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AI in Modern Safety Regulators.pdf

  1. AI in Modern Safety Regulators Jessie Nghiem, PhD Energy Safe Victoria
  2. Who are we Energy Safe Victoria: the state’s energy safety regulator, responsible for electricity, gas and pipelines safety. Data and Analytics team: - Team of 8 - Skillsets: data analysis, data modelling, BAU reporting and advanced analytics - In collaboration with Melbourne uni, Monash uni, Deakin uni and CSIRO.
  3. Featured on-going projects and future projects 1. Compliance check on e-commerce platforms 2. OCR enabled compliance check 3. Weather incident predictive model 4. Incident categorisation 5. Electrical Work on Online Service Platforms 6. Incident Forecasting model 7. Voice to text and text analytics and classifying the calls for triage. 8. RPA applications for web scraping/data pulling from online sales platforms (eBay, Amazon, etc) 9. RPA for form processing
  4. Compliance check on e-commerce platforms Background • 26% are now more willing to buy appliances online than prior to the pandemic [Bain survey] • 69% of total market revenue in Household Appliances segment will be generated through online sales by 2024 [recent Statista report]. • The results of the latest stage of the project (involving 17 high-risk categories) found 2,578 (30%) electrical products to be compliant from 8,555 listings that had a match in the database. • The research component has been presented at ACM WSDM in Feb this year (CORE A* conference in data science field)
  5. • Each listing is matched to EESS using the Model and Brand (Trade Name) • Model inputs:  Textual data for pattern matching  Image data to determine if the listing belongs to the relevant category How Does the Model Check for Compliance? Online marketplace
  6. Architecture diagram
  7. How do we add value The model facilitates audits by:  Reducing manual checks  Identifying irrelevant listings (~70% not in- category + excluded listings) Listings from Amazon, Catch and eBay Total listings: 80,087
  8. How do we add value • A compliance officer could take ~6 years to check 100,000 listings (approx. 50 listings / day). That can be done in a couple of hours with this application.
  9. Auditing Online Equipment: Stage 4 Results
  10. Journey so far Stage 5 – Continuous Improvement Discovery & Prototyping Evaluate Build & Iterate Learn Minimum viable product Audit products sold online Audit products sold in-store Crowdsource compliance information for both online and in-store audits Stages 1 & 2 • Single application to audit online sales • 1 category pilot Stages 3 & 4 • Identifying opportunities and challenges • 31 categories
  11. OCR enabled compliance check Upload / click image with brand and model information on the web app Figure 8 – OCR App Working PC view Mobile view Results
  12. Architecture diagram of OCR compliance check
  13. Weather - incident predictive model
  14. Incident Forecasting Model
  15. Network-related Incident Categorisation
  16. Featured list of projects and what’s next Projects Status Complexity Funding OCR enabled compliance check Ready to be operationalised Medium External Compliance check on eCommerce platform Ready to be operationalised High External Network-related incident predictive model Ready to be operationalised Medium Internal Incident categorisation Ready to be operationalised Medium Internal Electrical Work on Online Service Platforms Under development High Internal Incident Forecasting model Under development High Mixed Voice to text and text analytics Under development High Internal RPA for data collection Not started Medium Internal RPA for form processing Not started Medium Internal
  17. LinkedIn: Email: Q & A