Stay Ahead with AI Insights
Subscribe to our newsletter for expert tips, industry trends, and the latest in AI quality, compliance, and performance— delivered for Financial Services and Fintechs. Straight to your inbox.
AI in Financial Risk Management
Banks want the speed of AI without creating new supervisory problems. The path is clear: pick use cases you can govern, attach tests and monitoring you can prove, and keep the evidence. Avido helps financial institutions apply AI safely across fraud detection, AML, credit, conduct, and operational resilience. We validate structured outputs, retrieval scopes, and compliance adherence through evaluation runs and monitoring. With Avido, banks gain risk-aligned AI that satisfies regulators, supported by full evidence packs, logged monitoring, and versioned documentation for every release.
LLM Guardrails: What to Build and How to Test in 2025
Guardrails define what AI systems can accept, retrieve, and produce. They don’t create safety by existing; they earn it through pressure testing. Avido doesn’t sell guardrails; we test whether yours hold up. Our framework validates input, output, retrieval, and tool controls through adversarial prompts, coverage measurement, and production monitoring. We identify weak filters, overblocking, and policy gaps before they reach users. With Avido, you get evidence-based assurance, clear pass or fail results, mapped test coverage by risk category, and continuous validation of your LLM safety layer.
AI in Finance 2025: Use Cases, Risks and Controls that Scale
Financial institutions face intense pressure to deploy AI, but success depends on pairing valuable use cases with strong controls that satisfy supervisors and build user trust. High-impact examples include retrieval-grounded customer support, fraud detection copilots, compliance summarization tools, and analyst copilots that surface data across systems. Yet these gains come with serious risks: data leakage, bias, hallucinations, and vendor lock-in that threaten both transparency and oversight. Safe adoption requires structured outputs with policy checks, retrieval constraints by role and source, evaluation for accuracy and contradiction, and ongoing monitoring for drift. Scaling responsibly means linking governance to delivery—evaluation before pilots, dashboards shared across product and risk, release gates tied to test results, and metrics reviewed monthly. Firms that treat AI governance as an operational discipline, not an afterthought, gain both speed and regulatory resilience. If you want AI adoption that stands up in review, talk to Avido.
AI Governance in 2025: From Policy to Proof that Works
AI governance isn’t a policy PDF. It’s a living system that keeps your models safe, fair, and compliant. In 2025, boards and regulators expect evidence that your AI behaves as intended—not promises. Modern frameworks like NIST AI RMF (Govern, Map, Measure, Manage), ISO/IEC 42001 for management, and ISO/IEC 23894 for risk give you scalable structure. Use a regulatory crosswalk to align them with local laws. Real governance happens in daily work: evaluation suites linked to releases, production monitoring for drift and policy violations, and reporting that aggregates tests, incidents, and ownership. Every model should have a named RACI, versioned prompts, and clear incident recovery records. Avoid governance theater. Policies without logs, manual spreadsheets, and one-time audits don’t scale. True governance automates proof generation through continuous evaluation, monitoring, and reporting—producing evidence your board and auditors will trust. If you want governance that generates its own proof, talk to Avido.
LLM Guardrails: What to Build and How to Test in 2025
LLM guardrails keep AI safe—but only if they’re tested. Avido doesn’t sell guardrails. We break them to see if they hold. Build guardrails for: Inputs, outputs, retrieval, tools, and observability. Test them by: Attacking filters, measuring coverage, spotting drift, and fixing weak points. Run tests like CI for safety—before release and on schedule. If you want proof your guardrails work, talk to Avido.
OWASP Top 10 for LLM Apps: Controls and Testing 2025
Language-model systems face unique security risks. This guide walks through OWASP’s latest Top 10 for LLMs and shows how to test and monitor controls that prevent prompt injection, data leaks, and unsafe outputs. Build security evidence that stands up to audit.
Detecting RAG Hallucinations: A 2025 Enterprise Guide
Hallucinations make AI unreliable. This piece explains how to detect them in RAG systems through retrieval checks, contradiction testing, and structured QA. It shows how teams can measure accuracy, reduce compliance risk, and keep AI answers grounded in real evidence.
AI Uncertainty: Ambiguity or Knowledge Gap?
When your AI says "I'm uncertain," it might not understand the question, or it might not know the answer—and that distinction changes everything. New research from the University of Arizona reveals how diagnosing these different uncertainty types can reduce escalations by 70% and improve accuracy by 42% in financial services AI, transforming vague confidence scores into precise operational insights that route to the right team with the right fix.
Europe’s AI Energy Crisis: GPUs vs Power Grids
GPUs are booming. Power to run them is not. If Europe hosts 20 percent of advanced AI compute, that is roughly 10 GW continuous demand, about 25 million Danish homes. (Inspired by The Economist.)
GPT-5 Failures Show System Design Matters
Despite the hype around new AI models like GPT-5, recent red-team testing revealed alarming security vulnerabilities—scoring just 2.4% on security and 13.6% on safety measures. For financial institutions, this highlights a critical insight: your AI governance platform matters far more than which foundation model you choose.
Chatbots in Finance: From Search to Money Coach
The financial industry is experiencing a seismic shift in how it interacts with customers. At the heart of this transformation is the evolution of chatbots—from rigid, decision-tree systems to dynamic, generative AI assistants.
Sales Meets Support: Chatbot Best Practices
As financial institutions embrace AI-powered chatbots, the question isn’t just how to automate support—but how to responsibly introduce sales capabilities without undermining customer trust.