Fraud Detection Using Machine Learning in Banking
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The financial landscape is evolving rapidly. With this evolution comes an increase in financial crimes, particularly fraud.
Financial institutions are constantly seeking ways to enhance their fraud detection and prevention mechanisms. Traditional methods, while effective to some extent, often fall short in the face of sophisticated fraudulent schemes.
Enter machine learning. This technology has emerged as a game-changer in the banking sector, particularly in fraud detection.
Machine learning algorithms can sift through vast volumes of transaction data, identifying patterns and anomalies indicative of fraudulent activities. This ability to learn from historical data and predict future frauds is revolutionising the way financial institutions approach fraud detection.
However, the implementation of machine learning in fraud detection is not without its challenges. Distinguishing between legitimate transactions and suspicious activity, ensuring data privacy, and maintaining regulatory compliance are just a few of the hurdles to overcome.
This article aims to provide a comprehensive overview of fraud detection using machine learning in banking. It will delve into the evolution of fraud detection, the role of machine learning, its implementation, and the challenges faced.
By the end, financial crime investigators and other professionals in the banking sector will gain valuable insights into this cutting-edge technology and its potential in enhancing their fraud detection strategies.
The Evolution of Fraud Detection in Banking
The banking sector has always been a prime target for fraudsters. Over the years, the methods used to commit fraud have evolved, becoming more complex and sophisticated.
In response, financial institutions have had to adapt their fraud detection systems. Traditional fraud detection methods relied heavily on rule-based systems and manual investigations. These systems were designed to flag transactions that met certain predefined criteria indicative of fraud.
However, as the volume of transactions increased with the advent of digital banking, these traditional systems began to show their limitations. They struggled to process the vast amounts of transaction data, leading to delays in fraud detection and prevention.
Moreover, rule-based systems were often unable to detect new types of fraud that did not fit into their predefined rules. This led to a high number of false negatives, where fraudulent transactions went undetected.
The need for a more effective solution led to the exploration of machine learning for fraud detection.
Traditional Fraud Detection vs. Machine Learning Approaches
Traditional fraud detection systems, while useful, often lacked the ability to adapt to new fraud patterns. They were rigid, relying on predefined rules that could not capture the complexity of evolving fraudulent activities.
Machine learning, on the other hand, offers a more dynamic approach. It uses algorithms that learn from historical transaction data, identifying patterns and anomalies that may indicate fraud. This ability to learn and adapt makes machine learning a powerful tool in detecting and predicting future frauds.
Moreover, machine learning can handle large volumes of data, making it ideal for the digital banking environment where millions of transactions occur daily.
Limitations of Conventional Systems in the Digital Age
In the digital age, the volume, velocity, and variety of transaction data have increased exponentially. Traditional fraud detection systems, designed for a less complex era, struggle to keep up.
These systems often generate a high number of false positives, flagging legitimate transactions as suspicious. This not only leads to unnecessary investigations but can also result in a poor customer experience.
Furthermore, conventional systems are reactive, often detecting fraud after it has occurred. In contrast, machine learning allows for proactive fraud detection, identifying potential fraud before it happens. This shift from a reactive to a proactive approach is crucial in minimising financial loss and protecting customer trust.
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Machine Learning: A Game Changer in Fraud Detection
Machine learning has emerged as a game changer in the field of fraud detection. Its ability to learn from data and adapt to new patterns makes it a powerful tool in the fight against financial fraud.
Machine learning algorithms can analyze vast amounts of transaction data in real-time. They can identify complex patterns and subtle correlations that may indicate fraudulent activity. This level of analysis is beyond the capabilities of traditional rule-based systems.
Moreover, machine learning can predict future frauds based on historical data. This predictive capability allows financial institutions to take proactive measures to prevent fraud, rather than reacting after the fact.
Machine learning also reduces the number of false positives. It can distinguish between legitimate transactions and suspicious activity with a high degree of accuracy. This not only saves resources but also improves the customer experience.
However, implementing machine learning in fraud detection is not without its challenges. It requires high-quality data, continuous model training, and a deep understanding of the underlying algorithms.
Understanding Machine Learning Algorithms in Banking
Machine learning algorithms can be broadly classified into supervised and unsupervised learning models. Supervised learning models are trained on labeled data, where the outcome of each transaction (fraudulent or legitimate) is known. These models learn to predict the outcome of new transactions based on this training.
Unsupervised learning models, on the other hand, do not require labeled data. They identify patterns and anomalies in the data, which can indicate potential fraud. These models are particularly useful in detecting new types of fraud that do not fit into known patterns.
Both supervised and unsupervised learning models have their strengths and weaknesses. The choice of model depends on the specific requirements of the financial institution and the nature of the data available.
Regardless of the type of model used, the effectiveness of machine learning in fraud detection depends largely on the quality of the data and the accuracy of the model training.
Real-Time Transaction Monitoring with Machine Learning
One of the key advantages of machine learning is its ability to process and analyse large volumes of data in real-time. This is particularly important in the context of digital banking, where transactions occur around the clock and across different channels.
Real-time transaction monitoring allows financial institutions to detect and prevent fraud as it happens. Machine learning algorithms can analyse each transaction as it occurs, flagging any suspicious activity for immediate investigation.
This real-time analysis is not limited to the transaction itself. Machine learning models can also analyze the context of the transaction, such as the customer's typical behavior, the time and location of the transaction, and other relevant factors.
This comprehensive analysis allows for more accurate fraud detection, reducing both false positives and false negatives. It also enables financial institutions to respond quickly to potential fraud, minimising financial loss and protecting customer trust.
Implementing Machine Learning Models for Fraud Detection
Implementing machine learning models for fraud detection requires a strategic approach. It's not just about choosing the right algorithms, but also about understanding the data and the business context.
The first step is to define the problem clearly. What type of fraud are you trying to detect? What are the characteristics of fraudulent transactions? What data is available for analysis? These questions will guide the choice of machine learning model and the design of the training process.
Next, the data needs to be prepared for analysis. This involves cleaning the data, handling missing values, and transforming variables as needed. The quality of the data is crucial for the performance of the machine learning model.
Once the data is ready, the machine learning model can be trained. This involves feeding the model with the training data and allowing it to learn from it. The model's performance should be evaluated and fine-tuned as necessary.
Finally, the model needs to be integrated into the existing fraud detection system. This requires careful planning and testing to ensure that the model works as expected and does not disrupt the existing processes.
Supervised vs. Unsupervised Learning in Fraud Detection
In the context of fraud detection, both supervised and unsupervised learning models have their uses. The choice between the two depends on the nature of the problem and the data available.
Supervised learning models are useful when there is a large amount of labeled data available. These models can learn from past examples of fraud and apply this knowledge to detect future frauds. However, they may not be as effective in detecting new types of fraud that do not fit into known patterns.
Unsupervised learning models, on the other hand, do not require labeled data. They can identify patterns and anomalies in the data, which can indicate potential fraud. These models are particularly useful in detecting new types of fraud that do not fit into known patterns.
Regardless of the type of model used, the effectiveness of machine learning in fraud detection depends largely on the quality of the data and the accuracy of the model training.
The Role of Data Quality and Model Training
Data quality plays a crucial role in the effectiveness of machine learning models for fraud detection. High-quality data allows the model to learn accurately and make reliable predictions.
Data quality involves several aspects, including accuracy, completeness, consistency, and timeliness. The data should accurately represent the transactions, be complete with no missing values, be consistent across different sources, and be up-to-date.
Model training is another critical factor in the success of machine learning for fraud detection. The model needs to be trained on a representative sample of the data, with a good balance between fraudulent and legitimate transactions.
The model's performance should be evaluated and fine-tuned as necessary. This involves adjusting the model's parameters, retraining the model, and validating its performance on a separate test set.
