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InfoQ Homepage Articles Generative AI: Shaping a New Future for Fraud Prevention

Generative AI: Shaping a New Future for Fraud Prevention

Key Takeaways

  • Digital transformation has led to sophisticated fraud methods like synthetic identities and deep fakes, escalating rapidly due to changes in economic activities and consumer behavior.
  • Traditional fraud detection systems face challenges in scalability, data balance, and require significant manual input, struggling to adapt to new fraud patterns.
  • Generative AI revolutionizes fraud detection with its adaptive learning abilities, capability to handle large data sets, improved anomaly detection, and reduction in false positives.
  • Combining Generative AI with Machine Learning, enhances adaptability, fraud pattern analysis, and contextual understanding in fraud detection

This is a summary of a talk I gave at QCon SF in October 2023. The complexity of fraudulent actions is rising along with the expansion of the digital era. Our lives are now much simpler because of the conveniences of Internet banking, e-commerce, and other transactions. But this convenience has a significant drawback: it makes everyone more vulnerable to fraud.

Many apps and websites now hold a plethora of priceless data, including personal, financial, and health information. Regrettably, criminals have adeptly utilized this digital convenience for their own gain, resulting in a surge of complex fraudulent schemes, including identity theft, deep fakes, and online payment scams. Such fraud has enormous financial ramifications which costs consumers and corporations together hundreds of billions, if not trillions, of dollars in losses.

Factors Driving the Surge in Fraud

There are two primary reasons behind the rapid increase in fraud.

First, as economic activities evolve and consumer behaviors change, fraudsters adapt and exploit weaknesses in systems.

The COVID-19 pandemic, for instance, saw a 52% surge in digital fraud rates between 2019 and 2021, particularly in the travel and financial services industries.

Secondly, consumer expectations for seamless digital experiences have grown exponentially. Consumers demand fast, secure, and convenient interactions, and they are quick to switch to competitors if these expectations are not met.

Top Fraud Trends

To better understand the need for advanced technology in fraud prevention, there are several key trends in the fraud industry:

  • Automation: Fraudsters are using various software bots, some powered by generative AI, to automate fraudulent activities. This automation has made fraud more scalable and efficient than ever before.
  • Escalating Costs: The financial impact of fraud is increasing, with consumers losing billions annually. Globally, fraud losses surpass $5 trillion, highlighting the urgent need for effective prevention measures.
  • Synthetic Identity Fraud: This is one of the fastest-growing forms of fraud, comprising more than 85% of identity fraud cases. Synthetic identity fraud is powered by generative AI, making it challenging to detect using traditional methods due to the lack of sufficient training data.
  • Balancing Consumer Experience: Companies must strike a balance between minimizing consumer friction and preventing fraud. Meeting customer expectations for seamless experiences while maintaining security is a complex challenge.
  • Proliferation of Point Solutions: Numerous specialized tools exist for different stages of the customer journey, but integrating data from these point solutions into a comprehensive risk management system is essential for effective fraud prevention.

Evolution of Fraud Detection

Risk management and fraud detection have experienced a significant change. The three different generations of technology that have shaped this transformation are as follows:

  1. Risk 1.0 Systems, which use static rule-based approaches;
  2. Risk 2.0 Systems, which combine traditional machine learning with rules; and
  3. Risk 3.0 Systems, which are the most recent and use generative AI in addition to traditional machine learning to address complex and emerging fraud patterns while lowering false positives.

As these technological generations have radically changed how firms fight fraud and manage risks in today's dynamic and connected world, it is imperative to understand their subtleties and evolution in order to navigate this terrain effectively.

Drawbacks of Existing Fraud Detection Methods

Before delving into the advantages of generative AI, it's crucial to understand the shortcomings of traditional fraud detection methods:

  1. Limited Scalability: Traditional machine learning models may struggle to efficiently scale as transaction complexity increases, often involving hundreds of features.
  2. Feature Engineering Overhead: Manual feature engineering is a time-consuming process, requiring data extraction, transformation, and cleaning. It may still miss essential information for accurate fraud detection.
  3. Data Imbalance: Fraudulent transactions are rare compared to legitimate ones, leading to imbalanced training data. This imbalance can skew traditional models' ability to detect fraud accurately.
  4. Lack of Context: Previous-generation methods may not incorporate a wide range of variables or understand the context, limiting their effectiveness in detecting complex or subtle fraud schemes.
  5. Need for Human Oversight: Human intervention is often required for model tuning, updates, and manual verification of flagged transactions, leading to resource-intensive operations.
  6. Lack of Adaptability: Static rule-based systems and some traditional ML based risk systems lack adaptability and require frequent manual updates to address evolving fraud challenges.

