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. Companies are estimated to spend >$20bn annually on fraud detection.

Since fraudsters improve their techniques over time and since number of transactions are too numerous to deal with manual controls, companies need to rely on machine learning to build resilient and efficient fraud detection systems.

What Is Fraud Detection?

Fraud detection is a major challenge for

  • merchants that accept electronic payments
  • acquirers that manage electronic payment networks
  • for 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.

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.
  • 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 device 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?

Fraud detection and prevention systems are inevitable for any fraud risk management strategy. A functional fraud detecting software is an important business need and the market is expected grow strongly.

As the number of digital payments and online transactions increase, the global number of frauds are expected to increase. The number of digital buyers is expected to exceed 2 billion by 2021.

According a recent Forbes Business Insights survey, global Fraud detection and prevention market value is $22 billion in 2019, and it is expected to reach $ 110 billion by 2026.

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.

Common Use Cases

The most common use cases of fraud detection includes account related frauds, payment & transaction frauds and policy and regulation related frauds.

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
  • Stolen merchandise
  • False request for refund

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

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
  • Site cloning
  • ATM frauds

E-Commerce/ Retail

  • Promo abuse
  • Payment fraud
  • Delivery Fraud

Marketplaces and Online Classifieds

  • Fake reviews
  • Referral and promo abuse
  • Scams

IT/Telecom

  • Phone Fraud

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.

What are Fraud Detection Companies?

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

If you have questions about how fraud detection can help your business, we can help:

Let us find the right vendor for your business

 

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