Continuous monitoring and updating of the model is also essential to ensure that it remains effective as new patterns of fraud emerge.
Challenges in Machine Learning-Based Fraud Detection
Despite the potential of machine learning in fraud detection, there are several challenges that financial institutions need to address. One of the main challenges is the complexity of financial transactions.
Financial transactions involve numerous variables and can follow complex patterns. This complexity can make it difficult for machine learning models to accurately identify fraudulent transactions.
Another challenge is the imbalance in the data. Fraudulent transactions are relatively rare compared to legitimate transactions. This imbalance can lead to models that are biased towards predicting transactions as legitimate, resulting in a high number of false negatives.
The dynamic nature of fraud is another challenge. Fraudsters continuously adapt their tactics to evade detection. This means that machine learning models need to be regularly updated to keep up with new patterns of fraud.
Finally, there are challenges related to data privacy and security. Financial transactions involve sensitive personal information. Financial institutions need to ensure that this data is handled securely and that privacy is maintained.
Distinguishing Legitimate Transactions from Fraudulent Activity
Distinguishing between legitimate transactions and fraudulent activity such as credit card fraud is a key challenge in fraud detection. This is particularly difficult because fraudulent transactions often mimic legitimate ones.
Machine learning models can help to address this challenge by identifying patterns and anomalies in the data. However, these models need to be trained on high-quality data and need to be regularly updated to keep up with changing patterns of fraud.
False positives are another concern. These occur when legitimate transactions are incorrectly flagged as fraudulent. This can lead to unnecessary investigations and can disrupt the customer experience. Strategies to minimise false positives include refining the model's parameters and incorporating feedback from fraud investigators.
Ethical and Privacy Considerations in Data Usage
The use of machine learning in fraud detection raises several ethical and privacy considerations. One of the main concerns is the use of personal transaction data.
Financial institutions need to ensure that they are complying with data protection regulations. This includes obtaining the necessary consents for data usage and ensuring that data is stored securely.
There is also a need for transparency in the use of machine learning. Customers should be informed about how their data is being used and how decisions are being made. This can help to build trust and can also provide customers with the opportunity to correct any inaccuracies in their data.
Finally, there are ethical considerations related to the potential for bias in machine learning models. Financial institutions need to ensure that their models are fair and do not discriminate against certain groups of customers. This requires careful design and testing of the models, as well as ongoing monitoring of their performance.
Financial Institutions Winning the Fight Against Fraud
Financial institutions are increasingly turning to machine learning to combat fraud. This is not just limited to large multinational banks. Smaller banks and credit unions are also adopting these technologies, often in partnership with fintech companies.
One example is the Royal Bank of Scotland, which uses machine learning to analyze customer behaviour and identify unusual patterns. This has helped the bank to detect and prevent fraud, improving customer trust and reducing financial loss.
Another example is Danske Bank, which uses machine learning to detect money laundering. The bank's machine learning model analyses transaction data and flags suspicious activity for further investigation. This has helped the bank to comply with anti-money laundering regulations and has also reduced the cost of investigations.
These examples show that machine learning is not just a tool for the future. It is already being used today, helping financial institutions to win the fight against fraud.
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The Future of Fraud Detection in Banking
The future of fraud detection in banking is promising, with machine learning playing a central role. As technology continues to evolve, so too will the methods used to detect and prevent fraud.
Machine learning models will become more sophisticated, capable of analysing larger volumes of data and identifying more complex patterns of fraudulent activity. This will enable financial institutions to detect fraud more quickly and accurately, reducing financial loss and improving customer trust.
At the same time, the integration of machine learning with other technologies, such as artificial intelligence and blockchain, will enhance fraud detection capabilities. These technologies will provide additional layers of security, making it even harder for fraudsters to succeed.
The future will also see greater collaboration between financial institutions, fintech companies, and law enforcement agencies. By sharing data and insights, these organizations can work together to combat financial fraud more effectively.
Emerging Trends and Technologies
Several emerging trends and technologies are set to shape the future of fraud detection in banking. One of these is deep learning, a subset of machine learning that uses neural networks to analyse data. Deep learning can identify complex patterns and correlations in data, making it a powerful tool for detecting fraud.
Another trend is the use of behavioural biometrics, which analyses the unique ways in which individuals interact with their devices. This can help to identify fraudulent activity, as fraudsters will interact with devices in different ways to legitimate users.
Finally, the use of consortium data and shared intelligence will become more common. By pooling data from multiple sources, financial institutions can build more accurate and robust machine learning models for fraud detection.
Preparing for the Next Wave of Financial Crimes
As technology evolves, so too do the methods used by fraudsters. Financial institutions must therefore be proactive in preparing for the next wave of financial crimes. This involves staying up-to-date with the latest trends and technologies in fraud detection, and continuously updating and refining machine learning models.
Financial crime investigators will also need to develop new skills and expertise. This includes understanding how machine learning works, and how it can be applied to detect and prevent fraud. Training and professional development will therefore be crucial.
Finally, financial institutions will need to adopt a multi-layered security approach. This involves using a range of technologies and methods to detect and prevent fraud, with machine learning being just one part of the solution. By doing so, they can ensure that they are well-prepared to combat the ever-evolving threat of financial fraud.
Conclusion: Embracing Machine Learning for a Safer Banking Environment
In conclusion, as financial institutions strive to stay ahead of increasingly sophisticated fraud tactics, adopting advanced solutions like Tookitaki's FinCense becomes imperative.
With its real-time fraud prevention capabilities, FinCense empowers banks and fintechs to screen customers and transactions with remarkable 90% accuracy, ensuring robust protection against fraudulent activities. Its comprehensive risk coverage, powered by cutting-edge AI and machine learning, addresses all potential risk scenarios, providing a holistic approach to fraud detection.
Moreover, FinCense's seamless integration with existing systems enhances operational efficiency, allowing compliance teams to concentrate on the most significant threats. By choosing Tookitaki's FinCense, financial institutions can safeguard their operations and foster a secure environment for their customers, paving the way for a future where fraud is effectively mitigated.
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Understanding the Source of Funds in Financial Transactions
In today's financial landscape, understanding the source of funds (SOF) is crucial for ensuring compliance and preventing financial crimes. Financial institutions must verify the origin of funds to comply with regulations and mitigate risks. This blog post delves into the meaning, importance, best practices, and challenges of verifying the source of funds.
Source of Funds in AML: What It Is and How Banks Verify It
Source of Funds Meaning
The term "source of funds" refers to the origin of the money used in a transaction. This can include earnings from employment, business revenue, investments, or other legitimate income sources.
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Source of Funds Example
For instance, if someone deposits a large sum of money into their bank account, the bank needs to verify whether this money came from a legitimate source, such as a property sale, inheritance, or salary.
Here are some common sources of funds:
- Salary: Imagine you've been saving up from your job to buy a new gaming console. When you finally get it, your salary is the Source of Funds for that purchase. In the grown-up world, this could mean someone buying a house with the money they've saved from their job.
- Inheritance: Now, let's say your grandma left you some money when she passed away (may she rest in peace), and you use it to start a college fund. The inheritance is your Source of Funds for that college account.
- Business Profits: If you have a lemonade stand and make some serious cash, and then you use that money to buy a new bike, the profits from your business are your Source of Funds for the bike.
- Selling Assets: Let's say your family decides to sell your old car to buy a new one. The money you get from selling the old car becomes the Source of Funds for the new car purchase.
- Investments and Dividends: Suppose you've invested in some stocks, and you make a nice profit. If you use that money to, say, go on vacation, then the money you made from your investments is the Source of Funds for your trip.
Difference Between Source of Funds and Source of Wealth
Source of Funds (SOF) refers to the origin of the specific money involved in a transaction, such as income from employment, sales, or loans. It is focused on the immediate funds used in a particular financial activity.
