Boost your sales with Stripe Radar assistant

Boost your sales with Stripe Radar assistant

Table of Contents:

1. Introduction
2. Fraud Management in Radar 2.1 Machine Learning for Automated Decisions 2.2 Rule Writing for Sophisticated Fraud
3. The Challenges of Rule Writing
4. The Story of Vibhav 4.1 Discovering Fraud Patterns 4.2 The Complex Process of Rule Writing
5. Introducing the Radar Assistant
6. Using Natural Language to Write Rules
7. The Power of Context in Rule Writing
8. Enhancing Fraud Prevention with Radar Assistant
9. Addressing Refund Abuse
10. Leveraging Business Information in Rule Writing
11. Empowering Collaboration with Engineering Teams
12. Conclusion

Using Natural Language to Streamline Fraud Rule Writing with Radar Assistant

Introduction: Fraud management is an essential aspect of any business, and it's often a two-fold process involving machine learning for automated decisions and rule writing for sophisticated fraud. While the core machine learning capabilities of Radar have been effective for most users, some face unique challenges in identifying and responding to rapidly changing fraud patterns. This is where the second part of fraud management, rule writing, becomes crucial.

Fraud Management in Radar: 2.1 Machine Learning for Automated Decisions: Radar leverages machine learning to analyze transaction data and determine the likelihood of fraud. With access to data from the Stripe network, the ML algorithm assesses over a thousand characteristics to make automated decisions in real-time. This helps in allowing genuine transactions while blocking or diverting suspicious traffic for additional security checks.

2.2 Rule Writing for Sophisticated Fraud: While automated decisions cover most fraudulent activities, dealing with more sophisticated fraud requires the ability to identify distinct patterns of fraudulent behavior. Users who notice such patterns need to write rules to block these specific behaviors. However, rule writing is not a one-and-done process, as fraudsters continuously evolve their strategies. It requires continuous iteration and adaptation of rules to stay ahead of fraudsters.

The Challenges of Rule Writing: Writing effective rules to combat fraud is not always easy, as demonstrated by Vibhav, a fraud analyst at Floorence. He faced challenges in translating his insights into rules using the Radar platform. The process involved navigating through extensive documentation, learning specific syntax, and locating the appropriate attributes. This not only consumed valuable time but also required a deep understanding of rule syntax and operators.

The Story of Vibhav: 4.1 Discovering Fraud Patterns: Vibhav, an engineer on the Stripe Radar team and a fraud analyst at Floorence, was tasked with combating fraud on the platform. He noticed a pattern of fraudulent behavior associated with emails ending with 'lolhi' before the domain name. To block this pattern, he attempted to write a rule using the Radar interface but faced challenges in understanding the syntax and finding the right attributes.

4.2 The Complex Process of Rule Writing: Vibhav's struggle to write a rule highlighted the complexity involved in rule writing. It required extensive scrolling through documentation, understanding wildcard characters, and identifying the appropriate operator to match a specific string pattern. This process was time-consuming and not ideal for analysts who are new to rule writing.

Introducing the Radar Assistant: Recognizing the challenges faced by fraud analysts, Stripe introduced the Radar Assistant, an AI-based tool that simplifies the rule writing process. This powerful assistant is trained with public Radar data and can understand natural language queries related to fraud rule writing.

Using Natural Language to Write Rules: The Radar Assistant allows users to write rules using natural language, eliminating the need to learn complex rule syntax. Analysts can simply describe the fraud pattern they want to block, and the assistant provides suggestions for rules based on that description. For example, Vibhav wanted to block emails ending with 'lolhi', and the assistant generated the appropriate rule: "Block If email includes 'lolhi'". The assistant maintains context from one prompt to another, making it easier to refer back to previous rules without repeating them explicitly.

The Power of Context in Rule Writing: The Radar Assistant not only understands natural language but also maintains context, saving valuable time for fraud analysts. Users can refer to previous rules without reiterating them, enabling a smoother rule writing experience. By avoiding the need to repeatedly specify certain conditions, analysts can efficiently generate complex rules that match the desired fraud patterns.

