---
title: "Risk Modeling"
description: "2 posts about Risk Modeling, summarised, each linking to the original."
---

# Risk Modeling
> 2 posts about Risk Modeling, summarised, each linking to the original.

## Articles

### [Fine-tuning LLMs for risk assessment](https://yomu.fyi/post/fine-tuning-llms-for-risk-assessment.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Hangzhi Guo
- Published: Oct 9, 2026
- Type: Problem & solution

Stripe makes thousands of near real-time risk decisions every second, balancing fraud detection against false positives that disrupt legitimate businesses. Zero-shot prompting achieved 0.487 precision, domain-guided prompting 0.706, and human reviewers 0.790 at the same recall. The team applied supervised fine-tuning (SFT) to an open-weight 4B-parameter LLM, serializing features like card payments and account activity into text records, training it on labels, and deriving a continuous fraud score from the next-token probabilities normalized over two label tokens. SFT increased average precision by approximately 24% over domain-guided prompting and 70% over zero-shot prompting, and consistently outperformed XGBoost across multiple risk tasks. Linear probing of hidden states showed that SFT made benign signals more linearly decodable, with ROC-AUC improving from 0.583 to 0.642, indicating the model better recognizes legitimate business activity and reduces false positives.


### [Financial Crime Mitigation with MongoDB / Part III: Behavioral Risk Modeling for Event-Driven Fraud Detection](https://yomu.fyi/post/financial-crime-mitigation-with-mongodb-part-iii-behavioral-risk-model.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 21, 2026
- Type: Explainer

MongoDB’s document model and event-driven architecture support behavioral risk modeling for fraud detection in financial institutions. The platform combines rule-based policies with probabilistic reasoning, using change streams for real-time event processing and vector search for similarity assessment. MongoDB captures behavioral events like typing patterns and device IP addresses, aggregating them into dynamic customer profiles. Vector search compares incoming transactions against historical patterns, but it does not evaluate numerical data such as transaction amount, which requires traditional risk scoring. This approach reduces false positives and lowers total cost of ownership by minimizing manual intervention, while MongoDB’s flexible schema allows adding risk factors without downtime.