Fraud Detection - Blue Street Data

Description:

Travel and hospitality companies have moved their operations online, meaning that customers can often book a hotel room or purchase a plane ticket with the click of a few buttons. With technologies such as VPNs and the growing market for credit and loyalty cards, anticipating and preventing fraud is more important than ever. Using machine learning models to determine which characteristics indicate a potentially fraudulent transaction can greatly improve the power of fraud detection systems for hotels, airlines, and other businesses.

Problem Statement:

Determining which transactions are fraudulent or suspicious and which ones are legitimate is a complex process that involves internal and external data about customers, transactions, and finances. Without a data-driven approach to fraud detection and prevention, companies can lose massive amounts of revenue and have an increased risk of further forms of fraud targeting their operations.

Value Drivers:

Increased revenue

Reduced risk

Decreased costs

Value of Implementation:

In 2022, there were 4000 instances of airline and hotel fraud worldwide on the dark web.

The airline sector experienced a whopping 530% surge in cybercrime incidents even during the COVID-19 pandemic.

AI-powered fraud detection software, fueled by consortium fraud data, can correctly predict fraud risk on over 99.5% of transactions.

Unique Insight:

Online travel fraud is more targeted than other industries because of the high resale value of airline tickets, hotel room bookings, and getaway packages. The different kinds of fraud include chargeback fraud, account takeover, and ticket cancellations. With so much money at stake, identity verification and fraud prevention are the key to success in the travel and hospitality business.

Advanced Analytics Solutions/Algorithms:

Natural Language Processing (NLP)
- NLP can analyze the interactions between the customer and the company to identify potential identity theft or bot activity.

Anomaly Detection
- Isolation Forests or Autoencoders can analyze transaction and customer information as well as fraud and financial data to determine if a transaction could be fraudulent.

Logistic Regression
- A model that has been trained on data labeled as fraudulent or not can predict if a transaction could be suspicious.

Tree-Based Models
- These models can analyze different possible outcomes to identify patterns that could indicate fraudulent behavior.

Vesta
- Vesta provides online travel fraud protection and prevention for OTAs, airlines, and hotel operators. It can detect multiple fraud types and is powered by machine learning and artificial intelligence models that learn from every transaction. Vesta also delivers actionable, real-time data as well as dashboards that showcase metrics and trends for data-informed decision-making.

Fraud.net
- Fraud.net is powered by machine learning and enriched data to provide fraud prevention solutions. They detect unique fraud signals, such as purchase timing and geography, can support multiple global fraud case management teams, and provide powerful analytics and visualizations of your data.

1st Party Data Needs:

Transaction information
- Currency, amount, credit card

Customer information
- Name, location, demographics

Communication data
- Emails, text messages, exchanges between customer and company

3rd Party Data Needs:

Fraud data
- Patterns, trends in fraud

Financial data
- Transaction history, account information

Examples:

The mobile booking app HotelTonight serves hundreds of cities and hotels, providing a quicker and easier way to find hotel deals. They applied a customized machine learning model to predict and detect fraud which allowed them to reduce chargebacks to 50%.