Data-driven Fraud Detection consists of two different approaches:

• Supervised Learning: Train Machine-Learning models on historical data to detect fraudulent behavior with precision.
• Unsupervised Learning: Identify anomalies, outliers, and unseen fraud patterns using advanced unsupervised techniques.

Different AI models for different fraud detection

Supervised Learning learns from existing patterns (frauds observed in the past). It is based on machine learning models that are capable of detecting fraud with precision.

Unsupervised Learning is needed to detect fraud that has not yet been detected in the past (previously unseen).

Customers use Taipy to build quickly and efficiently highly interactive Fraud detection software covering Supervised and Unsupervised learning.

Building a successful Fraud Detection Application

It may come as a surprise that the difficulty lies not so much in developing these AI models as it is in building a whole application around them.

Intuitive & Powerful User Interfaces

One of the most important factors is the ability to provide Powerful Data Visuals and Interactions. Customized interactive graphical interfaces are crucial to present the data and the results to the end-users.

Transparency

Second, it is essential to supply clear explanations as to why certain transactions are earmarked as Fraud by the ML engine. Taipy applications offer a “white-box” environment that provides insights into the logic behind flagged transactions.

Provide Users with Control

Finally, end-users need control: they must be able to influence. They must be able to influence how the solutions get generated and presented to her/him, for instance:

• Having the capability to prioritize frauds based on different factors: Monetary, Frequency, etc.

• Being able to ‘tune’ the AI models to minimize false positives, for example. Catching 99% of all the actual fraud cases implies that many of the suspected fraud will be false positives, creating a huge backlog for the investigating team of fraud experts.

• Perform What-if analysis using Taipy Scenario Management.

Unsupervised Learning: Finding outliers

When it comes to Unsupervised learning, specific custom graphics need to be provided to highlight outliers and potential frauds. This can be standard graphs (showing a sudden change in customer behavior), charts comparing a customer with its peers, or geographical maps highlighting unusual patterns.

Visualizing social networks can also be an extremely useful tool for detecting rings of fraudsters, etc.

Why Taipy?

The Taipy low-code Python platform is the ideal tool to quickly develop and deploy the next generation of fraud detection applications. Here is a non-exhaustive list of key functionalities provided by Taipy:

Empowering End-Users with Advanced Decision Support SystemsWith Taipy, you’re equipped to quickly build highly interactive Fraud detection applications:

• High level of customization: Every customer has different requirements in terms of fraud detection. Taipy provides a level of customization that most OLAP/BI tools can’t provide.
• Low-Code: Develop and deploy Fraud applications quickly and efficiently.
• Scenario Management: allowing for what-if analysis. This is important in the context of fraud detection since end-users need to ‘test’ the AI models using different options.
• Collaboration: Often, in the context of fraud, end-users need to interact with each other and exchange specific information about a given alleged fraud case. This is totally covered by Taipy which is designed as a collaborative tool.
• Great Integration with the rich AI eco-system: Taipy is pure Python, sharing the same ecosystem as all the standard AI models & libraries. This ensures a complete integration between the Taipy application and the underlying AI layer.

Fraud Alert and Case Management

Concerning the processing and triggering of alerts, Fraud Detection applications need to propose different modes of operations:

• Flag suspicious transactions in real-time, 24/7.
• Priority Management
• Batch-process complex cases for thorough analysis.
• Track alerts, route cases for investigation, and monitor resolution through tailored dashboards or existing management systems.

Taipy can integrate with existing use case management tools (if the customer already has one). Alternatively, a Taipy dashboard can be built to track the status of suspicious cases: progress/resolution/…

Conclusion

Many companies have failed to implement game-changing software using AI technology simply because they were using the wrong tools.

Taipy avoids the limitations of traditional approaches while providing new functionalities essential for Fraud Detection applications:

• Easy customization
• Fast development time thanks to its low-code API
• A collaborative framework ideal for team members' interaction
• Scalable Graphics
• Scenario Analysis
• Support for Large Data
• Support for Structured & Unstructured Data
• Best practices in terms of software engineering (versioning,...) ensure a smooth software evolution over time (new data sources, software updates, etc.).