratives for internal and regulatory reporting.
  • Risk Prioritisation: Highlights the highest-risk cases first.
  • Real-Time Intelligence: Pulls in global typology updates from the AFC Ecosystem.
  • Full Transparency: Glass-box AI explains every decision, satisfying AUSTRAC’s audit requirements.

With FinCense and FinMate, Australian institutions can cut investigation times by up to 50% — without compromising quality.

Conclusion: From Data to Decisions — Faster

The volume and complexity of alerts in modern AML programmes make manual investigation unsustainable. The right AML investigation tools transform scattered data into actionable insights, helping compliance teams stay ahead of both criminals and regulators.

Pro tip: Choose tools that not only investigate faster, but also learn from every case — making your compliance programme smarter over time.

Smarter Investigations: The Rise of AML Investigation Tools in Australia
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Smarter Defences: How Machine Learning is Transforming Fraud Detection in Philippine Banking

Fraud in banking has never been faster, smarter, or more relentless — and neither have the defences.

In the Philippines, the rapid rise of digital banking, mobile wallets, and instant payments has created unprecedented opportunities for growth — and for fraudsters. From account takeovers to synthetic identity scams, financial institutions are under constant attack. Traditional rule-based detection systems, while useful, are no longer enough. Enter machine learning (ML) — the technology redefining fraud detection by spotting suspicious activity in real time and adapting to new threats before they cause damage.

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The Growing Fraud Threat in Philippine Banking

Digital banking adoption in the Philippines has surged in recent years, driven by initiatives like the BSP’s Digital Payments Transformation Roadmap and the expansion of fintech services. While these advancements boost financial inclusion, they also open the door to fraud.

According to the Bankers Association of the Philippines, reported cyber fraud incidents have increased steadily, with phishing, account takeover (ATO), and card-not-present (CNP) fraud among the top threats.

Key trends include:

  • Instant payment exploitation: Fraudsters leveraging PESONet and InstaPay for rapid fund transfers.
  • Social engineering scams: Convincing victims to disclose personal and banking details.
  • Cross-border fraud networks: Syndicates funnelling illicit funds via multiple jurisdictions.

In this environment, speed, accuracy, and adaptability are critical — qualities where ML excels.

Why Traditional Fraud Detection Falls Short

Rule-based fraud detection systems rely on predefined scenarios (e.g., flagging transactions over a certain threshold or unusual logins from different IP addresses). While they can catch known patterns, they struggle with:

  • Evolving tactics: Fraudsters quickly adapt once they know the rules.
  • False positives: Too many alerts waste investigator time and frustrate customers.
  • Lack of contextual awareness: Rules can’t account for the nuances of customer behaviour.

This is where machine learning transforms the game.

How Machine Learning Enhances Fraud Detection

1. Pattern Recognition Beyond Human Limits

ML models can process millions of transactions in real time, identifying subtle anomalies in behaviour — such as unusual transaction timing, frequency, or geolocation.

2. Continuous Learning

Unlike static rules, ML systems learn from new data. When fraudsters switch tactics, the model adapts, ensuring defences stay ahead.

3. Reduced False Positives

ML distinguishes between legitimate unusual behaviour and true fraud, cutting down on unnecessary alerts. This not only saves resources but improves customer trust.

4. Predictive Capability

Advanced algorithms can predict the likelihood of a transaction being fraudulent based on historical and behavioural data, enabling proactive intervention.

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Key Machine Learning Techniques in Banking Fraud Detection

Supervised Learning

Models are trained using labelled datasets — past transactions marked as “fraud” or “legitimate.” Over time, they learn the characteristics of fraudulent activity.

Unsupervised Learning

Used when there’s no labelled data, these models detect outliers and anomalies without prior examples, ideal for spotting new fraud types.

Reinforcement Learning

The system learns by trial and error, optimising decision-making as it receives feedback from past outcomes.

Natural Language Processing (NLP)

NLP analyses unstructured data such as emails, chat messages, or KYC documents to detect potential fraud triggers.

Real-World Fraud Scenarios in the Philippines Where ML Makes a Difference

  1. Account Takeover (ATO) Fraud – ML flags login attempts from unusual devices or geolocations while analysing subtle session behaviour patterns.
  2. Loan Application Fraud – Models detect inconsistencies in credit applications, cross-referencing applicant data with external sources.
  3. Payment Mule Detection – Identifying suspicious fund flows in real time, such as rapid inbound and outbound transactions in newly opened accounts.
  4. Phishing-Driven Transfers – Correlating unusual fund movement with compromised accounts reported across multiple banks.

