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Key Criteria for Evaluating Intelligent Fraud Detection in Financial Transactionsv1.0

An Evaluation Guide for Technology Decision Makers

Table of Contents

  1. Summary
  2. Intelligent Fraud Detection Primer
  3. Report Methodology
  4. Decision Criteria Analysis
  5. Evaluation Metrics
  6. Key Criteria: Impact Analysis
  7. Analyst’s Take
  8. About Michael Azoff

Summary

The banking and financial services industry—and any large organization processing payments—faces a challenge with fraudulent activity. The move of many businesses to digital finance on the back of digital transformation, accelerated by the COVID-19 pandemic and reliance on an effective online presence, has led to a substantial increase in the number of fraud cases. The effort by banks to monitor fraud in real time (the ideal scenario) is virtually impossible without automation. While anomaly detection systems have been available for many years, using techniques such as data mining and business rules engines, it’s the latest efforts in AI that have opened the industry to innovation and improvements in the battle against fraud.

Back in the days when banking transactions required three to five working days to process, there was a period of opportunity to detect fraud; however, with digital finance enabling real-time transactions, modern IFD solutions need to operate within millisecond time windows.

Anomaly detection involves understanding what is normal and triggering alerts when abnormality occurs. The earliest attempts at anomaly detection involved establishing simple thresholds for normal behavior by the person or process under observation and monitoring for threshold crossings. With AI, a more sophisticated model can be created that goes further to exploit multiple data channels and complex data relationships, leading to more accurate results with fewer false positives and false negatives.

The varied methods of fraud are a testament to the diversity of finance industry methods for transacting payment. The growth of digital finance and internet-based payment systems have propelled an increase in fraudulent activity across the various payment systems, with the fastest rise happening in identity-related fraud.

Frauds can be categorized in two ways:

  • Perpetrator is internal to the victim organization (i.e., fraud committed by a person against the organization for which they work).
  • Perpetrator is external to the victim organization.

Of course, it’s possible for external and internal agents to be acting in unison.

According to the 2020 Global Study on Occupational Fraud and Abuse (that is, internal fraud) reported by the Association of Certified Fraud Examiners (ACFE), proactive data monitoring and analysis (encompassing both traditional and AI-based solutions) was used in only 38% of victim organizations across 2,504 cases examined by the ACFE. This statistic indicates considerable growth potential for AI fraud detection technology.

We use the term intelligent fraud detection (IFD) to describe systems that primarily use AI to monitor and reveal fraud.

Figure 1 shows a high-level view of an IFD solution.

Figure 1: Architecture of an Intelligent Fraud Detection Solution: High-Level View.

These are the typical steps followed by an IFD solution:

  • Step 1: A customer or employee initiates a transaction with the organization, such as a payments system. The IFD begins its verification process, which will use its fraud-detection capabilities.
  • Steps 2 and 3: Real-time data is gathered, which typically involves retrieving records from back-end systems.
  • Step 4: A fraud risk score is produced using an ML model, which fraud analysts and financial investigators can monitor, and intervene if necessary.
  • Step 5: A decision is made as to whether to allow the transaction to proceed.
  • Steps 6 and 7: The ML model that is used in the fraud risk assessment (Step 4) is produced in an ML studio by experts and deployed into the production process. ML model development may use external ML resources and application accelerators, both hardware and software.
  • Step 8: Periodically, the ML model is retrained with the most up-to-date data to improve accuracy and eliminate “data drift” when the environmental data has changed.

Note: In this report we will refer to financial institutions (FIs), but we embrace any organization with a payment system.

How to Read this Report

This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding, consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.
Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.

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