Fraud detection with machine learning
Machine learning module as an extension to image forensics & rules engine
Specialized
With fraudify, you benefit from a fraud detection system that has been specially developed for the requirements of the insurance industry.
Updates
Through regular updates, the fraudify modules regularly learn through machine learning based on confirmed cases of fraud.
Extension
Fraud detection through machine learning fits seamlessly into the fraudify rules engine and image forensics as an extension.
Expertise
Based on our many years of experience, our team will support you with tried-and-tested rules throughout the entire project phase.
Fraud methods are developing rapidly - traditional systems are reaching their limits. With fraudify's machine learning module, you can detect suspicious activities in real time before they cause damage.
Whether it's insurance fraud through collusion or manipulated claims images, forged transactions or identity theft - fraudsters are using increasingly sophisticated methods to circumvent existing verification processes. Our fraudify machine learning module is constantly learning, discovering new patterns and giving you the decisive edge in the fight against digital fraud.
How machine learning fraud detection works
The fraudify Rules Engine combines efficiency, cost-effectiveness and flexibility - for fraud detection that integrates seamlessly into your processes.
Functionality
The machine learning module supplements fraudify image forensics and the existing fraudify rules engine with a powerful, self-learning component. While the rules engine is based on predefined patterns and rules, the machine learning module goes one step further: it analyzes all available data sources - from image content and metadata to context and process information - and determines a clear prediction, i.e. the probability of a fraud case.
fraudify's machine learning fraud detection is an extension that integrates seamlessly with the existing fraudify Rules Engine and fraudify Image Forensics.
In contrast to the fraudify Rules Engine and image forensics, the machine learning module is not a stand-alone tool. It only unfolds its full strength in combination with the other modules, as it evaluates their results and data in order to make even more precise predictions.
Machine Learning
The major advantage of machine learning is that the module continuously learns from new cases, automatically adapts its models and thus also recognizes previously unknown fraud patterns. This means you are always one step ahead of attackers and receive a well-founded, data-driven assessment that can be seamlessly integrated into your existing inspection process.
Your next step in the fight against fraud
The world of fraud prevention is evolving rapidly - and with fraudify you are well equipped to stay one step ahead. Our machine learning module gives you the tools you need to stop fraudsters before they cause damage. Precise, efficient and seamlessly integrated into your existing systems.
With fraudify, you benefit from a fraud detection system designed specifically for the needs of the insurance industry. Our many years of experience working with leading insurance companies enables us to combine in-depth industry knowledge with state-of-the-art technology.
Do you have questions, want to find out more or get started right away? We are here for you! Let's tackle your challenges together and find out how fraudify can optimize your fraud prevention.
FAQ - Rule-based fraud detection rethought
Machine learning is a branch of artificial intelligence (AI) that enables systems to learn from data and recognize patterns without being explicitly programmed to do so. It is based on:
Data-driven learning: analyzing large amounts of data to recognize patterns.
Regular adaptation: Continuous learning from new data.
Precise predictive ability: decisions based on historical and current data.
Machine learning recognizes suspicious activity accurately and in real time by:
Automatically identifying new fraud patterns.
Analyzing data from multiple sources (e.g. transaction and behavioral data).
Adapting to new fraud methods to stay one step ahead.
Areas of application:
Insurance: Detecting falsified claims and collusion.
Finance: Detection of credit card fraud.
E-commerce: protection against account takeovers and chargeback fraud.
Insurance industry:
Manipulated claims reports: Identifying falsified invoices or repeated submissions.
Collusion: Analyzing networks between insureds to detect fraud collusion.
Other industries:
Credit card fraud: flagging suspicious transactions in real time.
Invoice fraud: Detection of duplicate invoices or discrepant amounts.
Loyalty programs: Identification of points collection or attempted abuse.
The fraudify Machine Learning module is compatible with:
fraudify Rules Engine: Supplements rule-based systems with data analysis and the recognition of unknown fraud patterns.
fraudify Image Forensics: Uses image analysis to detect manipulation even more precisely.
Integration:
Seamlessly into existing systems, whether cloud or on-premises.
No complex customizations required.
Strengthens existing solutions and increases their detection power.
Yes, the machine learning module is fully compatible with the fraudify Rules Engine and fraudify Image Forensics. It complements these modules to ensure holistic fraud detection.
Yes, the machine learning module and all fraudify products are fully GDPR-compliant:
Data anonymization: Protection of sensitive data through anonymization.
Minimized data collection: Processing only necessary data.
Security measures: Protection through state-of-the-art standards.
The module combines machine learning with minimal manual effort:
Learning: Continuous adaptation to new fraud patterns based on your data.
Regular updates: Minimal effort for updates to optimize performance. The importance of regular updating and review plays a central role in the performance of the system and offers decisive advantages in detecting new fraud methods.
Feedback optimization: Adaptation to individual requirements through user feedback.
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