marvis-risk-agent

MARVIS makes a specific architectural bet: the LLM proposes the plan, but platform code owns the numbers. Ask it for a strategy review and it returns datasets, evidence, models, strategy code, and reports, with KS, AUC, PSI, bad rate, approval rate, profit, and impact computed by fixed kernels instead of guessed by the model. For model validators that single design choice is the difference between a defensible review and a pile of plausible-looking prose. The V2 scope is unusually wide for an open-source risk tool. The seven-step strategy-development workflow runs from current and historical evidence through governed dual-population sampling, univariate and model evidence, trees, Cross, scorecards, Voting, Strategy Pools, impact measurement, validation, and code delivery, ending in four-format review reports. The governed data-to-model path handles ingest and join with match-rate, fan-out, and row-inflation diagnostics, feature engineering with IV/KS/AUC/PSI/Lift/Coverage and binning, multi-recipe training and comparison, PMML export for supported recipes, dataset scoring, and a direct hand-off into model validation. Model validation executes notebooks and compares scores against submitted PMML. Conversational analysis covers VTG-terminal annualized bad rate and profitability and produces an audited Excel report, while Standard Vintage and roll-rate run as separate governed workflows with structured evidence. Excel and Word validation report drafting is included. Two things deserve particular credit. First, Agent mode and the Manual Workbench share the same validated workflows, tools, schemas, and deterministic calculation kernels, so conversational and hands-on analysts don't drift apart or produce divergent results. Second, every governed result carries lineage, data fingerprints, parameters, and audit evidence, which is exactly what a validator needs to trace a submitted model back to its inputs. The honest constraints: this is a local install with no native cloud or web interface, so your team owns setup, runtime dependencies, and upgrades — though the one-click Windows installer bundles Python and Java, which removes the worst of that pain on Windows. It is narrow by design: credit risk and consumer-lending analytics, not general-purpose AI tooling. There's no out-of-the-box real-time credit decisioning, so if you need sub-second scoring on a live application stream you'll be building that layer yourself. And it is not a no-code GUI; useful adoption assumes analysts comfortable with data engineering and model tooling. Teams that want a managed service with a support contract should look at DataRobot or H2O.ai and accept the data-residency trade.