Alexander Gude
Statement
Machine learning engineer with 11 years of experience in fraud detection and financial risk at Cash App and Intuit. Built the ACH fraud program that reduced fraud volumes by over 200x and the transaction categorization system that drives loan sizing, benefits eligibility, and compliance for millions of customers.
Experience
2023–Present
2020–2023
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Sized the opportunity and built the anti-fraud controls for instant bank account opening, the primary launch blocker. Worked with compliance and external banking partners to get the program green-lit. Instant bank accounts delivered a 90% increase in paycheck deposits and a 55% increase in revenue from paycheck deposits.
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Built the ACH fraud detection program from the ground up: developed high-recall monitoring metrics, scaled a human review pipeline to thousands of reviews per week, and deployed targeted rules and machine learning models. Reduced fraud volumes by 12x in the first 18 months, and with continual improvements over 200x across five years.
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Briefed the IRS, state tax administrators, and banking partners on the anti-fraud program, securing continued confidence in Cash App’s deposit controls, without which tax refund deposits, 18% of all inflows, could have been redirected away from Cash App accounts.
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Built and own the ACH transaction categorization system for Cash App Banking. Deployed the first ML model, then integrated generative AI, the first GenAI deployment at Cash App, reducing uncategorized transactions by an additional 3x. The system drives lending decisions, benefits eligibility, fraud risk scoring, and compliance workflows for millions of customers. Work submitted as a patent.
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Analyzed deposit limit policies and demonstrated that per-transaction caps were blocking legitimate customers at a rate exceeding 97%, driving a cross-functional initiative to restructure limits that unblocked over $100M in annual deposit volume.
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Built an LLM-powered fraud investigation tool that exceeded human reviewer precision. Automated compliance document generation from code and metrics. Championed AI adoption across the risk organization, presenting at Block’s inaugural AI Summit, building over 20 agent skills for daily fraud analysis, and coaching engineers across multiple teams on integrating LLMs.
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Led 8 data scientists in building machine learning models to detect and stop fraud.
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Drove adoption of engineering best practices by the team, including peer review, automated correctness checking, CI/CD pipelines for code and model deployment, and test coverage metrics, reducing P0 production bugs from 2 in the first year to 0.
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Deployed the first in-product, real-time account takeover prevention model at Intuit—launched in production in TurboTax and alerted security to a possible breach within the first week of running. Back-testing showed it would have detected 95% of last year’s stolen tax return downloads.
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Drove migration of machine learning models from Intuit’s on-prem data center to AWS platform without interruption of services, saving $1.5M per year in operation costs.
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Improved the legacy TurboTax Online account takeover model, leading to a 90% reduction in wrongly challenged users, stopping 10X as many fraudsters, and shortening feature processing time from 2 hours to under a second.
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Led a team of 3 engineers to build a vehicle re-identification system using deep learning within 6 months, enabling clients to automatically detect the same vehicle across multiple security camera feeds. Handed over the system to the customer’s internal development team and provided training.
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Built a system using TensorFlow that embedded images and text into a joint vector space, trained on unlabeled open-source image data. Enabled customers to perform content-based image retrieval on a corpus of 100M untagged images. Published at BMVC 2017.
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Designed and implemented a recommender system evaluation framework in Python and Spark and leveraged it to develop a Python-snippet recommender using word embeddings.
Skills
Python, SQL, Scala, shell script, C++, LaTeX
PyTorch, scikit-learn, XGBoost, Snowflake, dbt, NumPy, Pandas, Matplotlib, git, Linux, vim