Geovani CapitalIntelligence, Investigation, Compliance and Training
GC-AR-103 · Advanced course

Artificial Intelligence and Machine Learning in Asset Tracing and Recovery

An AI asset tracing course on using machine learning, entity resolution and document analysis to find hidden assets faster, with sound governance.

5 days Classroom, online or in-house QR-verified certificate Next intake 19 Oct 2026

Course overview

Asset tracing teams now face thousands of bank statements, mobile money records, scanned title deeds and company filings in a single case. Reading them line by line is slow, and the links that matter are easily missed. This AI asset tracing course shows how machine learning and related tools can sort, match and prioritise that material, and where human judgement must stay in charge.

The course explains, without heavy mathematics, how entity resolution, anomaly detection, network analytics and document processing work, and what each can and cannot do in a recovery case. Participants run guided exercises with no-code and low-code tools on a synthetic dataset: matching names across registries, extracting data from scanned documents, flagging unusual transaction patterns and generating leads on hidden holders of assets. Governance runs through every day, covering bias, explainability, data protection and how to present AI-assisted findings in court.

Participants leave able to judge where AI genuinely speeds up tracing in their own unit, to specify a realistic pilot, and to challenge vendors' claims.

What you will be able to do

  • Explain the main AI and machine learning techniques used in asset tracing
  • Apply entity resolution to match people, companies and assets across datasets
  • Use anomaly detection to prioritise transactions for analyst review
  • Extract structured data from scanned documents with document AI tools
  • Analyse networks to surface likely proxies and hidden asset holders
  • Assess bias, error and explainability risks in AI-assisted findings
  • Design a governed pilot for AI in an asset tracing unit
  • Present AI-assisted results in a form that can withstand legal challenge

Who should attend

For experienced analysts, investigators and managers in asset recovery and financial intelligence units who want to use AI responsibly. No coding background is needed, but familiarity with financial analysis is assumed.

  • Senior financial intelligence analysts
  • Asset recovery investigators
  • Heads of analysis and investigation units
  • Data analysts in law enforcement and FIUs
  • Digital forensics specialists
  • ICT managers supporting investigations
  • Legal advisers on technology and evidence

Course outline

Day 1AI Foundations for Asset Tracing

Topics

  • What AI and machine learning are and are not
  • Supervised and unsupervised learning
  • Use cases across the recovery lifecycle
  • Data readiness in African agencies
  • Build, buy or partner decisions
  • Human-in-the-loop principles
Practical exerciseMapping a unit's tracing workflow and marking the steps AI could support.
Day 2Entity Resolution and Data Matching

Topics

  • Name variants and transliteration
  • Fuzzy matching techniques
  • Matching across registries and bank data
  • Confidence scores and thresholds
  • Reviewing false matches
  • Building a resolved entity list
Practical exerciseResolving subjects and associates across land, company and mobile money records.
Day 3Anomaly Detection and Transaction Analytics

Topics

  • Baseline behaviour and outliers
  • Clustering transaction patterns
  • Structuring and rapid movement indicators
  • Mobile money and cash patterns
  • Prioritising alerts for review
  • Measuring tool performance
Practical exerciseRanking a year of account activity to find periods worth manual review.
Day 4Document AI and Network Analytics

Topics

  • Optical character recognition of title deeds and statements
  • Extracting entities from documents
  • Large language models for summarising case material
  • Risks of fabricated output
  • Network centrality and community detection
  • Identifying likely proxies
Practical exerciseExtracting parties and properties from scanned documents and adding them to a network chart.
Day 5Governance, Evidence and Implementation

Topics

  • Bias, fairness and error
  • Explainability for courts
  • Data protection and retention
  • Audit trails for AI-assisted work
  • Procurement and vendor evaluation
  • Designing and measuring a pilot
CapstonePreparing a governed AI pilot proposal for an asset recovery unit and defending it before a review panel.

Course outcome

Participants leave with a practical understanding of AI tools for tracing and a governed pilot plan they can propose in their own institution.

Fees are per participant in US dollars and cover tuition, course materials and your certificate. Travel and accommodation are not included.

19Oct

Pretoria, South Africa

GC-AR-103 Guaranteed to run
19–23 Oct 2026 · 5 days
USD 1,500per participant · 5 days
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19Oct

Pretoria, South Africa

GC-AR-103 Guaranteed to run
19–30 Oct 2026 · 10 days
USD 3,000per participant · 10 days
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26Oct

Nairobi, Kenya

GC-AR-103 Guaranteed to run
26–30 Oct 2026 · 5 days
USD 1,500per participant · 5 days
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02Nov

Nairobi, Kenya

GC-AR-103 Guaranteed to run
2–6 Nov 2026 · 5 days
USD 1,500per participant · 5 days
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02Nov

Mombasa, Kenya

GC-AR-103 Guaranteed to run
2–13 Nov 2026 · 10 days
USD 3,000per participant · 10 days
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09Nov

Mombasa, Kenya

GC-AR-103 Guaranteed to run
9–13 Nov 2026 · 5 days
USD 1,500per participant · 5 days
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16Nov

Kampala, Uganda

GC-AR-103 Guaranteed to run
16–20 Nov 2026 · 5 days
USD 1,500per participant · 5 days
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16Nov

Kampala, Uganda

GC-AR-103 Guaranteed to run
16–27 Nov 2026 · 10 days
USD 3,000per participant · 10 days
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