Fraud Detection with Machine Learning — A Use Case


The Challenge

Financial Services Industries (FSI) face a growing problem of fraudulent transactional activities. These activities require significant financial resources to detect, flag, and evaluate. As online transactions increase, so too, has the volume and velocity of fraudulent online transactions and the need to deal with them accurately and swiftly. If the trend persists, by 2025, there could be a worldwide loss of close to USD 44 billion due to fraud.1

FSIs that leverage Machine Learning (ML) can empower their fraud decision-makers with the ability to make informed decisions to stop fraud before it impacts the business’ bottom line and the overall brand.

FSIs have processes in place to detect, flag, and evaluate fraudulent transactions. Traditionally, these processes have been rule-based. Whilst rules remain important to an anti-fraud approach, there are drawbacks, the approach is often resource-intensive, manual, difficult to scale, and prone to human error.

Some of the issues driving these drawbacks include:

  • False Positives: The more rules, the higher the chance of false positives. This can block genuine customer transactions, negatively affect a customer’s experience and require costly human intervention.
  • Fixed Outcomes: Fraud thresholds are changeable, which means rules become invalid quickly.
  • Inefficient & Hard to Scale: As fraud evolves, so too must the library of rules. This slows systems and is a burden to fraud analyst teams.

The current challenges can be addressed and overcome by incorporating ML into fraud detection processes. FSIs stand to improve both the accuracy and speed of fraud detection and flagging, saving significant resources and mitigating downstream handling and brand damage.

The Solution

With time and labor being finite resources, quick and accurate real-time detection of potentially fraudulent transactions allows for cost and time-saving benefits. Machine Learning, when applied to fraud, provides the analytic powers to identify patterns and help stop fraudulent activity before the crime impacts an FSI’s bottom line.

The advantages of using ML in a fraud solution include:

  • Predicting future fraudulent transactions while reducing human error.
  • Greater speed in risk assessment by efficient pattern identification in data.
  • Increased automation, leading to reduction of resources required for manual tasks.
  • Increased accuracy of identifying “good” vs. fraudulent transactions reducing false positives1, negative user experience, and downstream human intervention.
  • Efficient resource utilization, as models are updated with new data and features over time.
Traditional rule-based approach VS ML approach

Simplicity with the AI & Analytics Engine

The AI & Analytics Engine, a smart AI-assisted Automated Machine Learning platform, easily integrates into existing systems and processes to provide a fraud detection engine. The Engine enables FSIs to ingest and analyze large datasets of historical transactional data to provide accurate, calculated data-driven predictions on future fraudulent transactions.

80% of fraud specialists have seen AI-based platforms reduce false positives, payments fraud, and prevent fraud attempts.2

Benefits of the ML approach with the AI & Analytics Engine

  • Fast, affordable setup and easy ongoing management of ML models
  • Near real-time fraudulent detection
  • Improvement on the accuracy of ML models over time (reducing false positives)
  • Reduction in complex rule-based approaches, giving bandwidth back to employees

Want to see other ML use cases? Visit our main Use Cases page.

Book a demo with us to see how you can take the next steps towards using ML for fraud detection! Alternatively, you can sign up for a 2-week free trial of the AI & Analytics Engine to see for yourself how it works!



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