Source of Wealth (SOW), on the other hand, pertains to the overall origin of an individual’s total assets, including accumulated wealth over time from various sources like investments, inheritances, or business ownership. It provides a broader view of the person's financial background.
Importance of Source of Funds Verification
Regulatory Requirements and Compliance
Verifying the source of funds is essential for financial institutions to comply with regulations such as anti-money laundering (AML) laws. Regulatory bodies like the Financial Action Task Force (FATF) mandate stringent checks to ensure that funds do not originate from illegal activities.
Financial and Reputational Risks
Failure to verify the source of funds can result in significant financial penalties and damage to an institution's reputation. Banks and other financial entities must implement robust verification processes to avoid involvement in financial crimes and maintain public trust.
Best Practices for Source of Funds Verification
Risk-Based Approach
Implementing a risk-based approach means assessing the risk level of each transaction and customer. Higher-risk transactions require more rigorous verification, ensuring that resources are allocated efficiently and effectively.
Advanced Technology Utilization
Utilizing advanced technologies such as artificial intelligence and machine learning can enhance the efficiency and accuracy of source of funds verification. These technologies can analyze large datasets quickly, identifying potential red flags.
Regular Updates and Audits
Maintaining updated records and conducting regular audits are crucial for an effective source of funds verification. This ensures that the verification processes remain robust and compliant with the latest regulations.
Source of Funds Requirements Across APAC
FATF Recommendation 13 requires financial institutions to apply enhanced due diligence, including source of funds verification for high-risk customers and transactions. In practice, each APAC regulator has translated this into specific obligations.
Australia (AUSTRAC)
Under the AML/CTF Rules Part 7, AUSTRAC requires ongoing customer due diligence that includes verifying source of funds when a transaction or customer profile is inconsistent with prior behaviour or stated purpose. Enhanced customer due diligence — triggered by high-risk customer classification, PEP status, or unusual transaction patterns — requires documented source of funds evidence before the transaction proceeds or the relationship continues.
Acceptable documentation under AUSTRAC guidance includes: recent pay slips (last 3 months), business financial statements, tax returns, property sale contracts, or investment account statements. For inheritance-sourced funds, a grant of probate or solicitor letter is required.
Singapore (MAS)
MAS Notice 626 requires Singapore-licensed FIs to verify source of funds as part of enhanced due diligence for high-risk customers and any customer whose funds originate from high-risk jurisdictions. MAS examination findings have consistently cited inadequate SOF documentation as a gap — specifically, accepting verbal declarations without supporting evidence.
Malaysia (BNM)
BNM's AML/CFT Policy Document requires source of funds verification for EDD-triggered customers, high-value transactions above MYR 50,000 in cash-equivalent form, and corporate accounts where beneficial ownership is complex. BNM specifically requires that SOF evidence be independently verifiable — a customer's own declaration is not sufficient for high-risk accounts.
Philippines (BSP)
BSP Circular 706 and its amendments require source of funds verification for customers classified as high-risk under the institution's risk assessment, and for any transaction that appears inconsistent with the customer's known financial profile. AMLC's guidance notes that source of funds documentation must be retained for a minimum of 5 years.
Common Sources of Funds
Legitimate Sources
Legitimate sources of funds include earnings from employment, business income, investment returns, loans, and inheritances. These sources are generally verifiable through official documentation such as pay slips, tax returns, and bank statements.
Illegitimate Sources
Illegitimate sources of funds might include money from illegal activities such as drug trafficking, fraud, corruption, or money laundering. These sources often lack proper documentation and can pose significant risks to financial institutions if not properly identified and reported.
Challenges in Verifying Source of Funds
Complex Transactions
Complex transactions, involving multiple parties and jurisdictions, pose significant challenges in verifying the source of funds. Tracing the origin of such funds requires comprehensive analysis and robust systems to track and verify all related transactions.
Privacy and Data Protection Concerns
Verifying the source of funds often involves handling sensitive personal data. Financial institutions must balance the need for thorough verification with strict adherence to privacy and data protection regulations, ensuring that customer information is secure.
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What Good Source of Funds Verification Looks Like in Practice
The institutions that handle SOF verification most effectively treat it as a tiered process, not a one-size-all checklist.
For standard-risk customers, verification at onboarding is enough — pay slips, a bank statement, or a tax return. For high-risk customers, EDD-triggered accounts, or transactions that don't fit the pattern, that standard is higher: independently verifiable documentation, a paper trail that shows the funds' journey from origin to arrival, and a compliance officer's written sign-off.
The documentation requirement is not the hard part. The hard part is knowing when to apply it — and that is a transaction monitoring question as much as a KYC question. A source of funds issue that doesn't get flagged at monitoring never reaches the verification stage.
For more on building the monitoring programme that surfaces these cases, see our Transaction Monitoring Software Buyer's Guide and our complete guide to KYC and customer due diligence.
Talk to Tookitaki's team about how FinCense handles source of funds flags as part of an integrated AML and transaction monitoring programme.
Frequently Asked Questions
1. What is source of funds in AML?
Source of funds refers to where the money used in a specific transaction or business relationship comes from. In AML compliance, financial institutions review source of funds to understand whether the money is legitimate and whether it matches the customer’s profile and declared activity.
2. Why is source of funds important in AML compliance?
Source of funds is important because it helps financial institutions assess whether the money involved in a transaction is consistent with what they know about the customer. It supports due diligence, helps identify unusual activity, and reduces the risk of money laundering or other financial crime.
3. What is the difference between source of funds and source of wealth?
Source of funds refers to the origin of the money used in a particular transaction or account activity. Source of wealth refers to how a customer built their overall wealth over time. In simple terms, source of funds looks at where this money came from, while source of wealth looks at how the person became wealthy in general.
4. How do financial institutions verify source of funds?
Financial institutions may verify source of funds using documents such as bank statements, salary slips, business income records, property sale agreements, inheritance papers, dividend records, or other documents that explain where the money originated. The exact documents required depend on the customer, the transaction, and the level of risk involved.
5. When is source of funds verification required?
Source of funds verification is commonly required during customer onboarding, enhanced due diligence, high-risk transactions, or periodic reviews. It may also be requested when a transaction appears unusual or does not match the customer’s known financial behaviour.
6. Is source of funds verification required for every customer?
Not always. The depth of source of funds verification usually depends on the customer’s risk level, the nature of the transaction, and applicable AML regulations. Higher-risk customers and more complex transactions generally require closer scrutiny.
7. What source of funds documentation does AUSTRAC accept?
AUSTRAC's AML/CTF guidance accepts: recent pay slips (last 3 months), business financial statements or tax returns, property sale contracts with settlement documentation, investment account statements, and for inherited funds, a grant of probate or solicitor's letter. Verbal declarations are not sufficient for high-risk customers or transactions triggering enhanced due diligence.
8. Is source of funds verification required for every transaction?No. Source of funds verification is triggered by risk level, not transaction volume. Standard-risk retail customers verified at onboarding do not require SOF documentation for routine transactions. The trigger points are: EDD classification, PEP status, transactions inconsistent with the customer's stated financial profile, high-value cash transactions above reporting thresholds, and periodic review of high-risk accounts. See your regulator's specific guidance — AUSTRAC's Part 7, MAS Notice 626, or BNM's AML/CFT Policy Document — for the applicable triggers in your jurisdiction.
eKYC in Malaysia: Bank Negara Guidelines for Digital Banks and E-Wallets
In 2022, Bank Negara Malaysia awarded digital bank licences to five applicants: GXBank, Boost Bank, AEON Bank (backed by RHB), KAF Digital, and Zicht. None of these institutions have a branch network. None of them can sit a customer across a desk and photocopy a MyKad. For them, remote identity verification is not a product feature — it is the only way they can onboard a customer at all.