AI Risk Decisioning

A new category called "AI Risk Decisioning" is poised to transform the landscape of fraud detection. It leverages the strengths of generative AI, combining them with traditional machine learning techniques to create a robust foundation for safeguarding online transactions. This dramatically improves the accuracy and speed of fraud detection and prevention. By analyzing data collected from user activity, AI Risk Decisioning platforms like Oscilar can quickly identify suspicious behavior and alert organizations about potential fraudulent activity.

Let's explore the core pillars that define this approach:

Knowledge: A 360-Degree Knowledge Fabric

The first pillar involves creating a comprehensive knowledge fabric that serves as the foundation for the entire platform. This fabric integrates various internal data sources unique to the company, such as transaction records and real-time customer profiles. Additionally, it incorporates external knowledge from consortium databases, open-source intelligence databases, and academic research. This integration of data creates a holistic view, enhanced with real-time stream processing methods. Importantly, it adds a layer of intelligence and reasoning, forming a cognitive core for effective risk management.

To illustrate the impact of this knowledge fabric, consider the example of synthetic payment fraud. Traditional methods struggle to differentiate between money laundering and legitimate unusual transactions in this complex form of fraud, primarily because it evolves rapidly and is powered by generative AI. Generative AI, on the other hand, continuously analyzes unstructured data, forming an adaptive knowledge fabric. This fabric identifies critical features for flagging payment fraud, such as account dormancy, account age, and changes in account information. It distinguishes between good and fraudulent behavior in real-time, making it a powerful component of the AI Risk Decisioning approach.

This approach combines traditional machine learning techniques with generative AI and the knowledge fabric, continuously updating models based on real-time transaction data and labels, ultimately enhancing fraud detection capabilities.

Creation: A Natural Language Interface

The second pillar of the AI Risk Decisioning approach introduces a natural language interface for creating fraud rules or models, making the process highly accessible. This interface allows users to customize workflows, models, and other components without requiring coding expertise or deep analytical skills. For instance, if you want to create a model for detecting account takeovers, you can specify features or let the system automatically identify relevant ones, such as tracking suspicious login behavior or deviations from a user's previous login patterns.

The natural language co-pilot translates these requirements into a machine learning model, conducts testing to assess its performance, and provides you with the results. Additionally, it offers the flexibility to incorporate device intelligence seamlessly. Furthermore, you can request backtesting, where the system analyzes how the model would have performed in past scenarios, aiding in decision-making.

Importantly, the human element remains an integral part of the process, as the AI system equips individuals with valuable risk insights. This democratizes risk management by enabling a broader team with varying skills to tackle fraud effectively, and makes fraud prevention programs more scalable and inclusive.

Recommendation: Auto-recommendations

The third pillar of the AI Risk Decisioning approach focuses on automatic recommendations, offering powerful capabilities for real-time and effective risk management. It can automatically monitor transactions and identify trends or anomalies, suggest relevant features for risk models, conduct scenario analyses independently, and recommend the next best action to optimize performance.

For example, in the case of synthetic identity fraud, the AI system quickly learns the unique characteristics of this fraud type as it occurs. It can train a specialized machine learning model with key features to detect synthetic identity fraud, such as anomalies in application data, tracking credit application rates, and flagging high-risk transactions. The system then deploys this advanced model and makes recommendations for adding additional features to the decisioning workflow to detect subtle discrepancies.

Automatic recommendations streamline the process of iterating risk models, which is essential in fraud detection, where finding the right features and trends can be challenging. This reduction in fraud mitigation time from weeks to hours or even minutes significantly enhances the efficiency of risk management and fraud prevention efforts.

