Field notes on capturing financial definitions, models, policy, precedent and expert judgment—and making that knowledge usable across bankers, investors, risk leaders, insurers, models and agents.
Enterprise AI can now reach a financial institution's data. It still cannot apply the definitions, policy, precedent and judgment that decide what the right answer is for that institution.
By Penomic Research · · 14 minute read
Read the insightCredit policy describes the normal case. The decisions that matter most are exceptions, and the reasoning behind them is the knowledge lenders are most at risk of losing.
By Penomic Research · · 8 minute read
Read the insightAI can now draft a pitch book in minutes. The difference between a draft and a credible recommendation is the bank's house view, and that view is rarely written down.
By Penomic Research · · 7 minute read
Read the insightMost private equity firms have a value-creation playbook. Far fewer can apply it consistently across a portfolio, because the judgment that makes it work sits with a handful of operating partners.
By Penomic Research · · 8 minute read
Read the insightInsurers have automated the easy risks. The referrals that remain depend on underwriting judgment that is difficult to codify and expensive to lose.
By Penomic Research · · 7 minute read
Read the insightAsset managers sell a repeatable process. As research and operations adopt AI, that process has to become explicit enough for machines to follow and for clients to verify.
By Penomic Research · · 7 minute read
Read the insightCommunity banks and credit unions run on deep local knowledge held by a few long-tenured people. Capturing it is a succession question before it is a technology question.
By Penomic Research · · 6 minute read
Read the insightIn private banking hubs such as Switzerland and the Gulf, the value of a relationship manager lies in judgment about suitability, cross-border rules and client context. That judgment is what AI tools most need and least have.
By Penomic Research · · 7 minute read
Read the insightFinding trapped cash is an analysis problem. Deciding which actions management should approve is a judgment problem, and that is where AI support has to go.
By Penomic Research · · 6 minute read
Read the insightMost financial-services AI pilots impress in the demo and stall in production. The usual cause is not the model. It is the missing institutional context that experts add by hand.
By Penomic Research · · 6 minute read
Read the insightExposure, customer, default, liquidity, adjusted EBITDA. The most important numbers in a financial institution have several legitimate definitions. AI makes choosing the right one an engineering requirement.
By Penomic Research · · 6 minute read
Read the insightData has owners. Models have validators. The definitions, rules and precedent that tell AI how a financial institution thinks usually have neither, and that gap is becoming a governance question.
By Penomic Research · · 7 minute read
Read the insightModels can generate. Data platforms can retrieve. Neither can run a firm-specific decision without the institution’s operating context.
By Penomic Research · · 5 minute read
Read the insightA firm’s most valuable financial know-how lives in experts, working files and exceptions—not in a repository waiting to be scanned.
By Penomic Research · · 5 minute read
Read the insightPrompt controls are too narrow for agents that cross systems, interpret policy and produce consequential financial work.
By Penomic Research · · 5 minute read
Read the insight