That is why BNM's eKYC framework matters. The question for compliance officers and product teams at these institutions — and at the e-money issuers, remittance operators, and licensed payment service providers that operate under the same rules is not whether to implement eKYC. It is whether the implementation will satisfy BNM when examiners review session logs during an AML/CFT examination.
This guide covers what BNM's eKYC framework requires, where institutions most commonly fall short, and what the rules mean in practice for tiered account access.
The Regulatory Scope of BNM's eKYC Framework
BNM's eKYC Policy Document was first issued in June 2020 and updated in February 2023. It applies to a wide range of supervised institutions:
- Licensed banks and Islamic banks
- Development financial institutions
- E-money issuers operating under the Financial Services Act 2013 — including large operators such as Touch 'n Go eWallet, GrabPay, and Boost
- Money service businesses
- Payment Services Operators (PSOs) licensed under the Payment Systems Act 2003
The policy document sets one overriding standard: eKYC must achieve the same level of identity assurance as face-to-face verification. That standard is not aspirational. It is the benchmark against which BNM examiners assess whether a remote onboarding programme is compliant.
For a deeper grounding in what KYC requires before getting into the eKYC-specific rules, the KYC compliance framework guide covers the foundational requirements.
The Four BNM-Accepted eKYC Methods
BNM's eKYC Policy Document specifies four accepted verification methods. Institutions must implement at least one; many implement two or more to accommodate different customer segments and device capabilities.
Method 1 — Biometric Facial Matching with Document Verification
The customer submits a selfie and an image of their MyKad or passport. The institution's system runs facial recognition to match the selfie against the document photo. Liveness detection is mandatory — passive or active — to prevent spoofing via static photographs, recorded video, or 3D masks.
This is the most widely deployed method among Malaysian digital banks and e-money issuers. It works on any smartphone with a front-facing camera and does not require the customer to be on a live call or to own a device with NFC capability.
Method 2 — Live Video Call Verification
A trained officer conducts a live video interaction with the customer and verifies the customer's face against their identity document in real time. The officer must be trained to BNM's specified standards, and the session must be recorded and retained.
This method provides strong identity assurance but introduces operational cost and throughput constraints. Some institutions use it as a fallback for customers whose biometric verification does not clear automated thresholds.
Method 3 — MyKad NFC Chip Reading
The customer uses their smartphone's NFC reader to read the chip embedded in their MyKad directly. The chip contains the holder's biometric data and personal information, and the read is cryptographically authenticated. BNM considers this the highest assurance eKYC method available under Malaysian national infrastructure.
The constraint is device compatibility: not all smartphones have NFC readers, and the feature must be enabled. Adoption among mass-market customers remains lower than biometric methods as a result.
Method 4 — Government Database Verification
The institution cross-checks customer-provided information against government databases — specifically, JPJ (Jabatan Pengangkutan Jalan, road transport) and JPN (Jabatan Pendaftaran Negara, national registration). If the data matches, the identity is considered verified.
BNM treats this as the lowest-assurance method. Critically, it does not involve any biometric confirmation that the person submitting the data is the same person as the registered identity. BNM restricts Method 4 to lower-risk product tiers, and institutions that apply it to accounts exceeding those tier limits will face examination findings.
Liveness Detection: What BNM Expects
BNM's requirement for liveness detection in biometric methods is explicit in the February 2023 update to the eKYC Policy Document. The requirement exists because static facial matching alone — matching a selfie against a document photo — can be defeated by holding a photograph in front of the camera.
BNM expects institutions to document the accuracy performance of their liveness detection system. The specific thresholds the policy document references are:
- False Acceptance Rate (FAR): below 0.1% — meaning the system incorrectly accepts a spoof attempt in fewer than 1 in 1,000 cases
- False Rejection Rate (FRR): below 10% — meaning genuine customers are incorrectly rejected in fewer than 10 in 100 cases
These are not defaults — they are floors. Institutions must document their actual FAR and FRR in their eKYC programme documentation and must periodically validate those figures, particularly after model updates or changes to the verification vendor.
Third-party eKYC vendors must be on BNM's approved list. An institution using a vendor not on that list — even a globally recognised biometric vendor — does not have a compliant eKYC programme regardless of the vendor's technical capabilities.
Account Tiers and Transaction Limits
BNM applies a risk-based framework that links account access limits to the assurance level of the eKYC method used to open the account. This is not optional configuration — these are regulatory caps.
Tier 1 — Method 4 (Database Verification Only)
- Maximum account balance: MYR 5,000
- Maximum daily transfer limit: MYR 1,000
Tier 2 — Methods 1, 2, or 3 (Biometric Verification)
- E-money accounts: maximum balance of MYR 50,000
- Licensed bank accounts: no regulatory cap on balance (subject to the institution's own risk limits)
If a customer whose account was opened via Method 4 wants to move into Tier 2, they must complete an additional verification step using a biometric method. That upgrade process must be documented and the records retained — the same as any primary onboarding session.
This tiering structure means product decisions about account limits are also compliance decisions. A digital bank that launches a savings product with a MYR 10,000 minimum deposit and relies on Method 4 for onboarding has a compliance problem, not just a product design problem.
Record-Keeping: What Must Be Retained and for How Long
BNM requires that all eKYC sessions be recorded and retained for a minimum of 6 years. The records must include:
- Raw images or video from the verification session
- Facial match confidence scores
- Liveness detection scores
- Verification timestamps
- The outcome of the verification (approved, rejected, referred for manual review)
During AML/CFT examinations, BNM examiners review eKYC session logs. An institution that can demonstrate a successful biometric match but cannot produce the underlying scores and timestamps for that session does not have compliant records. This is a documentation failure, not a technical one and it is one of the more common findings in Malaysian eKYC examinations.
eKYC Within the Broader AML/CFT Programme
A compliant eKYC onboarding process does not discharge an institution's AML/CFT obligations for the full customer lifecycle. BNM's AML/CFT Policy Document — separate from the eKYC Policy Document — requires institutions to apply risk-based customer due diligence (CDD) continuously.
Two areas where this creates friction in eKYC-based operations:
High-risk customers require Enhanced Due Diligence (EDD) that eKYC cannot complete. A customer who is a Politically Exposed Person (PEP), operates in a high-risk jurisdiction, or presents unusual transaction patterns requires EDD. Source of funds verification for these customers cannot be completed through biometric verification alone. Institutions must have documented rules specifying when an eKYC-onboarded customer triggers the EDD workflow — and those rules must be reviewed and enforced in practice, not just documented.
Dormant account reactivation is a re-verification trigger. BNM expects institutions to treat the reactivation of an account dormant for 12 months or more as an event requiring re-verification. This is a common gap: many institutions have onboarding eKYC workflows but no corresponding re-verification process for dormant accounts coming back to active status.
For institutions that have deployed transaction monitoring alongside their eKYC programme, integrating eKYC assurance levels into monitoring rule calibration is good practice — a Tier 1 account that begins transacting at Tier 2 volumes is exactly the kind of pattern that should generate an alert. The transaction monitoring software buyer's guide covers what to look for in a system capable of handling this kind of integrated logic.
Common Implementation Gaps
Based on BNM examination findings and the February 2023 policy document guidance, four gaps appear most frequently in Malaysian eKYC programmes:
1. Using Method 4 for accounts that exceed Tier 1 limits. This is the most consequential gap. If an account opened via database verification reaches a balance above MYR 5,000 or a daily transfer above MYR 1,000, the institution is operating outside the regulatory framework. The fix requires either enforcing hard caps at the product level or requiring biometric re-verification before account limits expand.
