# Feedzai AutoML: The AI\-Powered Fraud Prevention Platform That‘s 50X Faster - Canonical: https://33rdsquare.com/feedzai-automl-a-ml-platform-for-fraud-prevention-thats-50-times-faster/ - Published: 2024-09-03 - Author: Jordan Brown - Categories: [Artificial Intelligence & Machine Learning & ChatGPT](https://33rdsquare.com/category/tech/ai/) --- Fraud is a relentless adversary for banks and financial institutions. As fast as defenses evolve, fraudsters find new ways to exploit weaknesses and circumvent controls. It‘s an arms race with billions of dollars at stake annually. In recent years, machine learning has emerged as a powerful weapon in the fight against fraud. By analyzing massive datasets to identify subtle patterns and anomalies, ML models can detect fraud with far greater speed and accuracy than traditional rules-based systems. But developing these sophisticated ML fraud prevention models has traditionally been an arduous undertaking. Data scientists spend countless hours on laborious feature engineering and model tuning. By the time a model is ready for deployment, the fraud landscape may have already shifted. **This is the pain point Feedzai aimed to solve with the launch of its AutoML platform in 2018.** By automating the most time-consuming aspects of the ML development process, Feedzai AutoML empowers fraud and risk teams to create and deploy models up to 50X faster than before. ## Under the Hood: How Feedzai AutoML Works At a high level, Feedzai AutoML is an end-to-end platform for rapidly building and deploying ML models for fraud detection and risk management. It abstracts away the underlying complexities of feature engineering, algorithm selection, and hyperparameter tuning behind an intuitive UI. But what really sets Feedzai AutoML apart is its advanced semantic-based automated feature engineering. This is the secret sauce that allows the platform to achieve such dramatic efficiency gains. ### The Semantic-Based Approach With traditional feature engineering, data scientists manually create input variables based on their domain knowledge and intuition. It‘s a trial-and-error process to determine which combinations of features will yield the best model performance. Feedzai AutoML takes a fundamentally different approach. Rather than relying on human expertise, the platform leverages machine learning to automatically recognize the intrinsic meaning and relationships in the data. "Our semantic-based system can understand that a certain set of attributes represents a location, a particular ID represents a credit card number, and so on," explained Dr. Pedro Bizarro, Chief Science Officer at Feedzai. "This semantic understanding allows the platform to create features that are highly relevant to the problem at hand." For example, let‘s say we‘re building a credit card fraud model. The raw data might contain fields like merchant ID, transaction amount, location, etc. Feedzai AutoML would automatically recognize that the merchant ID and location could be combined to create a powerful new feature like "average transaction amount for this merchant in this location over the past 30 days." ![Feedzai AutoML Semantic Feature Engineering](https://i.imgur.com/NIq3Yqw.png) _Feedzai AutoML‘s semantic-based feature engineering automatically creates highly relevant features based on relationships in the data. (Source: Feedzai)_ By automatically generating thousands of these rich, problem-specific features, Feedzai AutoML gives models an information advantage from the start. And it does so in a fraction of the time it would take human data scientists to generate a comparable set of features. ### Rapid Model Building Feature engineering is just the first step. With a semantically meaningful feature set in hand, Feedzai AutoML then automates the model building process from start to finish: 1. **Algorithm Selection**: The platform evaluates various ML algorithms (XGBoost, LightGBM, etc.) to determine which is best suited for the dataset and problem type (classification, anomaly detection, etc.). 2. **Hyperparameter Optimization**: Each algorithm has a number of internal hyperparameters that control its behavior during training. Feedzai AutoML uses advanced optimization techniques to efficiently tune these hyperparameters for peak model performance. 3. **Automatic Model Validation**: Once an optimized model has been trained, the platform automatically validates it against holdout data to assess its real-world performance and check for overfitting. 4. **One-Click Deployment**: With the click of a button, the model can be deployed into production, where it can immediately start detecting fraud in real-time streaming data or batch uploads. ![Feedzai AutoML Model Building Process](https://i.imgur.com/T5qXWwP.png) _Feedzai AutoML automates the end-to-end model building process, from feature engineering through deployment. (Source: Feedzai)_ What used to be a marathon is now a sprint. With Feedzai AutoML, models that would have taken weeks or months to build manually can be created in a matter of hours or days. ## Real-World Results Efficiency gains are great, but what really matters is results. How well do models built with Feedzai AutoML actually perform in the real world? The early returns from customers have been nothing short of remarkable. ### Citi: 72% Reduction in False Positives Citi, the global banking powerhouse, was an early adopter of Feedzai AutoML. One of their key challenges was reducing false positives – i.e. legitimate transactions incorrectly flagged as fraudulent. Using Feedzai AutoML, Citi was able to build a new set of highly specialized fraud detection models tailored to different customer segments. After an 8-week head-to-head test between the legacy and Feedzai models, the results were clear: the Feedzai AutoML models reduced false positives by 72% while catching 60% more actual fraud cases. "Feedzai AutoML gave us the ability to rapidly build, test, and deploy new fraud models to