Challenges in Implementing ML for Fraud Detection in the Philippines

  • Data Quality and Availability – ML models need vast amounts of clean, structured data. Gaps or inaccuracies can reduce effectiveness.
  • Regulatory Compliance – BSP regulations require explainability in AI models; “black box” ML can be problematic without interpretability tools.
  • Talent Gap – Limited availability of data science and ML experts in the local market.
  • Integration with Legacy Systems – Many Philippine banks still run on legacy infrastructure, complicating ML deployment.

Best Practices for Deploying ML-Based Fraud Detection

1. Start with a Hybrid Approach

Combine rule-based and ML models initially to ensure smooth transition and maintain compliance.

2. Ensure Explainability

Use explainable AI (XAI) frameworks so investigators and regulators understand why a transaction was flagged.

3. Leverage Federated Learning

Share intelligence across institutions without exposing raw data, enhancing detection of cross-bank fraud schemes.

4. Regular Model Retraining

Update models with the latest fraud patterns to stay ahead of evolving threats.

5. Engage Compliance Early

Work closely with risk and compliance teams to align ML use with BSP guidelines.

The Tookitaki Advantage: The Trust Layer to Fight Financial Crime

Tookitaki’s FinCense platform is built to help Philippine banks combat fraud and money laundering with Agentic AI — an advanced, explainable AI framework aligned with global and local regulations.

Key benefits for fraud detection in banking:

  • Real-time risk scoring on every transaction.
  • Federated intelligence from the AFC Ecosystem to detect emerging fraud typologies seen across the region.
  • Lower false positives through adaptive models trained on both local and global data.
  • Explainable decision-making that meets BSP requirements for transparency.

By combining advanced ML techniques with collaborative intelligence, FinCense gives banks in the Philippines the tools they need to protect customers, meet compliance standards, and reduce operational costs.

Conclusion: Staying Ahead of the Curve

Fraudsters in the Philippines are becoming more sophisticated, faster, and harder to trace. Relying on static, rules-only systems is no longer an option. Machine learning empowers banks to detect fraud in real time, reduce false positives, and adapt to ever-changing threats — all while maintaining compliance.

For institutions aiming to build trust in a rapidly digitising market, the path forward is clear: invest in ML-powered fraud detection now, and make it a core pillar of your risk management strategy.

Smarter Defences: How Machine Learning is Transforming Fraud Detection in Philippine Banking
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Stopping Fraud in Its Tracks: The Future of Transaction Fraud Detection in Singapore

Fraud doesn’t knock—it slips through unnoticed until it’s too late.

As digital payments accelerate across Singapore, financial institutions face a mounting challenge: detecting fraudulent transactions in real time, without slowing down legitimate users. From phishing scams and mule accounts to synthetic identities and account takeovers, transaction fraud has become smarter, faster, and harder to catch.

This blog explores how transaction fraud detection is evolving in Singapore, the gaps still present in legacy systems, and how AI-driven tools are helping financial institutions fight back.

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Why Transaction Fraud Detection Is Critical in Singapore

Singapore’s position as a fintech hub comes with exposure to increasingly sophisticated fraud schemes. According to the Singapore Police Force, scam-related crimes in 2024 accounted for over 70% of all crimes reported, with transaction fraud and unauthorised transfers making up a large portion of the losses.

The government’s drive for real-time payments — from PayNow to FAST — adds pressure on banks and fintechs to detect fraud instantly, without delaying genuine transactions.

Missed fraud isn’t just a financial risk — it erodes trust. And in Singapore’s tightly regulated environment, trust is everything.

Types of Transaction Fraud Facing Financial Institutions

Understanding the tactics fraudsters use is the first step toward stopping them. In Singapore, common forms of transaction fraud include:

1. Account Takeover (ATO)

Fraudsters use stolen credentials to gain control over an account and initiate transfers, bill payments, or cash withdrawals — often within minutes.

2. Social Engineering Scams

Victims are tricked into authorising payments themselves under false pretences — for example, investment scams, job scams, or fake relationships.