2. No liveness detection documentation. An institution that has deployed biometric eKYC but cannot demonstrate to BNM that it tested for spoofing — with documented FAR/FRR figures — does not have a defensible eKYC programme. The technology alone is not enough; the validation and documentation must exist.
3. Third-party eKYC vendor not on BNM's approved list. BNM maintains an approved vendor list for a reason. An institution that integrated a non-listed vendor, even one with strong global credentials, needs to remediate — either by migrating to an approved vendor or by engaging BNM directly on the approval process before continuing to use that vendor for compliant onboarding.
4. No re-verification trigger for dormant account reactivation. Institutions that built their eKYC programme around the onboarding workflow and never implemented re-verification logic for dormant accounts have a gap that BNM examiners will find. This requires both a policy update and a system-level trigger.
What Good eKYC Compliance Looks Like
A compliant eKYC programme in Malaysia has five elements that work together:
- At least one BNM-accepted verification method, implemented with a BNM-approved vendor and validated to the required FAR/FRR thresholds
- Hard account tier limits enforced at the product level, with a documented upgrade path that triggers biometric re-verification for Tier 1 accounts requesting higher access
- Complete session records — images, scores, timestamps, and outcomes — retained for the full 6-year period
- EDD triggers documented and enforced for high-risk customer categories, including PEPs and high-risk jurisdiction connections
- Re-verification workflows for dormant accounts reactivating after 12 months of inactivity
Meeting all five is not a one-time project. BNM expects periodic validation of vendor performance, regular review of threshold calibration, and documented sign-off from a named senior officer on the state of the eKYC programme.
For Malaysian institutions building or reviewing their eKYC programme, Tookitaki's AML compliance platform combines eKYC verification with transaction monitoring and ongoing risk assessment in a single integrated environment — designed for the requirements BNM examiners actually check. Book a demo to see how it works in a Malaysian digital bank or e-money context, or read our KYC framework overview for a broader view of where eKYC sits within the full compliance programme.
The App That Made Millions Overnight: Inside Taiwan’s Fake Investment Scam
The profits looked real. The numbers kept climbing. And that was exactly the trap.
The Scam That Looked Legit — Until It Wasn’t
She watched her investment grow to NT$250 million.
The numbers were right there on the screen.
So she did what most people would do, she invested more.
The victim, a retired teacher in Taipei, wasn’t chasing speculation. She was responding to what looked like proof.
According to a report by Taipei Times, this was part of a broader scam uncovered by authorities in Taiwan — one that used a fake investment app to simulate profits and systematically extract funds from victims.
The platform showed consistent gains.
At one point, balances appeared to reach NT$250 million.
It felt credible.
It felt earned.
So the investments continued — through bank transfers, and in some cases, through cash and even gold payments.
By the time the illusion broke, the numbers had disappeared.
Because they were never real.
Inside the Illusion: How the Fake Investment App Worked
What makes this case stand out is not just the deception, but the way it was engineered.
This was not a simple scam.
It was a controlled financial experience designed to build belief over time.
1. Entry Through Trust
Victims were introduced through intermediaries, referrals, or online channels. The opportunity appeared exclusive, structured, and credible.
2. A Convincing Interface
The app mirrored legitimate investment platforms — dashboards, performance charts, transaction histories. Everything a real investor would expect.
3. Fabricated Gains
After initial deposits, the app began showing steady returns. Not unrealistic at first — just enough to build confidence.
Then the numbers accelerated.
At its peak, some victims saw balances of NT$250 million.
4. The Reinforcement Loop
Each increase in displayed profit triggered the same response:
“This is working.”
And that belief led to more capital.
5. Expanding Payment Channels
To sustain the operation and reduce traceability, victims were asked to invest through:
- Bank transfers
- Cash payments
- Gold and other physical assets
This fragmented the financial trail and pushed parts of it outside the system.
6. Exit Denied
When withdrawals were attempted, friction appeared — delays, additional charges, or silence.
The platform remained convincing.
But it was never connected to real markets.
Why This Scam Is a Step Ahead
This is where the model shifts.
Fraud is no longer just about convincing someone to invest.
It is about showing them that they already made money.
That changes the psychology completely.
- Victims are not acting on promises
- They are reacting to perceived success
The app becomes the source of truth.This is not just deception. It is engineered belief, reinforced through design.
For financial institutions, this creates a deeper challenge.
Because the transaction itself may appear completely rational —
even prudent — when viewed in isolation.
Following the Money: A Fragmented Financial Trail
From an AML perspective, scams like this are designed to leave behind incomplete visibility.
Likely patterns include:
- Repeated deposits into accounts linked to the network
- Gradual increase in transaction size as confidence builds
- Use of multiple beneficiary accounts to distribute funds
- Rapid movement of funds across accounts
- Partial diversion into cash and gold, breaking traceability
- Behaviour inconsistent with customer financial profiles
What makes detection difficult is not just the layering.
It is the fact that part of the activity is deliberately moved outside the financial system.
Red Flags Financial Institutions Should Watch
Transaction-Level Indicators
- Incremental increase in investment amounts over short periods
- Transfers to newly introduced or previously unseen beneficiaries
- High-value transactions inconsistent with past behaviour
- Rapid outbound movement of funds after receipt
- Fragmented transfers across multiple accounts
Behavioural Indicators
- Customers referencing unusually high or guaranteed returns
- Strong conviction in an investment without verifiable backing
- Repeated fund transfers driven by urgency or perceived gains
- Resistance to questioning or intervention
Channel & Activity Indicators
- Use of unregulated or unfamiliar investment applications
- Transactions initiated based on external instructions
- Movement between digital transfers and physical asset payments
- Indicators of coordinated activity across unrelated accounts
The Real Challenge: When the Illusion Lives Outside the System
This is where traditional detection models begin to struggle.
Financial institutions can analyse:
- Transactions
- Account behaviour
- Historical patterns
But in this case, the most important factor, the fake app displaying fabricated gains — exists entirely outside their field of view.
By the time a transaction is processed:
- The customer is already convinced
- The action appears legitimate
- The risk signal is delayed
And detection becomes reactive.
Where Technology Must Evolve
To address scams like this, financial institutions need to move beyond static rules.
Detection must focus on:
- Behavioural context, not just transaction data
- Progressive signals, not one-off alerts
- Network-level intelligence, not isolated accounts
- Real-time monitoring, not post-event analysis
This is where platforms like Tookitaki’s FinCense make a difference.
By combining:
- Scenario-driven detection built from real-world scams
- AI-powered behavioural analytics
- Cross-entity monitoring to uncover hidden connections
- Real-time alerting and intervention
…institutions can begin to detect early-stage risk, not just final outcomes.
From Fabricated Gains to Real Losses
For the retired teacher in Taipei, the app told a simple story.
It showed growth.
It showed profit.
It showed certainty.
But none of it was real.
Because in scams like this, the system does not fail first.
Belief does.
And by the time the transaction looks suspicious,
it is already too late.
Understanding the Source of Funds in Financial Transactions
In today's financial landscape, understanding the source of funds (SOF) is crucial for ensuring compliance and preventing financial crimes. Financial institutions must verify the origin of funds to comply with regulations and mitigate risks. This blog post delves into the meaning, importance, best practices, and challenges of verifying the source of funds.
Source of Funds in AML: What It Is and How Banks Verify It
Source of Funds Meaning
The term "source of funds" refers to the origin of the money used in a transaction. This can include earnings from employment, business revenue, investments, or other legitimate income sources.
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Source of Funds Example
For instance, if someone deposits a large sum of money into their bank account, the bank needs to verify whether this money came from a legitimate source, such as a property sale, inheritance, or salary.
Here are some common sources of funds:
- Salary: Imagine you've been saving up from your job to buy a new gaming console. When you finally get it, your salary is the Source of Funds for that purchase. In the grown-up world, this could mean someone buying a house with the money they've saved from their job.
