Fraud Detection: In-Depth Guide [2024 Update]

With the increase in digital banking and e-commerce transactions, digital fraud has become a larger threat. There are numerous types of fraud such as account take over and new account fraud. That has led to an increase in investment to fraud detection and prevention, with this specific market estimated to surpass $141.75B valuation by 2028.

With fraudsters improving their techniques, and with the number of transactions to be too numerous to manually monitor, companies need to rely on machine learning to build resilient and efficient fraud detection systems.

In this article, we plan to explain what fraud detection is, how it works, and the top vendors of the solution are.

What is fraud detection?

Fraud detection is a major challenge for

• Merchants that accept electronic payments,
• Acquirers that manage electronic payment networks,
• Banks that are exposed to various types of financial fraud, including money laundering,
• Other organizations that rely on user accounts such as e-commerce marketplaces, telecom operators etc.,
• And basically any other company that issues receipts and invoices for its transaction.

Fraud detection is important because if undetected, fraudulent invoices will result not only in a financial loss, but will also create potential compliance risk, as sanctioned organizations could be behind the invoice fraud.

How does fraud detection work?

Machine learning is the common method to detect fraud. Data analysis and pattern recognition are the key steps to building a fraud detection model. The detailed steps in fraud detection are:

• Data collection: Data must be collected and analyzed from every possible source. Data will be used to identify fraud activities. Sometimes this data is contained in documents and document data extraction technologies may need to be used.
• Data Classification:
  • Collected data is classified by the fraud detecting tools to make automated decisions to approve or reject transactions or to refer transactions to an analyst who will make the final decisions.
  • Potential fraud activities and safe activities have their own characteristics based on their behavioral patterns. For example, fraud detection tools review attributes like browser fingerprinting, proxy filtering and behavioral analytics which are connected to an activity or transaction.
• Decision and Feedback: Some tools may provide visual analysis, trend analysis, queries, reports, and scores, to help analysts make manual decisions. These manual decisions provide feedback to improve the machine learning models.

Why does it matter now?

A functional fraud detecting software is an important business need and fraud detection and prevention systems are inevitable for any fraud risk management strategy. As the number of digital payments and online transactions increase, the global number of frauds is also expected to increase: global losses to eCommerce fraud was $20 billion in 2021, representing a 18% growth from 2020.

As a result, the global fraud detection and prevention market value is expected to reach $110 billion by 2026 from $22 billion in 2019.

What are use Cases/Applications of Fraud Detection?

Banks and other companies that receive significant number of financial transactions are at risk of suffering from financial fraud. e-Commerce companies, credit card companies, electronic payment platforms, B2C fintech companies all need to employ software to limit financial fraud.

Account related frauds

There are two different types of account related frauds: new account fraud and account takeover.

• New account fraud: Fake identities must be prevented to create new accounts. The patterns of various device and session indicators are used to identify frauds.
• Account takeover fraud: Account theft results with the hackers to obtain products and services by using another person’s exciting accounts. In order to prevent this, session, device, and behavioral bio-metrics of the user is computed and scored to verify an account.  Accounts taken over by hackers leveraging various social or technical vulnerabilities. Analyzing user journeys for behavioral patterns can help detect account takeovers before they cause financial harm

Payment & transaction frauds

• Transaction fraud: Fraud committed using credit, debit or prepaid cards in merchants for card-present or card-not-present payments.
• Unauthorized transactions: Transactions that have not been unauthorized and have gone through without the merchant’s consent.
• Stolen merchandise: Merchandise that have been taken but not paid for.
• False request for refund: Merchandise that have not been sold, yet have received a request for refund by a counterparty.

Real time risk scoring models are used to score various financial operations to eliminate fraud while managing the impact of false positives.

Invoice fraud

Certified Fraud Examiner (CFEs) estimate that companies lose 5% of their revenues to fraud. Asset misappropriation, the most common case of fraud is most commonly perpetuated through fraudulent invoices according to ACFE (Association of Certified Fraud Examiners).

Modern Accounts Payable (AP) automation companies have built AI-powered tools to identify invoice fraud by:

• History of transactions with the company
• Checking relevant records about the company
• Checking the items in the invoice vs companies’ general purchases

Feel free to check our data-driven, prioritized list of AP solution providers to minimize your company’s losses to invoice fraud.

Industry-specific use cases

Banking & Financial Services

Frauds have been increasing with the increasing number of online/ATM transactions in recent years. The most common types of banking frauds are:

• API fraud: PSD2 mandates certain European financial institutions to open up their services via APIs. This creates a new attack surface that banks need to manage.
• Stolen/fake credit card frauds: Transactions that were made by someone else’s cards, or with fake ones.
• Site cloning: Fake websites, imitating a legitimate counterpart, that have received payments from users.
• ATM frauds: Cash withdrawals using someone else’s card.

E-Commerce/ Retail

• Promo abuse: A common type of abuse especially for online subscriptions, where users find a loophole with a company’s promotional offers. An example is creating multiple accounts for Netflix after each trial-run has ended.
• Payment fraud: A broad category, consisting of any type of transactions made by a cybercriminal.
• Delivery Fraud: A specific type of fraud where the user, having already received their goods, claims otherwise to have another one sent to them.

Marketplaces and Online Classifieds

• Fake reviews: This has been growing in prominence, where users, or bots, write fake reviews on a vendor’s online profile to tarnish its reputation.
• Scams: This is where a payment gets made not for the original advertisement, but for another item, or lack thereof.

IT/Telecom

• Phone fraud: This is when criminals call random phone numbers, posing as a charity representative or any other legitimate organization, to incentivize people to give money.

How is AI changing fraud management?

Since fraud constantly evolves, it is hard to manage fraud with rule sets. Fraud detection systems should be able to identify new types of fraud which requires detecting anomalies that are seen for the first time. Therefore, detecting fraud is an anomaly detection exercise which is a sub-field of AI application and research.

We have an article that dives deep into this matter.

What are some fraud detection solutions vendors?

Here is a comprehensive list of AI vendors that have leading edge fraud detection solutions.

For more on fraud detection

If you are interested in learning more about fraud detection, read:

• How AI/ML Models Can Improve Fraud Detection & Prevention?
• 4 Steps To Protect Your Brand From Copyright Infringement
• Ad Fraud in 2022: What Is It, How It Works, & How to Combat It?

Finally, if you are interested in adopting a fraud detection system for your enterprise, we have a data-driven list of vendors prepared.

We will help you choose the best one tailored to your needs: