Fraud Detection in financial institutions: Prevention and management

The detection of anomalies (also called detection of toolkits) is a task of machine learning which consists in detecting in the data, the instances (individuals) having a behavior different (unusual) from the other instances of the base called normal. In the case of fraud detection for example, any very strange spending by credit card is suspicious.
The variations are so numerous and the examples of training so rare, that it is not possible to know what a fraudulent activity or an incident looks like.

Preconditions

General knowledge of information systems

Date / Place

  • June 21, 2020 at 9:00 AM
  • The exporter’s house (CEPEX), Tunis, Tunisia

Program

  • Overview of problems & types of supervised and unsupervised learning for the detection of anomalies
  • Main Anomaly Detection Learning Models and Algorithms
  • Practice on datasets with scikit-learn (http://scikit-learn.org/) under Python on case studies from the banking sector.
  • Build a fraud detection model in banking transactions
  • Implementation of an empirical study using supervised learning algorithms for churn prediction and for automatic bank credit approval.

Goals

  • Detect anomalies by learning what normal activity looks like (using a history of supposedly non-fraudulent transactions) and identifying anything very different.
  • Supervised and other unsupervised approaches to fraud detection
  • Experimentation with solutions to respond to various problems related to the detection of anomalies with the Python language, in the banking and financial fields.

Participants

  • Company managers,
  • Managers,
  • Directors and managers of information systems departments,
  • Financial companies

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