Our course "Data Mining for Controlling and Fraud Detection" provides an introduction into data mining methods and demonstrates how data mining can be employed in various accounting, controlling, and auditing related tasks by companies, financial institutions, insurance companies, and tax authorities. Accounting data can be analyzed and leads to models for detecting patterns and irregularities, improper accounting practices, dubios transactions, potential fraud, money laundering, and other undesired activities as well as for transaction monitoring, credit scoring, credit default prediction, risk assessment and minimization, finding risk factors, and predicting expected future demand, prices, and sales. This course also describes the practical steps necessary to create such models with the open-source data mining software RapidMiner. The large amount of practical examples and exercises enables the participants to design appropriate data mining processes and apply the gained knowledge to their data mining problems and to solve them efficiently and successfully.

You can register to this course online.

Details

• Course ID: 130803
• Date: August 28th - 29th, 2008
• Number of days: 2 days
• Location: Dortmund, Germany
• Target audience: employees of accounting, controlling, auditing, or internal revision departments or tax authorities; decision makers, accountants, auditors, analysts, developers
• Previous knowledge: basic knowledge of computer programs
• Methods: lectures, discussions, individual and group work, exercises on realistic data. Participants may introduce own work and project specific questions in order to find particular solutions together with the trainer and other participants.
• Content: this course includes a compact introduction into the foundations of data mining and into the software RapidMiner. It addresses beginners and intermediate learners. Topics of this course are
  • Introduction to data mining methods for data cleaning, finding reliable indicators and key factors, feature selection, pattern and outlier detection, anomaly detection, automated classification, segmentation, clustering, regression, forecasting, predictive analytics
  • Basics of cost-sensitive machine learning and data mining with RapidMiner
  • Identifying your most profitable business units, and what characteristics make them so
  • Identifying behavioural changes – detecting opportunities and risks
  • Detecting patterns, irregularities, and inidcators for identifying and preventing improper accounting practices, dubious transactions, potential fraud, money laundering, or other undesired activities
  • Credit scoring, credit default prediction, and risk factors for risk assessment, mitigation, and minimization
  • Predicting future demand, prices, and sales
  • Optimizing controlling actions by cost-sensitive data mining
  • Mining structured and unstructured data, i.e. database tables as well as e.g. textual information using statistical analysis, data and text mining
  Extensive exercises on different data sets will be performed for all topics. For advanced topics like text mining, sophisticated forecasting, classification and clustering, introductions and application examples are provided here, but for an in-depth coverage and understanding, special courses on these topics are offered and recommended.

Prices

Value added tax (VAT) may have to be added to these base prices.