#DigitalDefence Hack
Winner Showcase

This is the second in a five-part series which will showcase the projects built by the Top 5 winners of the #DigitalDefence Hackathon – the biggest international cybersecurity challenge of 2020!

Team Dine Detect was one of the Top five finalists of the Digital Defence Hack - the largest cybersecurity hackathon of 2020

Team Dine Detect

 Jainish Shah, Revathy Sivaraman, Hanifa Baporia, Ayaz Mujawar

 

Team Dine Detect created a customer fraud detection software for restaurant owners. They ranked #4 globally across all challenges

Challenge by our lead sponsor Oracle - Anomaly Detection

When considering the different phishing techniques, privileged cloud administration accounts and service accounts, accounts with access to high-value systems such as finance systems, are high priority targets and are constantly the target of cybercriminals. How might we better understand, detect and alert on compromised or suspicious accounts based upon unexpected actions or transactions within environments and applications?

Problem Statement

Restaurants owners, specifically small businesses, face multiple false orders, last minute cancellations through bogus complaints, and fraudulent refund requests, etc. This causes massive losses to the owners. 

Motivation:

Restaurants should be able to differentiate between legitimate and fraudulent orders and claims, without having to slow down their service. 

Solution:

A subscription based software that uses historical data (frequently updated) to gauge the authenticity of a customer

 

This project works like a grocery shop manager who can estimate when fruits will ripe or go rotten based on their characteristics.

Feature Highlights:

  • Easy to use dashboard
  • Smooth integration with the restaurant
  • Fraudulent transaction detection without added effort on part of the restaurant owner
Food delivery

What does it do:

This software is a subscription style solution. It gathers historical data that is frequently updated, to understand customer demographics details. The software also analyzes customer behaviour and then, using a clustering technique, it is able to detect fraudulent customers. The analysis is shown through a dashboard that restaurant owners will have access to. It’ll aid the owners to dismiss or further verify probable fraudulent orders. 

Company costs: 

  • Cloud hosting and server infrastructure
  • Marketing and outreach
  • Administration and overheads

Revenue Stream:

  • Subscription revenue from restaurant owners

What do you think about this project? Three more projects will be up in the new few weeks. Let us know on Linkedin.