Export Assurance — Risk-Targeted Machine Learning

GoSource analysed export assurance data and built a machine learning model to test more targeted and evidence-based compliance interventions.

Challenge

Australian agricultural exporters operate in a market worth approximately AUD $80 billion per annum. Farmers are subjected to a high number of assurance activities throughout the export lifecycle — including document reviews, inspections, audits, compliance checks, enforcement actions, and sampling — across all commodity types. Many farmers reported undertaking compliance activities two out of every three days. Only 20% of audits resulted in corrective actions, indicating a potential deficiency in targeting the highest-risk areas and entities. There was no comprehensive individual exporter profile or transparent understanding of an exporter’s compliance track record within the Department.

Solution

GoSource was engaged as a strategic partner on the Export Assurance project, part of the “Taking Farmers to Market” program. The work was delivered in two phases:

Phase 1 — Discovery and Pain Point Analysis: GoSource collected and analysed data across the entire agricultural export lifecycle, covering 12 stages from market understanding through to audits. This data was mapped against eight Australian export commodity categories (plant, meat, dairy, egg, fish, live animal exports — livestock and non-livestock, and genetic material). Hundreds of data points on export volume, export cost, regulatory volume, regulatory cost, pain, and process volume were analysed, considering both severity and frequency.

Phase 2 — Machine Learning Feasibility: Following the discovery findings, the Department initiated a project to determine whether a machine learning model could accurately predict compliance outcomes for regulated entities. GoSource built a machine learning model using risk profile data to predict likely non-compliance (who, what, and where to target). Historical audit and inspection schedules, as well as synthetic data, were used to test the model’s ability to predict non-compliance outcomes from previous assurance activities. The model was showcased to business users to evaluate both feasibility and potential business value for risk-based audit targeting.

Outcomes

• Comprehensive pain point analysis completed across the entire AUD $80 billion Australian agricultural export market
• Identified that only 20% of audits resulted in corrective actions, highlighting inefficient targeting
• Identified the gap in comprehensive exporter risk profiling within the Department
• Machine learning model demonstrated the ability to better predict non-compliance and identify new targets
• Model results could be overlaid with outcomes from planned assurance activities for evidence-based targeting
• Department equipped to make a well-informed decision to advance to the subsequent alpha stage
• Proposed alpha projects could then be tested with departmental subject matter experts

Technologies & Methods

• Machine learning (predictive compliance modelling)
• Data analytics and statistical analysis (severity and frequency scoring)
• Synthetic data generation for model testing
• Risk profiling across supply chain dimensions (commodities, establishments, entities, activities, markets)
• Export lifecycle and service mapping
• User research and stakeholder showcases
• Discovery and feasibility methodology

Team Size

Not specified