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B2C Fraud

What Is B2C Fraud?

B2C fraud includes insurance fraud, payment fraud, identity theft, etc. and reviewing claims is so time-consuming and difficult that many insurers do little more than a cursory review on small claims. Fraudsters know this and will file—and win—small claims for losses that didn’t really occur.

Why Is It Important to Have a Good B2C Fraud Model?

Machine Learning is fast and efficient (and cheap!) – When it comes to fraud decisions, you need results FAST! ML is like having several teams of analysts running hundreds of thousands of queries and comparing the outcomes to find the best result. But ML models do it all in real-time, in mere milliseconds.

As well as making real-time decisions, ML is assessing individual customer behavior as it happens. It’s constantly analyzing ‘normal’ customer activity, so when it spots an anomaly it can automatically block or flag it for analyst review.

Moreover, ML is more accurate than humans at uncovering non-intuitive patterns or subtle trends before they cause any damage.

What Internal Data Should I Have for a Good B2C Fraud Model?

For machine learning, as much data as possible should be included. Additionally, the data should be tagged as good (honest customers) and bad (risky customers who have made charge-backs).

What External Data is Essential for a Good B2C Fraud Model?

Fraud signals can be divided into different categories, such as identity markers (the age of an account, how many devices were used to enter the account), incongruities between billing name and customer name, and more.

What External Data May Prove Useful for a Good B2C Fraud Model?

Businesses can work with law enforcement to share some information about the clients. Businesses not only look for red flags in terms of conflicts but they also look for connections to organised crime.

What Are the Main Challenges of the B2C Fraud Use Case?

Machine learning models need a lot of data in order to work properly—without enough data points, the machine can draw incorrect inferences and flag honest customers or miss fraud cases.

Interesting Case Studies and Blogs to Look Into

Intellias: How to Use Machine Learning in Fraud Detection
Ravelin: Machine Learning for Fraud Detection

Tangible Examples of Impact

According to Infosecurity Magazine, fraud cost the global economy £3.2 trillion in 2018. For some businesses, losses to fraud reach more than 10% of their total spending. … Capgemini claims their ML fraud detection system can reduce fraud investigation time by 70% while increasing accuracy by 90%. Another ML fraud prevention solution provider, Feedzai, claims that a well-trained machine learning solution can identify and prevent 95% of all fraud while minimizing the amount of human labor required during the investigation stage.

Large corporations like Airbnb, Yelp, and are already using AI solutions to get insights from big data and prevent issues such as fake accounts, account takeover, payment fraud, and promotion abuse. Machine learning takes care of all the dirty work of data analysis and predictive analytics and allows companies to grow and develop safe from fraud.

How to Use Machine Learning in Fraud Detection

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