- Inheritance: Now, let's say your grandma left you some money when she passed away (may she rest in peace), and you use it to start a college fund. The inheritance is your Source of Funds for that college account.
- Business Profits: If you have a lemonade stand and make some serious cash, and then you use that money to buy a new bike, the profits from your business are your Source of Funds for the bike.
- Selling Assets: Let's say your family decides to sell your old car to buy a new one. The money you get from selling the old car becomes the Source of Funds for the new car purchase.
- Investments and Dividends: Suppose you've invested in some stocks, and you make a nice profit. If you use that money to, say, go on vacation, then the money you made from your investments is the Source of Funds for your trip.
Difference Between Source of Funds and Source of Wealth
Source of Funds (SOF) refers to the origin of the specific money involved in a transaction, such as income from employment, sales, or loans. It is focused on the immediate funds used in a particular financial activity.
Source of Wealth (SOW), on the other hand, pertains to the overall origin of an individual’s total assets, including accumulated wealth over time from various sources like investments, inheritances, or business ownership. It provides a broader view of the person's financial background.
Importance of Source of Funds Verification
Regulatory Requirements and Compliance
Verifying the source of funds is essential for financial institutions to comply with regulations such as anti-money laundering (AML) laws. Regulatory bodies like the Financial Action Task Force (FATF) mandate stringent checks to ensure that funds do not originate from illegal activities.
Financial and Reputational Risks
Failure to verify the source of funds can result in significant financial penalties and damage to an institution's reputation. Banks and other financial entities must implement robust verification processes to avoid involvement in financial crimes and maintain public trust.
Best Practices for Source of Funds Verification
Risk-Based Approach
Implementing a risk-based approach means assessing the risk level of each transaction and customer. Higher-risk transactions require more rigorous verification, ensuring that resources are allocated efficiently and effectively.
Advanced Technology Utilization
Utilizing advanced technologies such as artificial intelligence and machine learning can enhance the efficiency and accuracy of source of funds verification. These technologies can analyze large datasets quickly, identifying potential red flags.
Regular Updates and Audits
Maintaining updated records and conducting regular audits are crucial for an effective source of funds verification. This ensures that the verification processes remain robust and compliant with the latest regulations.
Source of Funds Requirements Across APAC
FATF Recommendation 13 requires financial institutions to apply enhanced due diligence, including source of funds verification for high-risk customers and transactions. In practice, each APAC regulator has translated this into specific obligations.
Australia (AUSTRAC)
Under the AML/CTF Rules Part 7, AUSTRAC requires ongoing customer due diligence that includes verifying source of funds when a transaction or customer profile is inconsistent with prior behaviour or stated purpose. Enhanced customer due diligence — triggered by high-risk customer classification, PEP status, or unusual transaction patterns — requires documented source of funds evidence before the transaction proceeds or the relationship continues.
Acceptable documentation under AUSTRAC guidance includes: recent pay slips (last 3 months), business financial statements, tax returns, property sale contracts, or investment account statements. For inheritance-sourced funds, a grant of probate or solicitor letter is required.
Singapore (MAS)
MAS Notice 626 requires Singapore-licensed FIs to verify source of funds as part of enhanced due diligence for high-risk customers and any customer whose funds originate from high-risk jurisdictions. MAS examination findings have consistently cited inadequate SOF documentation as a gap — specifically, accepting verbal declarations without supporting evidence.
Malaysia (BNM)
BNM's AML/CFT Policy Document requires source of funds verification for EDD-triggered customers, high-value transactions above MYR 50,000 in cash-equivalent form, and corporate accounts where beneficial ownership is complex. BNM specifically requires that SOF evidence be independently verifiable — a customer's own declaration is not sufficient for high-risk accounts.
Philippines (BSP)
BSP Circular 706 and its amendments require source of funds verification for customers classified as high-risk under the institution's risk assessment, and for any transaction that appears inconsistent with the customer's known financial profile. AMLC's guidance notes that source of funds documentation must be retained for a minimum of 5 years.
Common Sources of Funds
Legitimate Sources
Legitimate sources of funds include earnings from employment, business income, investment returns, loans, and inheritances. These sources are generally verifiable through official documentation such as pay slips, tax returns, and bank statements.
Illegitimate Sources
Illegitimate sources of funds might include money from illegal activities such as drug trafficking, fraud, corruption, or money laundering. These sources often lack proper documentation and can pose significant risks to financial institutions if not properly identified and reported.
Challenges in Verifying Source of Funds
Complex Transactions
Complex transactions, involving multiple parties and jurisdictions, pose significant challenges in verifying the source of funds. Tracing the origin of such funds requires comprehensive analysis and robust systems to track and verify all related transactions.
Privacy and Data Protection Concerns
Verifying the source of funds often involves handling sensitive personal data. Financial institutions must balance the need for thorough verification with strict adherence to privacy and data protection regulations, ensuring that customer information is secure.
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What Good Source of Funds Verification Looks Like in Practice
The institutions that handle SOF verification most effectively treat it as a tiered process, not a one-size-all checklist.
For standard-risk customers, verification at onboarding is enough — pay slips, a bank statement, or a tax return. For high-risk customers, EDD-triggered accounts, or transactions that don't fit the pattern, that standard is higher: independently verifiable documentation, a paper trail that shows the funds' journey from origin to arrival, and a compliance officer's written sign-off.
The documentation requirement is not the hard part. The hard part is knowing when to apply it — and that is a transaction monitoring question as much as a KYC question. A source of funds issue that doesn't get flagged at monitoring never reaches the verification stage.
For more on building the monitoring programme that surfaces these cases, see our Transaction Monitoring Software Buyer's Guide and our complete guide to KYC and customer due diligence.
Talk to Tookitaki's team about how FinCense handles source of funds flags as part of an integrated AML and transaction monitoring programme.
Frequently Asked Questions
1. What is source of funds in AML?
Source of funds refers to where the money used in a specific transaction or business relationship comes from. In AML compliance, financial institutions review source of funds to understand whether the money is legitimate and whether it matches the customer’s profile and declared activity.
2. Why is source of funds important in AML compliance?
Source of funds is important because it helps financial institutions assess whether the money involved in a transaction is consistent with what they know about the customer. It supports due diligence, helps identify unusual activity, and reduces the risk of money laundering or other financial crime.
3. What is the difference between source of funds and source of wealth?
Source of funds refers to the origin of the money used in a particular transaction or account activity. Source of wealth refers to how a customer built their overall wealth over time. In simple terms, source of funds looks at where this money came from, while source of wealth looks at how the person became wealthy in general.
4. How do financial institutions verify source of funds?
Financial institutions may verify source of funds using documents such as bank statements, salary slips, business income records, property sale agreements, inheritance papers, dividend records, or other documents that explain where the money originated. The exact documents required depend on the customer, the transaction, and the level of risk involved.
5. When is source of funds verification required?
Source of funds verification is commonly required during customer onboarding, enhanced due diligence, high-risk transactions, or periodic reviews. It may also be requested when a transaction appears unusual or does not match the customer’s known financial behaviour.
6. Is source of funds verification required for every customer?
Not always. The depth of source of funds verification usually depends on the customer’s risk level, the nature of the transaction, and applicable AML regulations. Higher-risk customers and more complex transactions generally require closer scrutiny.
7. What source of funds documentation does AUSTRAC accept?
AUSTRAC's AML/CTF guidance accepts: recent pay slips (last 3 months), business financial statements or tax returns, property sale contracts with settlement documentation, investment account statements, and for inherited funds, a grant of probate or solicitor's letter. Verbal declarations are not sufficient for high-risk customers or transactions triggering enhanced due diligence.
