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24 GenAI in Banking & Finance: Intelligent Document Processing in Finance - Contracts, Reports, and Beyond

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Introduction The financial services industry stands at a pivotal transformation point where document processing has evolved from manual, error-prone tasks to intelligent, automated workflows. The decommissioning of LIBOR, which officially ended on September 30, 2024, serves as a powerful example of how traditional Natural Language Processing (NLP) approaches struggled with complex, time-sensitive contract analysis. Today's Generative AI-powered Intelligent Document Processing (IDP) represents a quantum leap forward, offering unprecedented capabilities to handle the most challenging document workflows in finance. The LIBOR Transition: A Catalyst for Change When the London Interbank Offered Rate (LIBOR) faced its final cessation after decades of use in an estimated $400 trillion worth of financial contracts, financial institutions worldwide confronted an unprecedented challenge. The transition required combing through millions of legacy contracts to identify LIBOR references, assess ...
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23 GenAI in Banking & Finance: Post 3 100% On-Premise, 100% Open-Source Adverse Media Screening with AI-Driven Risk Insights

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100% On-Premise, 100% Open-Source Adverse Media Screening with AI-Driven Risk Insights  Financial crime detection is rapidly transitioning from traditional anomaly detection toward a holistic risk management approach. This evolution incorporates real-world financial risk factors including geographical inconsistencies, unusual transaction behaviors such as structuring or layering, exposure to high-risk financial products, and counterparty risks involving sanctions and politically exposed persons (PEPs). As outlined in our previous discussion on sanctions screening and fraud detection, enhancing classic anomaly detection with fuzzy sanctions screening and contextual analysis powered by generative AI significantly elevates detection capabilities. This integrated approach allows institutions to uncover complex, evolving risk patterns, accelerate investigations, and maintain stringent regulatory compliance more accurately and efficiently. Building on that foundation, today’s discussion ...
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22 GenAI in Banking & Finance: Post 2 100% On-Premise, 100% Open-Source Sanctions Screening and Fraud Detection Pipeline with AI-Powered Explanations

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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...
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21 GenAI in Banking & Finance: Post 1 Fraud and Risk Management

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...
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20 - Voice of Industry Experts - The Ultimate Guide to Gen AI Evaluation Metrics Part 2

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  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, “...
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19 - Voice of Industry Experts - The Ultimate Guide to Gen AI Evaluation Metrics Part 1

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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.
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18 - Voice of Industry Experts - Leveraging Generative AI to Solve Recurring Data Ecosystem Problems

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Leveraging Generative AI to Solve Recurring Data Ecosystem Problems Leveraging Generative AI to Solve Recurring Data Ecosystem Problems: A Data Practitioner's Perspective Artificial Intelligence (AI) has already started solving some process problems across industries, but it has barely scratched the surface of challenges within data ecosystems. As data practitioners, we see recurring issues throughout data lifecycle that persist regardless of the technology used. While some issues are systemic in nature, others are related to usage and interpretation of data. In this blog, we explore these common problems in data ecosystems, how AI can be leveraged to address them and potential risks involved.
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