Understanding: Human-Understandable Reasoning

The fourth pillar of the AI Risk Decisioning approach emphasizes human-understandable reasoning. This pillar aims to make every decision, recommendation, or insight provided by the AI system easily understandable to human users. It allows risk experts to comprehend the factors influencing risk assessments and provides explanations for decisions made.

By offering a deep understanding of the "why" behind each action or recommendation, this pillar empowers risk experts to spot new patterns, build necessary defenses, and effectively collaborate with broader teams. This transparency fosters confidence and trust, reducing the time required for model iteration.

For example, if there's a 12% increase in chargeback rates and a rise in false positive rates linked to the launch of a new credit card, the AI Risk Decisioning platform conducts a root cause analysis. It provides a rundown of relevant risk factors, such as changes in customer behavior or transaction patterns. This enables experienced risk operators to understand key factors contributing to the situation and generate explanations. The system can also offer proactive recommendations, helping users address the issue strategically.

In essence, human-understandable reasoning elevates risk management from a reactive to a proactive and strategic function by shedding light on the reasoning behind decisions and recommendations.

Guidance: Augmenting Risk Experts

The fifth pillar of the AI Risk Decisioning approach focuses on guidance, with the aim of augmenting the capabilities of risk experts rather than replacing them. The increasing complexity of fraud patterns and the sheer volume of data in digital transactions have left even the most experienced risk experts overwhelmed.

AI Risk Decisioning serves as a valuable co-pilot to risk experts by providing real-time intelligence on ongoing events, conducting specialized root cause analysis, and suggesting relevant features or models that need training. It also offers a contextual understanding of data and explains the factors behind certain trends, enabling better-informed decisions.

For example, when triaging suspicious automated clearing house (ACH) transactions, the traditional manual process involves collecting data, identifying trends from previous cases, and manually investigating potential collusion. AI Risk Decisioning, on the other hand, continuously analyzes transactions, quickly identifies irregularities (e.g., a lack of connection between beneficiary and customer or high-value transactions from unknown sources), and recommends blocking transactions. It also conducts graph analysis on related entities to detect collusion, reducing the need for manual review.

Risk experts can ask the system to explain why a case was formed and make informed decisions based on their business knowledge and understanding of fraud trends.

AI Risk Decisioning empowers risk experts to be more strategic and proactive by providing reliable insights and explanations, making fraud detection more scalable and efficient.

Automation: Risk Automation

The final pillar in the AI Risk Decisioning approach is automation, which simplifies and streamlines the work of risk experts. Risk experts often spend a significant amount of time on repetitive tasks such as monitoring fraud trends and generating performance summaries.

Automating these reporting tasks is possible with generative AI, which is capable of gathering and processing data in the background and producing reports quickly. For example, when compiling monthly reports on performance trends, the traditional process involves manually gathering data and using tools like Excel to create reports, which is time-consuming and tedious. AI Risk Decisioning automates this process. You can simply request it to generate a report on performance trends for the last quarter, and it provides both an overview of the trends and creates the report itself. If the report proves useful, you can ask it to generate similar reports regularly.

Automation is a crucial pillar that enhances risk management by freeing up time and effort spent on repetitive tasks, allowing risk experts to focus on more strategic aspects of their work. These six pillars collectively form the foundation of the AI Risk Decisioning approach, revolutionizing fraud detection and risk management.


With the use of generative AI, AI Risk Decisioning is revolutionizing the field of fraud detection. It can tackle fraud with unmatched precision and agility because of its special combination of technologies and skills. Through the integration of generative AI and conventional machine learning, this methodology provides a holistic resolution to the always-evolving fraud scene.

A thorough grasp of fraud patterns is provided by the knowledge fabric, and real-time adaptation to new risks is ensured by adaptive learning. Anomaly detection and data augmentation improve model performance and lower false positives. By reducing the need for human intervention, AI Risk Decisioning makes fraud detection a proactive and effective procedure.

As the digital world continues to evolve, the battle against fraud must evolve with it. AI-powered solutions like AI Risk Decisioning will play a pivotal role in safeguarding online transactions, protecting both consumers and businesses from the ever-growing threat of fraud.

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