AI Governance Frameworks Compared: NIST, ISO 42001, and the New Financial Services Standard
TL;DR A bank comparing AI governance frameworks is usually trying to answer one question: which one should we adopt first? For most of this audience, the answer is the Financial Services AI Risk Management Framework, since it was built specifically for institutions like theirs. ISO/IEC 42001 matters once a partner or customer asks for independent […]
Data Governance Models: Which One Fits a Bank
TL;DR A bank choosing a data governance model is really deciding who gets to say yes when a new model needs access to customer data, and how fast that yes arrives. The three common structures, centralized, decentralized, and federated, assign that decision right differently. Most banks end up running a blend of more than one. […]
Model Governance in Banking: Ownership, Approval, and Retirement
TL;DR Most banks think they have model governance once a model risk policy exists and a monitoring dashboard tracks drift. Neither answers the three questions an examiner asks: who owns this model, who approved it and every change made to it, and what happens when it comes out of production. A bank without documented answers […]
AI Governance Software: The Criteria That Separate a Platform From a Checkbox Tool
TL;DR Financial institutions shopping for AI governance software usually start with a feature list and end up comparing marketing pages that all use the same five words: explainable, audit-ready, governed, compliant, automated. The words do not tell you which platform tiers a model by risk and which one displays a list of models someone typed […]
AI Governance Solutions for Regulated Financial Institutions: What the Category Covers
TL;DR “AI governance solutions” is a crowded search term because two different things get sold under it. One is software: model inventory, tiering, monitoring, and an audit trail. The other is consulting or policy work: writing the rules for what your institution will and won’t let AI do. Buying one when you meant the other […]
RBI’s device-locking rules for loan recovery: what NBFCs and banks must build before January 2027
TL;DR Most compliance teams are asking the wrong first question about India’s new device-locking rule. The question isn’t whether a lender can restrict a borrower’s phone. It’s whether the loan qualifies at all, because the rule only ever applies to the exact device that loan financed. RBI issued the amendment on 6 August 2026. It […]
Model Serving at Decision Latency: The Layer That Decides If a Model Is Fast Enough
TL;DR A model that performs well in validation can still fail in production for a reason that has nothing to do with its accuracy: it takes too long to answer. A collections model that needs three seconds to score a borrower is not useful for a decision that has to happen the moment a payment […]
MLOps Services for Financial Institutions: The Work That Starts After Deployment
TL;DR A bank that just finished deploying a model usually has a working pipeline, a monitoring dashboard, and a project team about to move on to its next engagement. What it often doesn’t have is anyone whose job continues past that point. MLOps services are the work that keeps happening after deployment: watching for drift, […]
AI Cannot Cure Natural Stupidity
A bank can now deploy an agent that reads a customer record, drafts an offer, sends a message, and schedules a follow-up. That is impressive. But if the underlying decision is wrong, the bank has simply found a faster way to make the same mistake at scale. This is the uncomfortable question behind the race […]
LLMs and Agents are Not Enough
Banks need predictive intelligence to see what happens next. Banks are moving rapidly from experimenting with generative AI to deploying AI agents. The appeal is obvious. Large language models can understand complex information, summarize documents, converse naturally and reason across a growing range of tasks. Agents add another powerful capability: they can act. But there […]