Enhancing Fraud Prevention with Radar Assistant: With the Radar Assistant, complex rule operations like regex pattern matching, handling multiple conditions, and blocking entire lists can be done using natural language descriptions. This empowers fraud analysts to generate rules in minutes, leveraging the insights they have already worked hard to discover. By streamlining the rule writing process, the Radar Assistant enables faster, more efficient fraud prevention.

Addressing Refund Abuse: Refund abuse is another challenge faced by businesses, where customers exploit refund policies by filing fraudulent disputes. Floorence, a popular AI company selling custom rugs, experienced refund abuse on their wool carpets. To tackle this issue, the Radar Assistant suggested using a product type metadata attribute to identify fraud patterns specific to wool carpets. By passing in this attribute, Floorence can pinpoint fraudulent refund attempts and take appropriate action.

Leveraging Business Information in Rule Writing: The Radar Assistant leverages business-specific information to provide tailored suggestions for rule writing. By understanding the unique fraud patterns associated with a business, the assistant can recommend attributes and conditions relevant to the specific context. This enhances the accuracy and effectiveness of rule creation, ensuring that fraud prevention strategies are aligned with the business's needs.

Empowering Collaboration with Engineering Teams: In addition to simplifying rule writing, the Radar Assistant bridges the gap between fraud analysts and engineering teams. It generates code snippets in Python for integrating business information into the fraud prevention system. Analysts can share these code snippets with engineers, facilitating seamless collaboration and efficient implementation of rule changes. This acceleration of manual workflows frees up engineering resources for other critical tasks.

Conclusion: The Radar Assistant revolutionizes the rule writing process for fraud analysts, enabling them to generate complex rules quickly and easily using natural language descriptions. By eliminating the need for extensive knowledge of rule syntax and operators, the assistant saves valuable time and empowers analysts to stay ahead of evolving fraud patterns. With its ability to maintain context, suggest relevant attributes, and facilitate collaboration with engineering teams, the Radar Assistant enhances fraud prevention capabilities and streamlines manual workflows.

Highlights:

• The Radar Assistant simplifies the rule writing process for fraud analysts by allowing them to write rules using natural language descriptions.
• By leveraging business information and maintaining context, the assistant provides tailored suggestions that Align with the specific fraud Patterns faced by a business.
• The Radar Assistant accelerates manual workflows by generating code snippets for integrating business information into the fraud prevention system, fostering collaboration between fraud analysts and engineering teams.
• With the Radar Assistant, complex rule operations, regex pattern matching, and blocking entire lists can now be done using natural language descriptions.
• The Radar Assistant saves valuable time and empowers fraud analysts to efficiently generate rules Based on their hard-earned insights.

FAQ:

Q: Is the Radar Assistant available to all Stripe Radar users? A: The Radar Assistant is currently in private beta and is expected to be available to all Radar for Fraud Teams users later this year.

Q: Does the Radar Assistant only generate rules based on natural language queries? A: Yes, the Radar Assistant uses natural language queries to generate rules. This eliminates the need to learn complex rule syntax and operators.

Q: Can the Radar Assistant handle multiple rule conditions and regex pattern matching? A: Yes, the Radar Assistant can handle multiple rule conditions and perform regex pattern matching. It provides suggestions and code snippets to facilitate complex rule operations.

Q: How does the Radar Assistant leverage business information in rule writing? A: The Radar Assistant analyzes business-specific fraud patterns and provides tailored suggestions based on this information. It recommends attributes and conditions relevant to the specific business context.

Q: Does the Radar Assistant facilitate collaboration between fraud analysts and engineering teams? A: Yes, the Radar Assistant generates code snippets in Python that can be shared with engineering teams. This simplifies the implementation of rule changes and promotes collaboration between analysts and engineers.