In July 2025, Singapore’s central bank, the Monetary Authority of Singapore (MAS), penalised nine financial institutions a total of S$27.45 million following a landmark money-laundering case in which authorities seized more than S$3 billion in illicit assets.
What MAS identified as the root causes were the shortcomings in the financial institutions’ customer risk assessments, their tracing of the sources of customers’ wealth as well as their ability to monitor and follow up on suspicious transactions.
Financial crime rarely presents itself in an overtly suspicious manner. In many cases, it appears through ordinary customers, standard documentation and transactions that, when assessed individually, do not raise concerns. In this regard, the issue does not lie in a single data point, but in the pattern that emerges when those elements are connected, as well as in what may go unnoticed between one review and the next.
Compliance programmes based on manual approvals and periodic reviews are not designed to identify this type of risk effectively. The programmes that prove more effective are those integrated into the operational process itself, from onboarding to ongoing monitoring, while also generating documentation capable of supporting the institution’s position in the event of regulatory scrutiny.
In this context, an AML compliance API such as Irbis API constitutes a practical solution. It allows the first layer of compliance decision-making to be automated, reduces false positives and preserves a clear audit trail without creating unnecessary friction for legitimate customers by integrating identity validation, data enrichment and screening directly into existing systems.
A Quick Introduction to Money Laundering and AML Compliance
Money laundering is the process through which illegally obtained funds are made to appear legitimate by concealing their true origin. It is a mechanism commonly used by criminal organisations, corruption networks and fraud schemes to introduce illicit proceeds into the financial system without raising immediate suspicion.
Although laundering methods may vary, most operations generally follow three stages:
Placement: the introduction of illicit funds into the financial system.
Layering: the movement of funds through multiple transactions in order to obscure their origin.
Integration: the reintroduction of those funds as apparently legitimate income.
In response to this threat, governments and regulatory authorities have developed anti-money laundering frameworks requiring financial institutions and other regulated entities to implement a series of control measures. These usually include sanctions screening, transaction monitoring, suspicious activity reporting and ongoing customer due diligence.
From a compliance perspective, customer onboarding processes must include checks aimed at identifying known risk factors, including sanctions exposure and politically exposed persons (PEPs). Failure to implement such controls may result in significant penalties, reputational damage and increased exposure to financial crime.
As digital services continue to expand and cross-border transactions become more frequent, the volume of information involved in compliance processes has increased considerably. In this context, many organisations are turning to AML APIs to automate part of the compliance function and strengthen the efficiency of their control frameworks.
The Role of OSINT in AML Investigations
Open-source intelligence (OSINT) has become an increasingly relevant component of modern AML compliance. Rather than relying exclusively on internal information or limited official lists, investigators are now making greater use of publicly available sources to identify potential risk indicators that may otherwise remain unnoticed.
These sources may include public records, corporate registries, media reporting, sanctions and watchlists, as well as social and digital identity signals. Taken together, they can provide a broader picture of an individual or organisation than traditional compliance checks alone.
For example, a company may appear legitimate in official filings while maintaining links to questionable business networks or being associated with adverse media coverage. In such cases, the risk does not arise from a single source in isolation, but from the broader context that becomes visible when the available information is assessed as a whole.
Integrating OSINT into compliance workflows allows organisations to enrich their internal data and develop a more complete understanding of potential exposure. Tools designed for intelligence gathering and data enrichment, such as Irbis API, can support this process by collecting structured information from multiple sources in a more efficient manner.
As a result, analysts are no longer required to manually review different databases one by one. Instead, a single API call can return relevant identity and risk-related indicators within seconds.
The Regulatory Landscape and Obligations
AML compliance requirements vary from one jurisdiction to another, although the underlying principles are broadly consistent. Financial institutions and other regulated entities are expected to establish procedures capable of detecting and preventing money laundering. In practice, these controls are not limited to laundering alone. They are also intended to disrupt terrorist financing by identifying high-risk customers, suspicious networks and sanctions exposure at an early stage.
Several international bodies have played a central role in shaping these standards, most notably the Financial Action Task Force (FATF), whose recommendations have had a significant influence on national regulatory frameworks. Among the authorities most commonly referenced in practice is the Office of Foreign Assets Control (OFAC), which maintains sanctions lists covering individuals, organisations and jurisdictions subject to economic restrictions. Any business operating internationally must ensure that its customers are not included in those watchlists.
From an operational perspective, regulatory obligations usually require organisations to implement customer identification procedures, apply risk-based screening against sanctions and PEP lists, carry out ongoing monitoring of higher-risk customers and maintain proper documentation of compliance actions for audit purposes.
