Diploma in Network Analysis and Suspicious Behaviour
Online postgraduate diploma of 800 hours in Network Analysis and Suspicious Behaviour: 8 modules covering applied graph theory, entity resolution, community detection, anomalous behaviour indicators, graph machine learning, intelligence analysis and the AML and data protection framework, with an assigned technical tutor.
Diploma in Network Analysis and Suspicious Behaviour — studying from Singapore
Singapore's off-road and special vehicle sector is smaller than in larger industrial economies but serves port operations, construction, airport ground support and marine logistics. Equipment such as cranes, forklifts and specialised port machinery is maintained and developed locally. The Ministry of Manpower and the Land Transport Authority set safety and operating standards. Employers include port operators, construction groups and heavy equipment dealers.
Fees for Singapore: S$1,840 (Singapore dollar), with instalments and employer sponsorship available.
Relevant bodies in Singapore
Typical roles
Questions from Singapore
Will this qualification be recognised by professional bodies in Singapore?
SEIUM's qualifications are private, not official state degrees, and are not accredited by any national body in Singapore. Engineers may seek Professional Engineer registration through the Professional Engineers Board, and machinery work may also involve Workplace Safety and Health requirements set by the Ministry of Manpower. Recognition of a private qualification is decided by employers or the relevant body.
How might this training be used by employers in Singapore?
Singapore's construction, port, logistics, marine and industrial sectors use heavy machinery and special vehicles, so these skills may be relevant to maintenance, design and equipment engineering roles. The programme does not guarantee employment. Employers usually weigh practical experience, safety knowledge and project work alongside any qualification.
Can I study this alongside a full-time job, and can my employer help fund it?
Study is fully online and in English, with an assigned technical tutor, so you can fit it around work from home in Singapore. Workload ranges from 300 to 1,900 hours depending on the programme. Fees can be paid in instalments, and employer sponsorship is possible; many Singapore employers fund job-relevant training, so ask your management.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Apply graph theory to the analysis of relationships between entities.
Build graphs from transactional and registry sources.
Detect communities and anomalous structures in networks.
Identify fraud and money laundering typologies through behavioural indicators.
Train machine learning models on graphs.
Produce investigation reports with evidential value.
Meet the legal and ethical requirements for analysing personal data.
Who it's for
- Fraud analysts.
- Anti-money laundering analysts.
- Graph data scientists.
- Claims investigators.
- Financial intelligence analysts.
- Compliance managers.
- Engineering or science graduates looking to enter the insurance sector.
What you'll work with
- Spanish Law 10/2010 (prevention of money laundering)
- Directive (EU) 2015/849 (EU anti-money laundering directive)
- Regulation (EU) 2024/1624 (EU single AML rulebook)
- Regulation (EU) 2016/679, GDPR (data protection)
- FATF Recommendations (international AML/CFT standards)
- ISO/IEC 27001 (information security management)
- Neo4j
- Gephi
- NetworkX
- i2 Analyst's Notebook
- PyTorch Geometric
8 modules
1.1Nodes, edges and adjacency matrices1.2Directed, weighted and bipartite graphs1.3Multilayer and temporal graphs1.4Degree, betweenness and closeness centrality1.5PageRank and eigenvector centrality1.6Shortest paths and reachability1.7Density, reciprocity and transitivity1.8Reference random graph models1.9Network visualisation and layout1.10Workshop: building a transaction graph
2.1Sources: payments, claims, company registers2.2Normalising names and addresses2.3Fuzzy matching and Jaro-Winkler distance2.4Probabilistic entity resolution2.5Modelling in graph databases2.6The Cypher query language2.7Data quality and traceability2.8Pseudonymisation and data minimisation2.9Incremental graph updates2.10Practical exercise: unifying identities in Neo4j
3.1Modularity and the Louvain method3.2The Leiden algorithm3.3Label propagation3.4Connected components and k-cores3.5Detecting cliques and rings3.6Overlapping communities3.7Evaluating partition quality3.8Interpreting suspicious clusters3.9Communities in temporal graphs3.10Case study: a ring of staged claims
4.1FATF money laundering typologies4.2Splitting and structuring of transactions4.3Money mule accounts and round-tripping4.4Shell companies and beneficial ownership4.5Patterns of coordinated claims4.6Red flags in suppliers and repair shops4.7Velocity and frequency analysis4.8Deviations from the historical profile4.9Risk-based alert prioritisation4.10Lab: defining alert rules
5.1Features derived from network structure5.2Embeddings with node2vec5.3Graph neural networks: GCN and GraphSAGE5.4Unsupervised anomaly detection5.5Classification with imbalanced classes5.6Temporal validation and data leakage5.7Precision and recall metrics5.8Explainability of risk scores5.9Monitoring performance in production5.10Practical exercise: scoring model with NetworkX and PyTorch Geometric
6.1The intelligence cycle and requirements6.2Link charts and timelines6.3Financial flow analysis6.4Open sources and public registers6.5Competing hypotheses and cognitive biases6.6Chain of custody for digital evidence6.7Writing investigation reports6.8Suspicious transaction reporting6.9Coordination with financial intelligence units6.10Case study: report with i2 Analyst's Notebook
7.1Spanish Law 10/2010 on the prevention of money laundering7.2Directive (EU) 2015/849 and its amendments7.3Regulation (EU) 2024/1624 and the new AMLA authority7.4GDPR and processing of third-party data7.5Automated decisions and Article 22 GDPR7.6Proportionality and risk of discrimination7.7Model governance and audit7.8Record retention and duty of confidentiality7.9Liability of the obliged entity7.10Workshop: data protection impact assessment
8.1Framing the investigation case8.2Designing the graph data model8.3Loading and cleaning sources8.4Exploration and network metrics8.5Community and anomaly detection8.6Risk scoring model8.7Alert dashboard8.8Conclusions and recommendations8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
Training is 100% online on SEIUM's campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Neo4j, Gephi, NetworkX, i2 Analyst's Notebook and PyTorch Geometric, in free, academic or evaluation versions. Exercises are based on real procedures for detecting claims fraud rings, analysing payment flows in money laundering investigations and prioritising alerts in a compliance team. Assessment is continuous by module, plus the final capstone project.
Standards covered: Spanish Law 10/2010 on the prevention of money laundering, Directive (EU) 2015/849, Regulation (EU) 2024/1624, Regulation (EU) 2016/679 (GDPR), FATF Recommendations, ISO/IEC 27001.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer able to run the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Neo4j, Gephi, NetworkX, i2 Analyst's Notebook and PyTorch Geometric, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the programme software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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