Thumb 0919b6ae 94ab 402f a65f 2bcc1bb4575d

Fraud Detection and Fraud Prevention Analytics for Online Travel Company

Industry Hospitality, Travel and Leisure

Specialization Or Business Function Risk and Compliance, Securities and Operations

Technical Function Analytics (Predictive Modeling, Machine Learning), Security Analytics

Technology & Tools


Project Description

We are an online travel company and are looking at fraud detection and fraud prevention analytics and intelligence from inside and outside the organization. What this implies is collecting, processing and analyzing all characteristics of a transaction and focusing on people, process and technology needed to improve fraud rates.

We would like an expert to iterate through fraud pattern changes, perform multi variable analytics, detect anomalies using strong data profiling, study fraud indicators, derived from the current transaction attributes as well as cardholder’s historical activities. Over time, we would like to build models using algorithms and machine learning to provide more predictive capabilities that can identify and mitigate fraud.

Our intent is to make as many systematic decisions as possible in order to lower overall overhead costs and ensure optimal customer experience and continuous cyber security enhancements.

The universal problem is how to quickly determine the root cause of incidents and then contain and remediate them. Once this is completed, the goal is to return intelligence from the analysis back into the system for proactive diagnostics and mitigation for continuous cyber security improvement

Expertise Required:

Statistics Ph.D. or related ML and classification methods (Multiclass SVM, MNLogit, Bayesian Belief Networks) Multiple imputation, sampling methods, factor analysis, PCA, Distributed processing , neural network technologies, GBM, data profiling and accuracy calculations, data standardization, root cause analysis, breach detection, fraud scoring,  Fraud Prevention Technologies – AVS, BIN,CVM,etc. data visualizations and master data management.

Contract period:

We are open to discussion based on your previous experience addresses similar project needs.

Location Preference:

The engagement may require some onsite presence at our New York City HQ to work and interact with our teams.

Project Overview

  • Posted
    March 25, 2016
  • Planned Start
    April 08, 2016
  • Preferred Location
    New York, New York, United States

Client Overview

Risk Modeling
Fraud Analysis
Fraud Detection

Matching Providers

Matching providers 2