These requirements create a considerable operational burden. Compliance teams are expected to verify identities, assess risk exposure and preserve a clear record of the steps taken during onboarding and ongoing monitoring. Where these processes are handled manually, they tend to become slower, more resource-intensive and more exposed to error.
APIs in Modern Compliance
Application Programming Interfaces (APIs) have significantly changed the way compliance functions operate within modern digital platforms. Rather than building separate systems or relying entirely on manual checks, organisations can connect external services directly to their internal workflows. This allows compliance controls to be performed automatically during key stages such as customer onboarding.
In the AML context, APIs are commonly used to retrieve identity verification data, perform sanctions screening, cross-check customers against watchlists, enrich profiles with additional sources of information and generate audit trails in a structured manner.
At a practical level, an AML compliance API serves as a link between the organisation’s internal systems and external data sources. When a customer submits their information during onboarding, the platform can automatically call the API to verify identity details and assess relevant risk indicators.
This approach reduces the need for manual review and allows organisations to scale their compliance operations more efficiently, without having to increase staff at the same rate.
IRBIS: How It Works
IRBIS API supports AML compliance by taking the core onboarding attributes already collected and turning them into a structured risk profile. In practice, the process starts when a customer provides basic identity data such as an email address and phone number, and when the organisation captures session attributes such as the originating IP. Those attributes are then submitted to IRBIS lookups, for example combined email, phone, and IP geolocation, and the workflow returns enriched results with source coverage and confidence style indicators such as percentage and sources.
Email enrichment establishes whether the address has a meaningful footprint and whether it resolves to associated identifiers in the response. Phone enrichment adds context that is often missing from onboarding forms, including line type and location signals, plus reputation and verification style fields in the response model. IP geolocation adds location and network context, including country, city, ISP and connection attributes, and a VPN indicator. When those three attributes are assessed together, compliance teams can identify mismatches and weak signals early, for example a customer claiming one geography while onboarding from a different region, use of VPN infrastructure, or an identity footprint that is unusually thin or inconsistent across attributes.
Where correlation is needed, IRBIS also supports validation between attributes. The phone versus IP validator is designed to verify the connection between a phone number and an IP address or to validate an email domain, supporting geolocation checks and domain validation as part of identity and fraud detection. If additional exposure checks are required, BreachScan can be used to verify whether an email or phone appears in data leak or breach datasets, adding another risk signal to the case narrative.
This approach reduces manual reviews because the same checks are executed consistently and returned in a single structured output. For instance, an API call can confirm whether an email has linked platform identifiers in seconds, whereas a manual review would require an analyst to search multiple sites and tools and can easily take an hour.
Where screening is required, the workflow can be extended to include watchlist and PEP screening so the result can be stored in the case file and referenced later for audit purposes. The operational benefit is a more repeatable process where triage is faster, unnecessary escalations are reduced, and legitimate customers move through onboarding with less friction.
Core Components: KYC and Customer Due Diligence
At the core of AML compliance is the principle of Know Your Customer (KYC). In practical terms, this means organisations are expected to establish who their customers are and assess, from the outset, whether they present any relevant risk. This usually involves collecting identification data, verifying the information provided, screening the customer against sanctions lists and watchlists, and building an initial risk profile.
Customer Due Diligence (CDD) builds on that first stage. It is not limited to confirming identity, but also considers the customer’s activity and whether the relationship requires a greater level of scrutiny. Where a customer is identified as higher risk, Enhanced Due Diligence (EDD) may be necessary, involving deeper review and closer ongoing monitoring.
In this context, APIs such as IRBIS can support the process by providing additional identity signals and intelligence data, allowing organisations to verify customer information with greater consistency, broader context and less reliance on purely manual checks.
Using IRBIS to Validate Identity
Identity verification is one of the key elements of AML compliance. When a new customer registers with a financial institution or digital platform, the organisation must determine whether the information provided actually corresponds to a real individual or entity. Although this may appear straightforward, it is often at this stage that the first inconsistencies or risk indicators begin to emerge.
When IRBIS API is integrated into the onboarding workflow, identity data can be validated automatically and enriched with additional information. In practice, the system may assess whether the phone number is consistent with the identity provided, cross-check email and identity details across available sources, and retrieve related records that help confirm whether the profile appears genuine.
These signals give compliance teams a clearer basis for assessment, allowing them to determine whether the identity appears consistent or whether the case should be subject to further review.
Steps to AML Compliance Using the IRBIS API
Implementing an AML compliance workflow through IRBIS API usually follows a clear sequence:
The process starts during onboarding, when a new customer provides the identification data required to create an account. That information is then sent through the API so it can be checked and supplemented with additional data obtained from external sources.
