Tech to Transform

Consensus & Adversarial Panels for Financial Crime Investigations In last two posts we discussed  Supervisor-based agent hierarchies & event-driven orchestrations of multi agent.   Event-driven architectures work well for transaction screening and alert generation. Supervisor-based agent hierarchies work well for deterministic workflows. Financial crime investigations, however, frequently require subjective judgment under regulatory scrutiny. Decisions must be explainable, defensible, and reproducible months or years after the fact. In these scenarios, a single LLM agent is insufficient. Different stakeholders interpret the same facts differently, and regulators expect evidence that competing viewpoints were considered. The Consensus & Adversarial Panel pattern addresses this by running multiple independent agents on identical inputs, aggregating their conclusions mathematically, and applying a critic agent to evaluate the quality of agreement. The Financia...

Event-Driven Agentic AI: A Practical Architecture for Financial-Crime Monitoring Financial crime doesn’t happen in batches. It happens in real time, as a succession of payments, adjustments, and transfers — a continual stream of events that, to a human analyst, tell a story about behaviour. Traditional systems, however, often treat detection as a periodic job: run overnight, review the flagged cases the next day, rinse and repeat. What if instead we treated every transaction as something worth reacting to instantly ? What if our system were built not as a single monolithic workflow, but as a tapestry of independent, autonomous listeners — each attuned to specific signals in the data, capable of reasoning and explanation, and orchestrated not by a rigid workflow but by the very events they observe? This is the promise of event-driven, agentic AI orchestration , and in the Python example we walk through here, you can see how a compact, multi-agent AI pipeline can detect suspicious fas...

Building a Hierarchical Supervisor Team for AI-Driven Financial-Crime Investigations A practical, modular architecture using Python and local LLMs Financial-crime investigations are complex, multi-step processes. Human analysts don’t simply look at a transaction and declare it suspicious—they build context, map behaviour to known typologies, and then make a recommendation. Modern agentic AI mirrors this flow well, but only when the architecture is structured, explainable, and easy to extend. One of the most effective patterns for doing this is the hierarchical supervisor team : a central supervisor orchestrating a set of small, specialized worker agents. In this post, we walk through a complete Python implementation of that pattern, powered by local LLMs from Ollama (e.g., gemma3:4b ) and designed specifically for financial-crime workflows. 1. Why a Hierarchical Supervisor Team? A hierarchical supervisor team is a multi-agent architecture built around three principles: A singl...

Exploring RAGAS: A Framework for evaluating the RAG application quantitatively The RAGAS framework is a cutting-edge approach that combines the strengths of retrieval-based and generation-based methods to produce high-quality outputs. In this blog post, we'll explore the RAGAS framework, its components, and its applications. What is RAGAS? RAGAS (Retrieval-Augmented Generation and Synthesis) is a framework that integrates retrieval and generation capabilities to produce high-quality outputs. It consists of two main components: 1. Retriever: This component is responsible for retrieving relevant information from a knowledge base or database. 2. Generator: This component takes the retrieved information and generates a response or output.

Understanding Answer Relevancy Metrics: A Comprehensive Guide Answer relevancy stands as one of the most critical dimensions of generative AI system performance, yet it remains frequently misunderstood or underutilized in quality assurance frameworks. Unlike metrics that measure factual accuracy or grammatical correctness, answer relevancy specifically addresses whether a generated response actually addresses the user's underlying intent and question. This distinction has profound implications for real-world deployment, particularly in customer-facing applications where irrelevant but well-written answers create significant friction and erode user trust. What Answer Relevancy Actually Measures Answer Relevancy evaluates the alignment between a generated response and the original user query, assessing whether the system stayed focused on addressing the actual question rather than wandering into tangential or off-topic territory. The metric operates on a fundamental principle: a resp...