Tech to Transform

100% On-Premise, 100% Open-Source Sanctions Screening and Fraud Detection Pipeline with AI-Powered Explanations In the first post of this series, we explored how Generative AI (GenAI) is redefining fraud detection — shifting from static, rule-based filters to dynamic, context-aware intelligence. Today, let’s go deeper with a hands-on example : building a fraud detection & Sanction Screening pipeline that not only detects issues, but also explains them using GenAI. We’ll extend the classic anomaly detection setup by incorporating real-world financial risk checks : Geographical risks (location mismatches) Transaction types (unusual behavior like structuring/cash layering) Product types (cash-intensive or high-risk financial instruments) Counterparty risks ( sanctions/PEP ) Most importantly, we’ll connect this to a live sanctions list — the OFAC Specially Designated Nationals (SDN) List published by the U.S. Treasury — to demonstrate how banks can operationalize co...

How GenAI Is Redefining Fraud Detection  From Rules to Real-Time Reasoning Series Overview GenAI in Banking & Finance – Powered by Your Voice Over the last 20 weeks, I’ve had the privilege of exploring AI/ML, Generative AI, Prompt Engineering, and hands-on experiments with open-source LLMs on #TechToTransform . The response from the community has been incredible — and it’s shaped what comes next. After running a poll  to understand where your curiosity and priorities lie, the message was clear: Fraud & Risk Management led with 53% of the vote Followed by Innovation & Future-Readiness at 28% Then Internal Operations Automation (13%) And Compliance & Governance (6%) With that in mind, I’m launching a new series: “GenAI in Banking & Finance: Fraud and Risk Management” In this series, we’ll dive deep into how Generative AI is transforming the financial world — not just hypothetically, but through real-world applications. From combating fraud to building in...

  The Ultimate Guide to Gen AI Evaluation Metrics In our last post, we unpacked the big picture of Gen AI evaluation — why it matters and the different ways we can look at it. But let’s be honest: knowing that evaluation is important is only half the story. The real challenge is figuring out which metrics actually tell you something useful about your model. That’s where this post comes in. We’re zooming in on the core evaluation metrics every AI practitioner should have in their toolkit . Think of it as your go-to playbook: from the basics like accuracy and precision , to deeper measures such as recall, F1 score, and contextual relevance , all the way to metrics designed for generative AI outputs . By the end, you won’t just recognize these terms — you’ll know when and why to use them, plus how they play out in real-world scenarios like spam detection, search engines, or even evaluating AI-generated answers. If you’ve ever looked at a model’s performance report and thought, “...

The Ultimate Guide to Gen AI Evaluation Metrics As Gen AI users, we are no stranger to the complexities of evaluating generative AI models. With numerous models available, it's crucial to understand which one is performing well and whether the selection is right. But how do you measure success? In this blog, we'll dive into the world of Gen AI evaluation metrics, exploring the various techniques used to assess model performance. We'll cover both general metrics and task-specific ones, providing examples to illustrate each concept.