8. Is source of funds verification required for every transaction?No. Source of funds verification is triggered by risk level, not transaction volume. Standard-risk retail customers verified at onboarding do not require SOF documentation for routine transactions. The trigger points are: EDD classification, PEP status, transactions inconsistent with the customer's stated financial profile, high-value cash transactions above reporting thresholds, and periodic review of high-risk accounts. See your regulator's specific guidance — AUSTRAC's Part 7, MAS Notice 626, or BNM's AML/CFT Policy Document — for the applicable triggers in your jurisdiction.
eKYC in Malaysia: Bank Negara Guidelines for Digital Banks and E-Wallets
In 2022, Bank Negara Malaysia awarded digital bank licences to five applicants: GXBank, Boost Bank, AEON Bank (backed by RHB), KAF Digital, and Zicht. None of these institutions have a branch network. None of them can sit a customer across a desk and photocopy a MyKad. For them, remote identity verification is not a product feature — it is the only way they can onboard a customer at all.
That is why BNM's eKYC framework matters. The question for compliance officers and product teams at these institutions — and at the e-money issuers, remittance operators, and licensed payment service providers that operate under the same rules is not whether to implement eKYC. It is whether the implementation will satisfy BNM when examiners review session logs during an AML/CFT examination.
This guide covers what BNM's eKYC framework requires, where institutions most commonly fall short, and what the rules mean in practice for tiered account access.
The Regulatory Scope of BNM's eKYC Framework
BNM's eKYC Policy Document was first issued in June 2020 and updated in February 2023. It applies to a wide range of supervised institutions:
- Licensed banks and Islamic banks
- Development financial institutions
- E-money issuers operating under the Financial Services Act 2013 — including large operators such as Touch 'n Go eWallet, GrabPay, and Boost
- Money service businesses
- Payment Services Operators (PSOs) licensed under the Payment Systems Act 2003
The policy document sets one overriding standard: eKYC must achieve the same level of identity assurance as face-to-face verification. That standard is not aspirational. It is the benchmark against which BNM examiners assess whether a remote onboarding programme is compliant.
For a deeper grounding in what KYC requires before getting into the eKYC-specific rules, the KYC compliance framework guide covers the foundational requirements.
The Four BNM-Accepted eKYC Methods
BNM's eKYC Policy Document specifies four accepted verification methods. Institutions must implement at least one; many implement two or more to accommodate different customer segments and device capabilities.
Method 1 — Biometric Facial Matching with Document Verification
The customer submits a selfie and an image of their MyKad or passport. The institution's system runs facial recognition to match the selfie against the document photo. Liveness detection is mandatory — passive or active — to prevent spoofing via static photographs, recorded video, or 3D masks.
This is the most widely deployed method among Malaysian digital banks and e-money issuers. It works on any smartphone with a front-facing camera and does not require the customer to be on a live call or to own a device with NFC capability.
Method 2 — Live Video Call Verification
A trained officer conducts a live video interaction with the customer and verifies the customer's face against their identity document in real time. The officer must be trained to BNM's specified standards, and the session must be recorded and retained.
This method provides strong identity assurance but introduces operational cost and throughput constraints. Some institutions use it as a fallback for customers whose biometric verification does not clear automated thresholds.
Method 3 — MyKad NFC Chip Reading
The customer uses their smartphone's NFC reader to read the chip embedded in their MyKad directly. The chip contains the holder's biometric data and personal information, and the read is cryptographically authenticated. BNM considers this the highest assurance eKYC method available under Malaysian national infrastructure.
The constraint is device compatibility: not all smartphones have NFC readers, and the feature must be enabled. Adoption among mass-market customers remains lower than biometric methods as a result.
Method 4 — Government Database Verification
The institution cross-checks customer-provided information against government databases — specifically, JPJ (Jabatan Pengangkutan Jalan, road transport) and JPN (Jabatan Pendaftaran Negara, national registration). If the data matches, the identity is considered verified.
BNM treats this as the lowest-assurance method. Critically, it does not involve any biometric confirmation that the person submitting the data is the same person as the registered identity. BNM restricts Method 4 to lower-risk product tiers, and institutions that apply it to accounts exceeding those tier limits will face examination findings.
Liveness Detection: What BNM Expects
BNM's requirement for liveness detection in biometric methods is explicit in the February 2023 update to the eKYC Policy Document. The requirement exists because static facial matching alone — matching a selfie against a document photo — can be defeated by holding a photograph in front of the camera.
BNM expects institutions to document the accuracy performance of their liveness detection system. The specific thresholds the policy document references are:
- False Acceptance Rate (FAR): below 0.1% — meaning the system incorrectly accepts a spoof attempt in fewer than 1 in 1,000 cases
- False Rejection Rate (FRR): below 10% — meaning genuine customers are incorrectly rejected in fewer than 10 in 100 cases
These are not defaults — they are floors. Institutions must document their actual FAR and FRR in their eKYC programme documentation and must periodically validate those figures, particularly after model updates or changes to the verification vendor.
Third-party eKYC vendors must be on BNM's approved list. An institution using a vendor not on that list — even a globally recognised biometric vendor — does not have a compliant eKYC programme regardless of the vendor's technical capabilities.
Account Tiers and Transaction Limits
BNM applies a risk-based framework that links account access limits to the assurance level of the eKYC method used to open the account. This is not optional configuration — these are regulatory caps.
Tier 1 — Method 4 (Database Verification Only)
- Maximum account balance: MYR 5,000
- Maximum daily transfer limit: MYR 1,000
Tier 2 — Methods 1, 2, or 3 (Biometric Verification)
- E-money accounts: maximum balance of MYR 50,000
- Licensed bank accounts: no regulatory cap on balance (subject to the institution's own risk limits)
If a customer whose account was opened via Method 4 wants to move into Tier 2, they must complete an additional verification step using a biometric method. That upgrade process must be documented and the records retained — the same as any primary onboarding session.
This tiering structure means product decisions about account limits are also compliance decisions. A digital bank that launches a savings product with a MYR 10,000 minimum deposit and relies on Method 4 for onboarding has a compliance problem, not just a product design problem.
Record-Keeping: What Must Be Retained and for How Long
BNM requires that all eKYC sessions be recorded and retained for a minimum of 6 years. The records must include:
- Raw images or video from the verification session
- Facial match confidence scores
- Liveness detection scores
- Verification timestamps
- The outcome of the verification (approved, rejected, referred for manual review)
During AML/CFT examinations, BNM examiners review eKYC session logs. An institution that can demonstrate a successful biometric match but cannot produce the underlying scores and timestamps for that session does not have compliant records. This is a documentation failure, not a technical one and it is one of the more common findings in Malaysian eKYC examinations.
eKYC Within the Broader AML/CFT Programme
A compliant eKYC onboarding process does not discharge an institution's AML/CFT obligations for the full customer lifecycle. BNM's AML/CFT Policy Document — separate from the eKYC Policy Document — requires institutions to apply risk-based customer due diligence (CDD) continuously.
Two areas where this creates friction in eKYC-based operations:
High-risk customers require Enhanced Due Diligence (EDD) that eKYC cannot complete. A customer who is a Politically Exposed Person (PEP), operates in a high-risk jurisdiction, or presents unusual transaction patterns requires EDD. Source of funds verification for these customers cannot be completed through biometric verification alone. Institutions must have documented rules specifying when an eKYC-onboarded customer triggers the EDD workflow — and those rules must be reviewed and enforced in practice, not just documented.
Dormant account reactivation is a re-verification trigger. BNM expects institutions to treat the reactivation of an account dormant for 12 months or more as an event requiring re-verification. This is a common gap: many institutions have onboarding eKYC workflows but no corresponding re-verification process for dormant accounts coming back to active status.
