5 Uses of Machine Learning For Identity Fraud In 2022

5 Uses Of Machine Learning For Identity Fraud In 2022

In response to the pandemic, many businesses had to digitize themselves to adapt to an increase in eCommerce. However, this increased fraud as well. An Aite Group report estimates that identity theft losses increased by 42% in 2020 compared to the previous year. And in 2021, FTC estimated that there were 2.8 million fraud reports — a 27% increase over 2020 at 2.2 million.

Banks and merchants often encourage users to change passwords and monitor their credit reports to minimize identity theft risk. However, in the digital world, preventing identity fraud involves much more than simply cross-checking personally identifiable information (PII). Relying on PII and credit bureau data to confirm someone’s identity is a dangerous and incomplete practice. Therefore, you need to integrate machine learning.

But What is Machine Learning?

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Machine learning (ML) involves the computer’s capability to process a large amount of data, only to figure out what to do with it after receiving it based on the data it gets.

An aspect of machine learning called deep learning involves making models for specific tasks. The tasks are highly customizable and flexible. Fraud detection is an example of deep learning.

How Does Machine Learning Facilitate Fraud Detection?

Machine learning can assist with fraud detection because the algorithms learn from past frauds, remember and identify patterns, then apply them to future scenarios.

Machine learning algorithms are valuable because they enable machines to process information much faster than humans. In addition, they identify subtle fraud traits that humans cannot. It can learn and improve rapidly because of the vast data available.

What Are the Uses Of Machine Learning For Identity Fraud?

Here are five uses of machine learning to identify fraud in 2022:

1.   Digital Identity Initiatives

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Many governments worldwide have launched digital identity initiatives to allow users to access various services online. For example, Singapore, the UAE, and Australia have already issued digital identity schemes, and the EU is moving in this direction with eIDAS revisions and European Digital Identity announcements.

Onboarding and authenticating digital applications are easier using mobile identities, which help combat account takeover fraud. Verifying a digital identity enables a consumer to sign up for any application online and authenticate using their digital identity‌. Citizens can sign contracts online legally using their digital identities via qualified electronic signatures.

2.   Email Phishing

The purpose of email phishing is to send spam emails to people to steal from them — in most cases, by convincing the victims that the email is from a company or professional within the recipient’s network. When fraudsters create high-quality phishing campaigns, they use URLs and visuals virtually indistinguishable from the web page they are impersonating.

Machine learning algorithms can help detect these attempts through logistic regression algorithms that help forecast the probability of some classes based on dependent variables. Through a linear model, they predict numbers between 0 and 1. A score of 0 to 1 shows whether the message is spam.

3.   Identification Models For Fake Accounts

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Identifying fake accounts is a classification problem, so it begins by selecting a profile you need to categorize as fake. Classification must look at parameters like engagement rate, activity, number of followers relative to the number of people the account follows, and relevance of comments.

A binary classifier like Naive Bayes, SVM, Decision Trees, or Logistic Regression requires the feature matrix to be built, which you feed into the classification model. You can continuously train the classifier with new information about fake and authentic accounts, which helps improve its accuracy.

4.   Fraudulent ID Document Detection Models

Fake IDs are becoming a more popular fraud tactic, especially with the advent of high-quality fakes that are increasingly difficult to verify as legitimate and create a risk of identity theft. Through image processing, machine learning can detect ID forgery fraud. A machine learning model is used to train machines to analyze images based on the visual information held in data.

It mimics the visual cortex’s ability to process visual information (called Convolutional Neural Network – CNN). To learn to distinguish actual documents from forged ones, you must use the method with several images.

5.   Educating Machines Right From Wrong

Educating Machines Right From Wrong

As far as how ML can help battle fraud, this post has covered a better part of it – albeit simplified and watered-down. But how long will it take us to reach a point where it can teach and operate itself to reduce human error and increase consumer safety worldwide? Currently, developers are trying out three different training techniques to improve the algorithm:

  • Semi-Supervised Learning —In most cases, ML can predict fake identity documents. Sometimes, worn-out or old passports and identification documents raise false alerts. A trained professional can keep the system on track through supervised learning.
  • Data Mining — It involves processing large amounts of data at once. Assisting AI in understanding and correcting the complex patterns and probability of identity theft, not only for fake documentation but also for several fake documentation.
  • Regression Analysis — You continually test and reassess the algorithm to ensure that it is learning on the right curve and making progress daily.


The fraud detection landscape has significantly benefited from machine learning. With fraudsters adopting new technologies, including Machine Learning, and a growing sophistication in fraud, banks and financial companies need to act quickly to build scalable and robust Machine Learning pipelines that identify fraud efficiently and effectively.

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About the Author: Barry Lachey

Barry Lachey is a Professional Editor at Zobuz. Previously He has also worked for Moxly Sports and Network Resources "Joe Joe." he is a graduate of the Kings College at the University of Thames Valley London. You can reach Barry via email or by phone.