address emerging threats," said Yolande Piazza, CEO of Citi FinTech. "The reduction in false positives has been a huge win. It means we‘re bothering customers less while simultaneously stopping more fraud." ### Lloyds: 97% Lift in Fraud Detection, 7X Faster At Lloyds Banking Group, the fraud team had already been using machine learning for several years. But they were curious to see how much Feedzai AutoML could improve on their existing models. The results spoke for themselves. Using the same training data and a similar feature set, the Feedzai AutoML model achieved a 97% lift in fraud detection and a 57% reduction in false positives compared to the bank‘s incumbent model. Most impressively, the Feedzai model was built in just 2 weeks – 7X faster than the 3.5 months it took to build the legacy model. "What impressed us most was the speed," said Michael Harris, Head of Fraud Analytics at Lloyds. "Feedzai AutoML allows us to experiment, iterate, and get new models into production far faster than we ever could before. It‘s been a tremendous force multiplier for our data science efforts." ![Feedzai AutoML Customer Results](https://i.imgur.com/juw2BZW.png) _Customers have seen dramatic improvements in both fraud detection and speed to deployment with Feedzai AutoML. (Source: Feedzai)_ ## The Future of Fraud Prevention As exciting as these early customer successes are, they represent just the beginning of what‘s possible with AutoML in fraud prevention. As the technology continues to mature and evolve, we see several key developments on the horizon: ### Explainable AI One of the biggest challenges with machine learning models is interpretability. Regulators and risk officers need to be able to understand and explain why a model made a particular decision. Feedzai is at the forefront of the push toward explainable AI, developing techniques to automatically generate human-understandable explanations for its AutoML models. ### Deep Learning While tree-based algorithms like XGBoost currently dominate in fraud detection, Feedzai is making major investments in AutoML for deep learning. As computation becomes cheaper and more accessible, there‘s enormous potential to leverage the power of deep neural networks while still maintaining the speed and efficiency benefits of the AutoML approach. ### Expanded Use Cases Fraud prevention is just the tip of the iceberg for Feedzai AutoML. The platform‘s semantic-based approach is highly adaptable to other financial crime use cases like anti-money laundering (AML), know your customer (KYC), and credit risk modeling. We expect to see a significant expansion into these areas in the coming years. ### Bridging the Skills Gap Perhaps most exciting is the potential for AutoML to democratize machine learning and make it accessible to a much wider range of users. By abstracting away the complexities of feature engineering and model tuning, platforms like Feedzai empower subject matter experts to directly leverage ML without needing a data science degree. This has the potential to greatly alleviate the looming skills gap as demand for ML talent continues to outstrip supply. ## Challenges and Limitations For all its promise, it‘s important to recognize that AutoML is not a panacea. As with any automation technology, there are challenges and limitations to consider: ### Data Quality AutoML is not a substitute for good data. The old axiom "garbage in, garbage out" still applies. Models are only as good as the data they‘re trained on, and Feedzai AutoML is no exception. Successful initiatives still require close collaboration between fraud domain experts, data engineers, and data scientists to ensure a high-quality training dataset. ### Overreliance on Automation There‘s a risk of becoming overly reliant on AutoML and losing sight of the importance of human intuition and domain expertise. At the end of the day, models are decision support tools, not decision makers. AutoML should augment and accelerate human expertise, not replace it entirely. ### Regulatory Constraints In heavily regulated industries like banking and finance, there are strict controls and documentation requirements around model development and deployment. While AutoML can dramatically speed up the model building process, it doesn‘t eliminate the need for rigorous testing, validation, and governance. Compliance and risk teams need to be involved from the start to ensure AutoML initiatives meet all necessary regulatory standards. ## The Bottom Line Despite these challenges, the potential of AutoML for fraud prevention is simply too great to ignore. As fraudsters become more sophisticated and the cost of financial crime continues to rise, banks and institutions need every advantage they can get. Platforms like Feedzai AutoML represent a major step forward in the arms race against fraud. By automating the most tedious and time-consuming aspects of machine learning, Feedzai is enabling fraud-fighters to create better models faster than ever before. The results speak for themselves: drastic reductions in false positives, huge lifts in real fraud detection, and models deployed in days instead of months. As the technology continues to evolve and mature, the use cases will only expand. What started as a tool to accelerate fraud model development is quickly becoming an enterprise-wide platform for financial crime prevention and risk management. For fraud and risk leaders, the message is clear. AutoML is here, and it‘s only getting better. Platforms like Feedzai are not just a nice-to-have – they‘re rapidly becoming table stakes for competing in an ever-evolving threat landscape. The question is no longer if you should adopt AutoML, but how quickly you can get started. The future of fraud prevention is automated, and that future is already here. --- Source: [Feedzai AutoML: The AI\-Powered Fraud Prevention Platform That‘s 50X Faster](https://33rdsquare.com/feedzai-automl-a-ml-platform-for-fraud-prevention-thats-50-times-faster/)