Saltar al contenido principal
InicioRFraud Detection in R

Fraud Detection in R

Learn to detect fraud with analytics in R.

Comience El Curso Gratis
4 Horas16 Videos49 Ejercicios
6816 AprendicesTrophyDeclaración de cumplimiento

Crea Tu Cuenta Gratuita

GoogleLinkedInFacebook

o

Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.
Group¿Entrenar a 2 o más personas?Pruebe DataCamp para empresas

Preferido por estudiantes en miles de empresas


Descripción del curso

The Association of Certified Fraud Examiners estimates that fraud costs organizations worldwide $3.7 trillion a year and that a typical company loses five percent of annual revenue due to fraud. Fraud attempts are expected to even increase further in future, making fraud detection highly necessary in most industries. This course will show how learning fraud patterns from historical data can be used to fight fraud. Some techniques from robust statistics and digit analysis are presented to detect unusual observations that are likely associated with fraud. Two main challenges when building a supervised tool for fraud detection are the imbalance or skewness of the data and the various costs for different types of misclassification. We present techniques to solve these issues and focus on artificial and real datasets from a wide variety of fraud applications.
Empresas

Group¿Entrenar a 2 o más personas?

Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más
Pruebe DataCamp Para EmpresasPara obtener una solución a medida, reserve una demostración.
  1. 1

    Introduction & Motivation

    Gratuito

    This chapter will first give a formal definition of fraud. You will then learn how to detect anomalies in the type of payment methods used or the time these payments are made to flag suspicious transactions.

    Reproducir Capítulo Ahora
    Introduction & Motivation
    50 xp
    Imbalanced class distribution
    100 xp
    Cost of not detecting fraud
    100 xp
    Time features
    50 xp
    Circular histogram
    100 xp
    Suspicious timestamps
    100 xp
    Frequency features
    50 xp
    Frequency feature for one account
    100 xp
    Frequency feature for multiple accounts
    100 xp
    Recency features
    50 xp
    Recency feature
    100 xp
    Comparing frequency & recency
    100 xp
  2. 3

    Imbalanced class distributions

    Fortunately, fraud occurrences are rare. However, this means that you're working with imbalanced data, which if left as is will bias your detection models. In this chapter, you will tackle imbalance using over and under-sampling methods.

    Reproducir Capítulo Ahora
Empresas

Group¿Entrenar a 2 o más personas?

Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más

Sets De Datos

Chapter 1 datasetsChapter 2 datasetsChapter 3 datasetsChapter 4 datasets

Colaboradores

Collaborator's avatar
Hadrien Lacroix
Collaborator's avatar
Sara Billen
Collaborator's avatar
Chester Ismay
Bart Baesens HeadshotBart Baesens

Professor in Analytics and Data Science at KU Leuven

Ver Mas

¿Qué tienen que decir otros alumnos?

¡Únete a 14 millones de estudiantes y empieza Fraud Detection in R hoy mismo!

Crea Tu Cuenta Gratuita

GoogleLinkedInFacebook

o

Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.