For institutions that have deployed transaction monitoring alongside their eKYC programme, integrating eKYC assurance levels into monitoring rule calibration is good practice — a Tier 1 account that begins transacting at Tier 2 volumes is exactly the kind of pattern that should generate an alert. The transaction monitoring software buyer's guide covers what to look for in a system capable of handling this kind of integrated logic.
Common Implementation Gaps
Based on BNM examination findings and the February 2023 policy document guidance, four gaps appear most frequently in Malaysian eKYC programmes:
1. Using Method 4 for accounts that exceed Tier 1 limits. This is the most consequential gap. If an account opened via database verification reaches a balance above MYR 5,000 or a daily transfer above MYR 1,000, the institution is operating outside the regulatory framework. The fix requires either enforcing hard caps at the product level or requiring biometric re-verification before account limits expand.
2. No liveness detection documentation. An institution that has deployed biometric eKYC but cannot demonstrate to BNM that it tested for spoofing — with documented FAR/FRR figures — does not have a defensible eKYC programme. The technology alone is not enough; the validation and documentation must exist.
3. Third-party eKYC vendor not on BNM's approved list. BNM maintains an approved vendor list for a reason. An institution that integrated a non-listed vendor, even one with strong global credentials, needs to remediate — either by migrating to an approved vendor or by engaging BNM directly on the approval process before continuing to use that vendor for compliant onboarding.
4. No re-verification trigger for dormant account reactivation. Institutions that built their eKYC programme around the onboarding workflow and never implemented re-verification logic for dormant accounts have a gap that BNM examiners will find. This requires both a policy update and a system-level trigger.
What Good eKYC Compliance Looks Like
A compliant eKYC programme in Malaysia has five elements that work together:
- At least one BNM-accepted verification method, implemented with a BNM-approved vendor and validated to the required FAR/FRR thresholds
- Hard account tier limits enforced at the product level, with a documented upgrade path that triggers biometric re-verification for Tier 1 accounts requesting higher access
- Complete session records — images, scores, timestamps, and outcomes — retained for the full 6-year period
- EDD triggers documented and enforced for high-risk customer categories, including PEPs and high-risk jurisdiction connections
- Re-verification workflows for dormant accounts reactivating after 12 months of inactivity
Meeting all five is not a one-time project. BNM expects periodic validation of vendor performance, regular review of threshold calibration, and documented sign-off from a named senior officer on the state of the eKYC programme.
For Malaysian institutions building or reviewing their eKYC programme, Tookitaki's AML compliance platform combines eKYC verification with transaction monitoring and ongoing risk assessment in a single integrated environment — designed for the requirements BNM examiners actually check. Book a demo to see how it works in a Malaysian digital bank or e-money context, or read our KYC framework overview for a broader view of where eKYC sits within the full compliance programme.
The App That Made Millions Overnight: Inside Taiwan’s Fake Investment Scam
The profits looked real. The numbers kept climbing. And that was exactly the trap.
The Scam That Looked Legit — Until It Wasn’t
She watched her investment grow to NT$250 million.
The numbers were right there on the screen.
So she did what most people would do, she invested more.
The victim, a retired teacher in Taipei, wasn’t chasing speculation. She was responding to what looked like proof.
According to a report by Taipei Times, this was part of a broader scam uncovered by authorities in Taiwan — one that used a fake investment app to simulate profits and systematically extract funds from victims.
The platform showed consistent gains.
At one point, balances appeared to reach NT$250 million.
It felt credible.
It felt earned.
So the investments continued — through bank transfers, and in some cases, through cash and even gold payments.
By the time the illusion broke, the numbers had disappeared.
Because they were never real.
Inside the Illusion: How the Fake Investment App Worked
What makes this case stand out is not just the deception, but the way it was engineered.
This was not a simple scam.
It was a controlled financial experience designed to build belief over time.
1. Entry Through Trust
Victims were introduced through intermediaries, referrals, or online channels. The opportunity appeared exclusive, structured, and credible.
2. A Convincing Interface
The app mirrored legitimate investment platforms — dashboards, performance charts, transaction histories. Everything a real investor would expect.
3. Fabricated Gains
After initial deposits, the app began showing steady returns. Not unrealistic at first — just enough to build confidence.
Then the numbers accelerated.
At its peak, some victims saw balances of NT$250 million.
4. The Reinforcement Loop
Each increase in displayed profit triggered the same response:
“This is working.”
And that belief led to more capital.
5. Expanding Payment Channels
To sustain the operation and reduce traceability, victims were asked to invest through:
- Bank transfers
- Cash payments
- Gold and other physical assets
This fragmented the financial trail and pushed parts of it outside the system.
6. Exit Denied
When withdrawals were attempted, friction appeared — delays, additional charges, or silence.
The platform remained convincing.
But it was never connected to real markets.
Why This Scam Is a Step Ahead
This is where the model shifts.
Fraud is no longer just about convincing someone to invest.
It is about showing them that they already made money.
That changes the psychology completely.
- Victims are not acting on promises
- They are reacting to perceived success
The app becomes the source of truth.This is not just deception. It is engineered belief, reinforced through design.
For financial institutions, this creates a deeper challenge.
Because the transaction itself may appear completely rational —
even prudent — when viewed in isolation.
Following the Money: A Fragmented Financial Trail
From an AML perspective, scams like this are designed to leave behind incomplete visibility.
Likely patterns include:
- Repeated deposits into accounts linked to the network
- Gradual increase in transaction size as confidence builds
- Use of multiple beneficiary accounts to distribute funds
- Rapid movement of funds across accounts
- Partial diversion into cash and gold, breaking traceability
- Behaviour inconsistent with customer financial profiles
What makes detection difficult is not just the layering.
It is the fact that part of the activity is deliberately moved outside the financial system.
Red Flags Financial Institutions Should Watch
Transaction-Level Indicators
- Incremental increase in investment amounts over short periods
- Transfers to newly introduced or previously unseen beneficiaries
- High-value transactions inconsistent with past behaviour
- Rapid outbound movement of funds after receipt
- Fragmented transfers across multiple accounts
Behavioural Indicators
- Customers referencing unusually high or guaranteed returns
- Strong conviction in an investment without verifiable backing
- Repeated fund transfers driven by urgency or perceived gains
- Resistance to questioning or intervention
Channel & Activity Indicators
- Use of unregulated or unfamiliar investment applications
- Transactions initiated based on external instructions
- Movement between digital transfers and physical asset payments
- Indicators of coordinated activity across unrelated accounts
The Real Challenge: When the Illusion Lives Outside the System
This is where traditional detection models begin to struggle.
Financial institutions can analyse:
- Transactions
- Account behaviour
- Historical patterns
But in this case, the most important factor, the fake app displaying fabricated gains — exists entirely outside their field of view.
By the time a transaction is processed:
- The customer is already convinced
- The action appears legitimate
- The risk signal is delayed
And detection becomes reactive.
Where Technology Must Evolve
To address scams like this, financial institutions need to move beyond static rules.
Detection must focus on:
- Behavioural context, not just transaction data
- Progressive signals, not one-off alerts
- Network-level intelligence, not isolated accounts
- Real-time monitoring, not post-event analysis
This is where platforms like Tookitaki’s FinCense make a difference.
By combining:
- Scenario-driven detection built from real-world scams
- AI-powered behavioural analytics
- Cross-entity monitoring to uncover hidden connections
- Real-time alerting and intervention
…institutions can begin to detect early-stage risk, not just final outcomes.
From Fabricated Gains to Real Losses
For the retired teacher in Taipei, the app told a simple story.
It showed growth.
It showed profit.
It showed certainty.
But none of it was real.
Because in scams like this, the system does not fail first.
Belief does.
And by the time the transaction looks suspicious,
it is already too late.