AML Transaction Monitoring and Suspicious Account Detection

ML risk scoring of transactions and accounts.

2in Indonesia

Named Models

Anti Money Laundering AI (AML AI)Google, HSBCReleasednot in the notesRuns onnot in the notesClasstabularGoogle Cloud AML transaction monitoring model co-developed with HSBC; HSBC reports 2 to 4x suspicious activity found and 60% fewer alerts.Commercial use: proprietary terms; Jakarta hosting: unknown; Indonesian support: not stated

Where this comes from

Models the research names for this use case. Research, with sources on each model record.

Who Runs It

production, 2planned, 1

Bank Negara Indonesia (BNI)Country: IndonesiaCloudera ML: product recommendation, procurement optimisation, suspicious account detection (anti-gambling with PPATK, OJK, BI), RM response assist, churnVendor: ClouderaproductionDate7 Aug 2025ML use cases in useEvidence: company statementConfidence: lowSource: aap.com.au

HSBCCountry: UK and Hong KongGoogle Cloud AML AIVendor: Google CloudproductionDate30 Nov 2023HSBC-reported: 2 to 4x suspicious activity found, 60% fewer alerts, detection 8 days after first alert (12 months post go-live vs prior 12 months); Celent Model Risk Manager of the Year 2023Evidence: company statementConfidence: lowSource: cloud.google.com, services.google.com

Kementerian Komunikasi dan Digital (Komdigi)Country: IndonesiaGambit Hunter AI to trace online gambling payment infrastructureVendor: Gambit HunterplannedDate13 Apr 2026none stated; the minister hopes the meeting leads to more concrete cooperationEvidence: government statementConfidence: mediumSource: katadata.co.id

Where this comes from

Documented uses by named organisations. The evidence column says who made the claim.

Candidate Models

TabArena: All Tasks, no imputation, snapshot 2026-05-06, 59 entries

A general benchmark for this model class, not a test on this use case. Elo across tabular tasks, anchored RandomForest default = 1000; TabPFN-lab researchers are involved. Higher is better. Elo has no zero, so the axis starts at 1,200. Showing 10 of 13.

AutoGluon 1.5Amazon maintainer-run1,700

TabPFN-2.6Prior Labs maintainer-run1,626

RealTabPFN-2.5Prior Labs maintainer-run1,604

TabICL v2INRIA maintainer-run1,598

RealMLPINRIA maintainer-run1,514

TabDPTLayer 6 AI maintainer-run1,460

TabMYandex maintainer-run1,448

LightGBMMicrosoft AI maintainer-run1,435

CatBoostYandex maintainer-run1,418

XGBoostDMLC community maintainer-run1,376

1,2001,5001,800

XGBoostDMLC community, tabularReleasedAug 2026open weightshas scoreslast 18 months

LightGBMMicrosoft AI, tabularReleasedJul 2026open weightshas scoreslast 18 months

TabPFN-2.6Prior Labs, tabularReleasedMar 2026open weightshas scoreslast 18 months

CatBoostYandex, tabularReleasedFeb 2026open weightshas scoreslast 18 months

AutoGluon 1.5Amazon, tabularReleased2026open weightshas scoreslast 18 months

TabICL v2INRIA, tabularReleased2026open weightshas scoreslast 18 months

RealTabPFN-2.5Prior Labs, tabularReleasedNov 2025open weightshas scoreslast 18 months

SAP-RPT-OSS (sap-rpt-1-oss)SAP, tabularReleasedJun 2025open weightshas scoreslast 18 months

TabDPTLayer 6 AI, tabularReleased2024open weightshas scores

TabPFN-3.5Prior Labs, tabularReleasedSep 2026last 18 months

Showing 10 of 31. All models of this class

Where this comes from

Derived, not researched: models in the directory whose class matches a class this use case uses, excluding family records and models shut down or deprecated. Ranked by how many fit facts they meet, then by release date. Each fit fact is read from the model record.

Use Case

Industry: finance and insurance
Model classes: tabular
Benchmarks or datasets: not in the notes
Regulatory notes: Surat Edaran Menkominfo 9/2023 Etika Kecerdasan Artifisial, Kementerian Komunikasi dan Informatika (now Kementerian Komunikasi dan Digital)AML transaction monitoring and suspicious account detection is an AI-based service run by an electronic system operator, and the circular asks such operators to build and use AI on the values of inclusivity, transparency, humanity and security.
UU 27/2022 Pelindungan Data Pribadi, DPR and President of the Republic of Indonesia (administered by Kementerian Komunikasi dan Digital)AML transaction monitoring and suspicious account detection processes personal and financial data of customers, which the law treats as a specific category of personal data, so the controller needs a lawful basis, must protect the data and must honour data subject rights.
Tata Kelola Kecerdasan Artifisial Perbankan Indonesia (2025 guideline), Otoritas Jasa Keuangan (OJK)AML transaction monitoring and suspicious account detection is an AI use in banking, which OJK's AI governance guideline covers across the AI life cycle, including bias, explainability and cyber risk.
POJK 8/2023 Penerapan Program APU, PPT dan PPPSPM di Sektor Jasa Keuangan, Otoritas Jasa Keuangan (OJK)AML transaction monitoring and suspicious account detection supports customer due diligence or transaction monitoring, which financial service providers must run under their anti-money laundering and counter-terrorist financing programme.
UU 8/2010 Pencegahan dan Pemberantasan Tindak Pidana Pencucian Uang, DPR and President of the Republic of Indonesia (PPATK as financial intelligence unit)AML transaction monitoring and suspicious account detection helps identify and report suspicious transactions, a duty this law places on reporting parties.

Where this comes from

From the industry tracks T08 to T10 of the research notes.

R&D Topics

Links to R&D topics are awaiting review.

Where this comes from

Which R&D topics would most change this use case. Drafted by Claude from the topic definitions and the use case; proposed until approved.

Opportunities

Opportunities are awaiting review.

Where this comes from

Gaps found by rule in what the directory holds for this use case. Each is a proposal for review.

Links

IndustryFinance29 use cases, 46 deployments

Use CaseAML Transaction Monitoring and Suspicious Account DetectionFinance

Models

Anti Money Laundering AI (AML AI)Google, tabular

Who Runs It

Kementerian Komunikasi dan Digital (Komdigi)Planned, 13 Apr 2026

Bank Negara Indonesia (BNI)In production, 7 Aug 2025

HSBCIn production, 30 Nov 2023

Graph

Models 1

Anti Money Laundering AI (AML AI)Google, HSBC, tabular

Deployments 3

HSBCGoogle Cloud AML AI, UK and Hong Kong Bank Negara Indonesia (BNI)Cloudera ML: product recommendation, procurement optimisation, suspicious account detection (anti-gambling with PPATK, OJK, BI), RM response assist, churn, Indonesia Kementerian Komunikasi dan Digital (Komdigi)Gambit Hunter AI to trace online gambling payment infrastructure, Indonesia

Sources

How HSBC fights money launderers with artificial intelligence | Google Cloud Blog cloud.google.com https://cloud.google.com/blog/topics/financial-services/how-hsbc-fights-money-launderers-with-artificial-intelligence
Bank Negara Indonesia Strengthens Partnership with Cloudera to Scale AI-Powered Business Transformation | AAP aap.com.au https://www.aap.com.au/aapreleases/globenewswire9507748/

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "AML Transaction Monitoring and Suspicious Account Detection", https://aurion.semai.id/usecases/fin-aml-monitoring/, records of October 2026.