RiskFusion
Hybrid risk modeling application that combines traditional numeric credit models with LLM-based text judgment signals to improve underwriting forecast accuracy.
The Problem
“Fuse numeric credit models with text-based judgment signals for safer, faster underwriting”
Organizations face these key challenges:
Manual Oracle Fusion Cloud data retrieval slows underwriting and creates operational bottlenecks
Unstructured evidence such as analyst notes, filings, and OSINT is inconsistently used in decisions
Policy changes require engineering support, delaying response to fraud and credit risk shifts
AI recommendations in lending must not violate hard compliance or internal credit rules
Authentication and product-specific access routing across support and customer portals is fragmented
Risk teams need explainable outputs and evidence trails for model governance and audits
Impact When Solved
The Shift
Human Does
- •Manual data preparation
- •Spreadsheet-based stress testing
- •Periodic governance reviews
Automation
- •Basic logistic regression modeling
- •Rule-based fraud monitoring
Human Does
- •Final approvals of risk models
- •Strategic oversight of risk management
- •Handling complex fraud investigations
AI Handles
- •Advanced ML for credit scoring
- •Anomaly detection for fraud
- •Automated stress testing
- •Generative AI for documentation
Operating Intelligence
How RiskFusion runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
RiskFusion is not allowed to issue a final underwriting approval or decline without an underwriter's decision. [S1][S6]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in RiskFusion implementations:
Key Players
Companies actively working on RiskFusion solutions:
Real-World Use Cases
Programmatic risk data retrieval and management in Oracle Fusion Cloud
Software can automatically read and update risk-management records in Oracle instead of people clicking through screens one by one.
Customer support portal SSO for Finastra product users via Salesforce Community
Customers use one approved account to sign into Finastra’s support site instead of managing separate logins for support access.
Policy and compliance AI for hard-constraint enforcement in loan approvals
Before a loan can be recommended, the system checks bank rules and compliance requirements like a strict checklist, and flags anything that breaks policy.
AI-assisted underwriting research and rule generation with OSINT and natural-language policy creation
Instead of analysts manually searching the web and writing technical rules, AI agents research applicants, summarize risks, and even turn plain-English policy ideas into working detection rules.