Fraud Detection techniques using AI and ML- Melbourne,Australia – 2019

Conference Info:

AI Live APAC – Exploring how Artificial Intelligence and Intelligent Automation can revolutionize your enterprise AI Live is the seventh online summit from the AIIA Network held on July 2019 with several international speakers from various industries discussing the main topic of AI For Financial Crime And Fraud Analysis

Sponsors : edgeverve, Hexaware, TCS

Topic Summary:

Fraud detection and prevention become easier through machine learning. Huge amounts of data can be fed to capable machines that can analyze suspicious behavior against past records and flag such activity. … Machines can also be programmed to self learn in the unsupervised model of artificial intelligence.

Five Keys to using AI and Machine learning in Fraud Detection.

  1.  Integrating Supervised and Unsupervised AI Models in a Cohesive Strategy
  2. Applying Behavioral Profiling Analytics in Fraud Detection.
  3. Distinguishing Specialized from Generic Behavior Analytics
  4. Leveraging Large Datasets to Develop Models
  5. Adaptive Analytics and Self-Learning AI.

AI in fraud is dependent on machine learning that, in turn, is dependent on humans who feed it data it can learn from and the underlying neural networks as well as other algorithms. Today, hardware is up to the task to analyze large amounts of streaming big data in real time and enable decisions with low latency. Accuracy increases as machines self learn and detecting anomalies can be easy. How efficiently fraud prevention or detection is performed depends on the algorithms in use. Various algorithms are in use in the background to power machine learning designed for fraud detection and prevention. The Machine Learning Benefits Fraud Prevention It’s scalable, faster, more efficient and cost.

The AI Is Predicting the Future of Online Fraud Detection

List of Speakers:

Key Takeaway:

In the current business world, evidence suggests that fraud, irregularity and corruption are on the increase. These crimes are committed in order to gain unfair advantage in both corporate and family businesses without any sectoral distinction. In an effort by the Association of Certified Fraud Examiners (ACFE) in 2016, it was reported that businesses lost 5% of their annual income through fraud. According to the same report, it is reported that the total loss at 2,410 cases was $ 6.3 billion and 23% of the cases had lost $ 1 million.

Fortunately, there are data mining techniques which you can use to detect fraud. The session will provide a brief on how to apply these techniques to the data.

  • Outlining the latest data mining techniques and evidence for their effectiveness
  • How to apply these techniques within your enterprise to detect fraud
  • Applying these techniques on the data: how to get started on best practice fraud prevention
  • What to expert in terms of ROI

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