Example Scenarios
Real-world walkthroughs showing how to use Infinidatum tools in sequence to make informed AI decisions
Regional Bank: Agentic Underwriting Assistant Decision
For a regional bank deciding whether to build or buy an agentic underwriting assistant in 2025, you can walk through the Infinidatum tools in a sequence that surfaces economics, risk, and feasibility before you commit.
Strategy, Readiness, and Fit
AI Readiness Assessment
Use AI Readiness Assessment to score the bank on Strategy, Technology, Data, People, and Governance; low scores in data quality or governance are strong signals to prefer a controlled vendor solution rather than a bespoke agentic stack.
Agentic AI Use Case Generator
Use Agentic AI Use Case Generator to pull underwriting-specific agentic patterns, ROI estimates, and architecture recommendations, making sure the proposed assistant aligns with credit policy, risk appetite, and regulatory expectations in your jurisdiction.
Compare Agentic vs Non-Agentic Options
Agent vs Traditional AI Comparison
Run Agent vs Traditional AI Comparison to see if underwriting really benefits from multi-step, autonomous agents versus a more constrained decision-support model or advanced rules plus ML, across dimensions like complexity, explainability, and ops risk.
If the comparison shows high complexity and governance overhead for agentic, you can scope the "MVP" as a semi-autonomous assistant (workflow orchestrator + human-in-the-loop) rather than a fully autonomous credit decision maker.
Economics – Cost, ROI, and Benchmarks
AI Cost Estimator
Use AI Cost Estimator to model build vs buy cost components: data engineering, models, infra, integration, monitoring, validation, and ongoing compliance work, with timeline-based projections.
Agentic AI ROI Calculator + AI ROI Benchmark Database
Then use Agentic AI ROI Calculator plus the AI ROI Benchmark Database to get 5‑year cash-flow, NPV, and risk-adjusted ROI ranges, cross-checking your underwriting assistant assumptions against real-world financial services use cases with 2.56×–17× ROI benchmarks.
Build vs Buy Decision and Vendor Shortlisting
Build vs. Buy Framework
Feed those inputs into the Build vs. Buy Framework, using the cost-benefit templates, capability gap analysis, TCO models, and risk matrices to quantify whether a custom agentic underwriting platform beats configurable vendor products over a 3–5 year horizon.
Vendor Evaluation
If leaning toward "buy," use Vendor Evaluation to compare underwriting/decisioning vendors and agentic orchestration platforms on criteria like model control, explainability, regulatory posture, on-prem/virtual private cloud options, and support SLAs.
Stack, Integration, and Governance
AI Technology Stack Selector + AI Integration Complexity Assessment
Use AI Technology Stack Selector and AI Integration Complexity Assessment to choose LLMs, vector DB, orchestration, and monitoring stack that meet banking compliance, and to quantify integration complexity with core banking, LOS, CRM, and data warehouses.
AI Governance Checklist + AI Transformation Roadmap
Run the AI Governance Checklist and AI Transformation Roadmap to ensure model risk management, auditability, and regulatory mapping (e.g., model validation, challenger models, human oversight) are designed into the underwriting assistant from day one.
How to Interpret the Result
Treat all outputs as structured decision artifacts and ranges, not single "answers": the bank's own risk, cost of capital, and regulatory environment should override generic benchmarks.
A reasonable rule of thumb from these tools: favor build if you have high readiness scores, strong in-house ML/engineering, and a differentiated underwriting thesis; favor buy if governance, data, and engineering capacity are moderate and time-to-value plus regulatory comfort are paramount.
Ready to Apply This Process?
Use these tools in sequence to make informed AI decisions for your organization