Once the response is received, the compliance system can assess the enriched information and determine whether any relevant risk indicators are present. Where the available signals suggest potential money laundering concerns or other financial crime risks, the case can be flagged for manual review.
The main advantage of this approach is that it allows organisations to handle a high volume of customer onboarding processes in a more consistent and efficient manner, while maintaining the level of control required for regulatory compliance.
PEP and Sanctions Lists
Screening customers against sanctions lists and PEP databases is a core element of AML compliance. Sanctions lists, including those maintained by authorities such as OFAC, identify individuals and organisations subject to financial restrictions. PEPs, on the other hand, are individuals who hold prominent public positions and may, by virtue of their role, present a higher exposure to corruption or other forms of financial crime.
For financial institutions, this is not merely a formal requirement. It is a control measure intended to ensure that the organisation is not establishing or maintaining relationships with sanctioned entities or with customers whose profile may require a greater level of scrutiny.
When supported by API-based compliance tools, this screening can be carried out automatically during onboarding and repeated throughout the customer relationship, allowing potential risk exposure to be assessed on an ongoing basis.
KYC: API Solutions for AML
KYC processes have traditionally involved a significant amount of manual work. In practice, analysts were required to verify identity documents, consult multiple databases and cross-check information across different sources before reaching a conclusion. With the development of modern APIs, many of these steps can now be automated as part of a more integrated compliance workflow.
API-driven KYC solutions allow organisations to verify customer identities automatically, screen against sanctions and watchlists, enrich customer profiles with additional intelligence data and maintain structured compliance records throughout the process. The result is a compliance function that is not only faster and more accurate, but also less resource-intensive from an operational perspective.
Using IRBIS to Determine Trust Levels
Risk assessment sits at the centre of AML compliance, as it determines the level of scrutiny a customer should receive and how often that customer should be reviewed. In practice, compliance teams need to assign clear trust levels so that onboarding decisions remain consistent, proportionate and defensible. A low-risk customer should be able to proceed with limited friction, whereas a high-risk customer should give rise to enhanced due diligence, deeper verification and closer monitoring.
In this context, IRBIS API provides practical support by combining basic identity verification with enriched intelligence data. This allows the assessment to move beyond the fact that a customer has completed a form and toward a more meaningful question: whether the identity signals attached to that customer actually align. Where the same identity footprint appears consistently across multiple sources, the profile may support a higher level of trust and a lower-risk assessment. By contrast, where the available signals are limited, inconsistent or incomplete, the profile may justify a lower level of trust and a more cautious risk decision, particularly where the customer falls within categories that usually require tighter controls, such as high-risk jurisdictions, unusual business activity or inconsistent contact information.
When integrated into AML workflows, IRBIS helps collect these signals automatically and incorporate them into internal risk evaluation processes. The benefit is not limited to screening alone. It also improves operational prioritisation by allowing compliance teams to focus manual review on the cases where trust levels are genuinely uncertain, reduce false positives and apply enhanced due diligence where the level of risk actually warrants it, while allowing legitimate customers to move through the process with less disruption.
Automating the Investigative Trail for Compliance Audits
Regulatory compliance does not end once onboarding has been completed. Organisations must also be able to demonstrate that the appropriate procedures were followed throughout the assessment and decision-making process.
In practice, regulators often expect institutions to retain a clear record of compliance actions, including identity verification results, sanctions screening outcomes, risk assessment decisions and internal investigation notes.
API-driven compliance systems help address this requirement by automatically recording each verification request and response generated during the process. This creates a clear audit trail showing the steps taken, the checks performed and the basis on which the organisation reached its compliance decision.
In the event of a regulatory review or formal investigation, these records provide evidence that the institution acted in accordance with the applicable AML framework and followed the procedures required in the case.
To wrap up
AML risk management is changing faster than most compliance teams can keep up with manually, mainly because digital activity has exploded, and cross-border and regulatory requirements have become more complex. When customer onboarding, payments, and counterparties span multiple jurisdictions and data sources, manual workflows become a bottleneck and a liability. They are slower to adapt, harder to evidence, and more prone to inconsistency at the exact moment regulators expect stronger controls and clearer audit trails.
That is why automated, intelligence-led solutions are no longer optional. Integrating tools such as the IRBIS API allows organisations to verify identity at scale, enrich profiles with relevant intelligence, and standardise screening and monitoring in a way that is repeatable and defensible. Automation reduces noise and operational drag, while improving the ability to surface risk signals earlier before they become incidents. The most effective approach is to keep human oversight in place, with automation handling data retrieval, verification, and initial screening, while compliance professionals focus on judgement, context, and escalation decisions. Used this way, intelligent automation strengthens the control environment, supports regulatory compliance, and protects operational capacity as financial crime risks continue to evolve.