Fraud detection in klarna
•
1 like•1,762 views
Klarna uses machine learning models to detect fraud in online transactions. They generate features from transactional data, customer profiles, merchants, and social graphs. Imbalanced class distributions are addressed using oversampling and undersampling techniques. Models include supervised algorithms like XGBoost and LightGBM as well as anomaly detection and ensemble meta models. Models are trained on AWS Sagemaker and deployed via Docker containers on Kubernetes clusters, which currently handle over 200 models. Key metrics include precision-recall curves and cost-based metrics to optimize for business goals. Production systems are monitored for performance, features, model metrics, and estimated business impacts.
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Fraud detection in klarna
Similar to Fraud detection in klarna (20)
Recently uploaded
Recently uploaded (20)
Fraud detection in klarna
- 1. Fraud Detection in Klarna Vaibhav Singh Data Science Manager
- 2. Consumer: Risk-free purchase Klarna Buyer Protection Policy Customers get their goods or they get their money back. Merchant: Risk-free selling When you sell with Klarna you will always get paid for the order. Klarna takes all the fraud and credit risk. Klarna has helped make e-commerce safer for 14 years. Merchants receive guaranteed payouts* Consumers have never had to pay for an item that has not arrived, was wrong or broken. Discover, Shop, Buy, Keep. Repeat. When asked, 90% of customers consider Buyer protection as attractive
- 3. Klarna Payment Methods Payments built around customers. Every Klarna payment method used to settle the bill directly Every Klarna payment method used to settle the bill a period after Every Klarna payment method used to part pay the order Pay now Pay later Slice it
- 4. Unicorns are real. And other facts. Klarna isn’t just a great place to work: it’s one of the world’s true unicorn companies. ● We’re valued at over $2.5bn and we’re growing by 40% a year ● We now have more than 2500 employees from more than 70 countries ● We process more than a million transactions a day ● About 10,000 people are buying with Klarna at any moment of any day ● We have developer centres in Stockholm, Berlin, and Linden
- 6. 1st Party 2nd Party 3rd Party An individual, or group of people, misrepresent their identity or give false information Money mules, is where an individual knowingly gives their identity or personal information to another individual to commit fraud. An individual, or group of people, use another person’s identity or personal details to open or takeover an account without the consent, or knowledge, of the person whose identity is being used
- 8. ● ID-thefts ● Credit Card fraud ● Abusive behavior ● Borrowed ID ● Loopholes in Klarna or merchant processes
- 10. 1. Human Agents 2. Dispute from customers 3. Chargebacks 4. Other signals eg. Bounce Emails, Merchant Defining the target
- 11. 1. Oversampling - SMOTE 2. Undersampling 3. Work out which class distribution works out best on validation set Class Imbalance
- 12. 1. Transactional features a. Shopping cart details b. Billing, Shipping details 2. Customer based a. Frequency of purchases b. Frequency of payments 3. Merchant based a. Current default rates b. Risky items 4. Graph based a. How is the transaction linked to other fraud transactions b. How is the customer linked to other fraudulent customers Features
- 13. Feature Generation Architecture Authorization Events on cid on email ... ... RaaS Requests Variable Calculation Variable Calculationon cid on email ... S3 EMR (Spark Structured Streaming) Dynamo Streams Storing point-in-time datasets Authorization Events RaaS Requests CID Datasets Email DataSets S3
- 14. 1. Supervised a. Algos - XGBoost, LightGBM 2. Anomaly Detection a. Autoencoders - Keras Based 3. Meta Models a. Ensemble of models for similar countries or merchants Models
- 15. 1. AWS Sagemaker a. Custom Images used to train models 2. Track experiments, models, real time decisions 3. Feature Importance and explainability a. Do not solely rely on decision trees for feature importance b. Use SHAP/ELI5 for better explainability 4. Production deployment a. Models hosted as Docker images with API endpoints b. Kubernetes Cluster i. Currently handling more than 200 models Tech behind the scenes
- 16. 1. Precision Recall - Area Under Curve (PR AUC vs ROC AUC) 2. Cost ($) based metric best for business Choose your METRICS wisely
- 18. Monitoring in production ● Things to monitor ○ Live System Performance ○ Features ○ Model metrics e.g. Precision, Recall ■ Estimation using random sampling and using Humans as ground truth ○ Business Metrics (Cost of TP, FP, FN) ■ Estimation using random sampling and extrapolating to overall population