3. Money Muling

Fraudsters use mule accounts — often belonging to unsuspecting individuals — to route stolen or laundered funds through multiple hops.

4. Real-Time Payment Exploits

With instant transfer systems, once funds are sent, they’re often impossible to recover. Fraudsters exploit this urgency and invisibility.

5. Business Email Compromise (BEC)

Corporate payments are manipulated through phishing or spoofing attacks, redirecting funds to illicit accounts under false vendor names.

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Challenges in Transaction Fraud Detection

Despite investment in fraud controls, many Singaporean financial institutions still face persistent roadblocks:

1. High False Positives

Basic rules-based systems raise alerts for normal user behaviour, overwhelming fraud teams and increasing friction for genuine customers.

2. Lack of Real-Time Detection

Many systems rely on batch processing or delayed scoring, leaving gaps for fraudsters to exploit instant payment rails.

3. Inability to Detect Novel Patterns

Fraudsters constantly change tactics. Systems that only recognise known fraud signatures are easily bypassed.

4. Poor Cross-Border Visibility

Singapore is deeply integrated into global financial flows. A lack of insight into transaction trails beyond borders makes it harder to detect layered laundering and syndicated fraud.

What Effective Transaction Fraud Detection Looks Like Today

Modern fraud detection is about being predictive, not just reactive. Here's what best-in-class solutions offer:

AI + Machine Learning

Rather than using only static rules, intelligent systems learn from historical patterns, adapt to new behaviours, and improve accuracy over time.

Behavioural Profiling

These systems build user profiles based on login patterns, spending habits, device data, and more — flagging anything outside the norm in real time.

Network Analysis

Sophisticated fraud often involves mule networks or linked entities. Graph analysis helps identify suspicious linkages between accounts.

Federated Intelligence Sharing

Platforms like Tookitaki’s AFC Ecosystem allow institutions to benefit from typologies and red flags contributed by others — without sharing sensitive data.

Explainable AI

Regulators require transparency. Solutions must explain why a transaction was flagged, not just that it was.

How Tookitaki Is Powering Smarter Fraud Detection

Tookitaki’s FinCense platform is purpose-built to detect transaction fraud in real time. Here’s how it helps Singapore-based institutions stay ahead:

  • Agentic AI Framework: Modular AI agents continuously scan transactions, user behaviour, and risk context to identify fraud patterns — even emerging ones.
  • Scenario-Based Detection: Leverages real-world fraud scenarios from the AFC Ecosystem, including scams unique to Southeast Asia like fake job recruitment and QR-enabled mule layering.
  • Real-Time Simulation & Threshold Optimisation: Before deploying rules, institutions can simulate detection impact to reduce false positives.
  • Smart Disposition Engine: AI-generated summaries assist investigators by surfacing key risk insights for flagged transactions.
  • Federated Learning: Combines privacy-preserving AI with community-sourced intelligence for faster, more adaptive detection.

Whether you’re a digital bank, a payment gateway, or a traditional financial institution, FinCense provides the flexibility, speed, and accuracy needed for the Singaporean fraud landscape.

Key Strategies for Singaporean Firms to Strengthen Fraud Defences

1. Upgrade From Rule-Based to Hybrid Systems

A combination of dynamic rules and machine learning provides greater precision and adaptability.

2. Focus on Early Detection

Identify mule accounts, layered transfers, and behaviour anomalies before the fraud is completed.

3. Enable Seamless Analyst Workflows

Reduce alert fatigue with AI-driven prioritisation and investigation summaries.

4. Join Intelligence-Sharing Networks

Collaborate with platforms like the AFC Ecosystem to keep up with evolving fraud typologies.

5. Design for Real-Time Action

Ensure that fraud decisions can be made in milliseconds — and tie detection systems directly to block/hold actions.

Conclusion: Fraudsters Are Getting Smarter. Are You?

In Singapore’s fast-moving financial ecosystem, transaction fraud detection is no longer just a compliance function — it’s a competitive advantage.

Banks and fintechs that invest in modern, intelligent fraud prevention are not only protecting their bottom line — they’re protecting their brand and customer relationships.

📌 The future of fraud detection is proactive, predictive, and powered by community-led intelligence. Don’t just keep up — get ahead.

Stopping Fraud in Its Tracks: The Future of Transaction Fraud Detection in Singapore