Data Mining to Identify Anomalies in Public Procurement Rating Parameters

• Performed Descriptive Analysis on Public Procument Data
• Built an anomaly detection model using the R anomalize package to detect anomalies in time-series data
• Built a R shiny dashboard with two main functionalities: (1) Descriptive Analysis and (2) anomaly detection on public procurement data
• Business Science anomalize package: https://business-science.github.io/anomalize/index.html

https://ds-analytics.shinyapps.io/Anomalies-in-Public-Procurement/

Public Procument

• Public procurement refers to the purchase by governments and state-owned enterprises of goods, services and works.
• The public procurement process is the sequence of activities starting with the assessment of needs through awards to contract management and final payment.
  

Bids and Tender

• Bidding is an offer to set a price tag by an individual or business for a product or service or a demand that something be done.
  
• A business tender is an offer to do work or supply goods at a fixed price.
• The tender or bid process is designed to ensure that the work to be done is given out in a fair way

Anomaly Detection

• Anomaly detection is the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behaviour.
• Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data.
  

Why Anomaly Detection?

• To detect suspicious activities within the public procument stage that deviate from the usual activities
• To identify possible/potential fraudulent activities in public procument or in the rewarding of tenders for government goods.
• For fraud detection in tender awarding and public procurement stages
  

More Info on Tender Fraud: https://www.purchasing-procurement-center.com/tender-fraud.html

RStudio Version: 2022.07.1 Build 554
Libraries: tidyverse, plotly, highcharter, lubridate, xts, DT, anomalize, tibbletime, shiny, bs4Dash, shinycssloaders, waiter
Public Procurement Dataset: https://data.world/city-of-ny/9k82-ys7w
Dataset Info: https://data.cityofnewyork.us/City-Government/Bid-Tabulations/9k82-ys7w

Average Bid Price Overtime


Top and Bottom 5 Bid Information


Contact Person Bid Prices Overtime


Anomaly Detection