The Anti-Money Laundering Council (AMLC) is now using artificial intelligence as part of its fight against money laundering, giving the Philippines’ financial intelligence unit another tool for analyzing potentially suspicious financial activity.
Bangko Sentral ng Pilipinas Governor and AMLC Chairman Eli Remolona Jr. said ₱125 million of the council’s additional 2026 funding was allocated to artificial intelligence and capacity-building, including the expertise needed to use the technology.
The allocation came from a ₱162.9-million increase in AMLC’s funding for 2026. During budget deliberations, Remolona said the additional resources had helped strengthen the agency’s capabilities, while AMLC officials confirmed that AI had been integrated into its processes.
But what does it actually mean for a financial intelligence unit to use AI?
For AMLC, the technology could help investigators work through large volumes of financial information and identify patterns that deserve closer examination. However, the council has not publicly detailed the specific AI models it is using or exactly how its systems decide which transactions warrant further scrutiny.
AI can help find patterns humans could miss
Banks and other covered institutions already submit Suspicious Transaction Reports (STRs) to AMLC when transactions meet circumstances that raise suspicion of possible money laundering or related unlawful activity.
Under AMLC’s Guidelines on Transaction Reporting and Compliance Submissions, covered persons are required to determine whether suspicious circumstances exist in transactions or activities and file the corresponding STR when warranted.
AMLC’s reporting framework collects structured information around these reports, including details about the transaction, people involved and the circumstances that prompted the report.
That creates a potentially large pool of financial information to analyze.
This is where AI and machine learning can become useful. Instead of relying exclusively on analysts to examine reports individually, technology can help identify anomalies, connections and recurring patterns across datasets.
The Financial Action Task Force (FATF), the global standard-setting body for anti-money laundering controls, has noted that some financial intelligence units and banks are already deploying machine-learning models on transaction data to detect anomalies associated with fraud and other forms of financial crime. Some have also developed risk-scoring systems for payments.
For example, an analytical system could potentially help surface unusual transaction frequencies, rapid movements of funds between accounts, transaction behavior that differs substantially from an established profile, or relationships among accounts that would be difficult to see when transactions are examined separately.
These are examples of what this class of technology can do, however, not confirmed details of AMLC’s specific AI system.
A suspicious transaction is not proof of a crime
AI also does not turn every unusual transaction into a money laundering case.
A transaction being flagged as suspicious means there are circumstances that warrant closer examination. It does not by itself establish that money laundering or another crime has occurred.
That distinction becomes especially important when automated systems are involved.
A Filipino suddenly making transactions much larger than their usual activity, for example, could attract additional scrutiny without necessarily having done anything illegal. There may be a legitimate explanation for the change.
AI is therefore better understood as a filter and analytical tool, rather than a machine that determines guilt.
Its value is in helping analysts decide where limited investigative resources should be focused.
Why AMLC needs technology in the first place
AMLC sits at the center of the country’s financial intelligence system.
Its online reporting infrastructure already allows covered institutions to electronically transmit both Covered Transaction Reports and Suspicious Transaction Reports, as well as attachments associated with STRs.
As digital payments expand and money can move between financial institutions increasingly quickly, investigators also have to analyze more complicated trails of financial activity.
Technology can help make that process faster, but it does not eliminate the need for human investigators.
That appears to be part of the reason AMLC’s AI investment includes capacity-building alongside the technology itself. Remolona specifically pointed to the need for experts capable of using AI as he explained how the additional budget was being spent.
The development also comes after the Philippines exited the FATF’s list of jurisdictions under increased monitoring in February 2025. FATF cited, among other improvements, an increase in the use of financial intelligence and increases in money laundering investigations and prosecutions in line with the country’s risks.
AI could give AMLC another way to build on those capabilities.
The more consequential question, however, will not be whether AMLC has artificial intelligence.
It will be whether the technology can help investigators find meaningful financial connections faster while ensuring that an algorithmic flag remains the beginning of an investigation, rather than its conclusion.
