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Fraud Analytics Course

Become an expert in Fraud Analytics with ‘Certification Course in Fraud Analytics’ you will emerge as a professional ready to grab innumerable opportunities and boost your career in the space of investigating team with Statistical Analysis, Machine Learning, Predictive Analytics, and many more.
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Fraud Analytics course - 360digitmg
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Fraud Analytics is described as the application of analytics technology and techniques along with domain experts to detect fraudulent transactions done with the intent of fraud or bribery either before or after the transaction takes place. The job of data analyst who is specialised in Fraud Analytics will gather and store the relevant data and process it to study the patterns, anomalies, and dispensaries. The story narrated by the data will help analysts to get more insights that can help a company in managing any potential threats that occur in future.

A Data analytics approach is used to understand the data to detect the anomalies through outliers existing in the data which indicates an irregular pattern. Fraud Detection helps in applying statistical sampling techniques for better understanding of data. The major types of occupational fraud are reviewed and specific data analytical detection tests.

Fraud Analytics

Program Cost

INR 12,000/-

Overview of Fraud Analytics Course

Fraud analytics combines analytic technology and techniques with human interaction to help detect potential improper transactions, such as those based on fraud and/or bribery, either before the transactions are completed or after they occur. The process of fraud analytics involves gathering and storing relevant data and mining it for patterns, discrepancies, and anomalies. The findings are then translated into insights that can allow a company to manage potential threats before they occur as well as develop a proactive fraud and bribery detection environment. In areas of anti-fraud, anti-bribery, and anti-money laundering, the regulatory environment has tightened. At the same time, fraud, corruption, and abuse are unrelenting—and constantly evolving. It’s a different world out there. And fraud analytics can help make sense of it.
Adding analytics to this mix can significantly expand fraud detection capabilities, enhancing the “white box” approach of the rules-based method.
Identify hidden patterns, Enhance and extend existing efforts
Use unstructured data to improve organisational processes and efficiency.
Use the Predictive Modelling features for planning
ML Algorithm trained to Identify concealed observations from any normal observations.
Changing Patterns over time.
Imbalance in the Classification of Fraud detection Model.
Time consuming in feature generation.

Fraud Analytics Course Training Learning Outcomes

This course provides analytical skills to Identify different hidden patterns for the data using various Machine Learning techniques and deep learning mechanisms. Also, Unsupervised or non-rules-based analyses driven by analytics technology can help in drawing new patterns and scenarios that are not upto the mark in traditional approach. Data Analytics will be beneficial as an extra layer for existing effort by enhancing statistical skill sets.
This course is designed as per the market trends which enhance the required skills. A lot of emphasis is given on important topics such as Fraudulent Analytics using Time series forecasting, various kinds of Analytics, Applications of Data Optimization, and many more. The concepts are based on real-time use cases so that the participants can understand the exact work environment.

Work with various information sources
Analyse structured and unstructured data using different tools and techniques
Develop an understanding of descriptive and predictive analytics
Apply data-driven, machine learning approaches for HR functions such as attrition prediction
Understanding how surveys work and perform analytics on them
Use ML techniques to create a personalized experience for employees
Use data visualisation concepts to represent data for easy understanding
Block Your Time
hr analytics course - 360digitmg

16 hours

Classroom Sessions

hr analytics course - 360digitmg

20 hours

Programming Videos

Who Should Sign Up?
  • IT Engineers
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • Math, Science Graduates
  • Graduates Planning to Apply for Railway Jobs

Fraud Analytics Course Modules

The digital world is built on the walls of data and this course is designed thinking about the future. Fraud analytics is into all data and combining it to provide different strategies for predictive insights that can be used for optimising the fraudulent activities. In this module on Fraud analytics course, you will learn about the various systems and machine learning models of Fraud analytics course and how it predicts illegal transactions or irregular patterns from data. You will also learn how Deep Learning is understanding the pattern from the data. Along with the various advanced fraud analytics techniques, you will also learn regarding Data Privacy and Security.

Understanding an overview of Data Analytics and their importance in the stream of Fraudulent detection using statistical analysis and also the vital role of data analytics in Fraud detection using Artificial Intelligence and Machine Learning.

Understanding different stages of Analytics with the help of Mind map of Data science which includes understanding of data using graphical representation. Machine learning algorithms that help for data analytics followed with various error functions & the appropriate validation methodology helps in a strong foundation for the course.

Understanding Descriptive statistics with various Business moments decisions that are part of preprocessing will help you continue the course.

Understand K-Means clustering model which is Unsupervised Machine Learning model for fraud analytics for better predictions.

Understanding Supervised Classification Machine Learning Model and their prerequisites and importance for fraud analytics to classify the irregular pattern detection in unbalanced data.

Understand how Supervised Machine learning method deals with continuous data in analysing unbalanced data to predict fraudulent behaviour in unbalanced data.

Better Understanding of different Classification models in segmenting the unbalanced data and their comparisons for better accuracy.

With the basic knowledge of Deep learning and their methods for data analytics helps in making better predictions and classification with improved accuracy.

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