Finance & Banking Semantic Ontology Layer¶

The Semantic Ontology Layer sits between the Data Plane and the Application Catalogue, providing a unified, standards-aligned knowledge graph that resolves entities across Core Banking System (CBS), Salesforce Financial Services Cloud (FSC), Loan Origination System (LOS), Market Data feeds, Document Management, and AML/Transaction Monitoring systems.

Industry Standards Alignment¶

The finance & banking ontology is aligned to global regulatory and industry standards.

Basel III/IV Framework¶

The Basel Committee on Banking Supervision framework governs capital adequacy, risk management, and liquidity. Every domain, workflow, and policy in this ontology is tagged with its Basel pillar:

Basel Pillar	Scope	Ontology Coverage
Pillar 1 — Minimum Capital	Credit risk, market risk, operational risk RWA	Credit Risk app, Regulatory Compliance app, Operational Risk app
Pillar 2 — Supervisory Review	ICAAP, stress testing, concentration risk	Credit Risk app, Treasury app, Operational Risk app
Pillar 3 — Market Discipline	Public disclosure, risk reporting	Regulatory Compliance app
Liquidity (LCR/NSFR)	Short-term and structural liquidity	Treasury & Liquidity app

IFRS 9 Financial Instruments¶

Expected Credit Loss (ECL) provisioning entities carry IFRS 9-aligned classifications:

IFRS 9 Concept	Purpose	Ontology Entity
Stage Classification	Impairment staging (1/2/3)	`ECL_Staging.Stage`
PD (Probability of Default)	Forward-looking default probability	`Credit_Score.PD_12M`, `Credit_Score.PD_Lifetime`
LGD (Loss Given Default)	Recovery rate estimation	`Collateral.Recovery_Rate`, `ECL_Staging.LGD`
EAD (Exposure at Default)	Exposure estimation	`Loan_Account.Outstanding`, `ECL_Staging.EAD`
SICR (Significant Increase in Credit Risk)	Stage migration trigger	`ECL_Staging.SICR_Flag`

FATF / AML Standards¶

Financial crime entities are aligned to FATF recommendations and FinCEN requirements:

FATF Standard	Purpose	Ontology Entity
Customer Due Diligence (CDD)	KYC verification and risk rating	`KYC_Document`, `Customer_Master.Risk_Rating`
Suspicious Activity Reporting	SAR/STR filing	`SAR_Filing`
Transaction Monitoring	Anomaly detection	`AML_Alert`, `Transaction_Pattern`
Sanctions Screening	Watchlist matching	`Watchlist_Match`

Architecture¶

graph TD
    subgraph DataPlane [Data Plane - 6 Source Systems]
        CBS["Core Banking System"]
        SFSC["Salesforce FSC"]
        LOS["Loan Origination System"]
        MKT["Market Data & News"]
        DMS["Document Management"]
        AML["AML / Transaction Monitoring"]
    end

    subgraph OntologyLayer [Semantic Ontology Layer]
        SCHEMA["Schema<br/><i>12 Domains · ~50 Tables</i>"]
        WF["Workflows<br/><i>10 Basel/IFRS-aligned</i>"]
        POL["Policies<br/><i>8 Governance Rules</i>"]
        INT["Integrations<br/><i>6 System Mappings</i>"]
    end

    subgraph Apps [Application Catalogue - 8 Apps]
        C360["Customer 360"]
        CREDIT["Credit Risk & EWS"]
        FRAUD["Fraud & AML"]
        REG["Regulatory Compliance"]
        TREASURY["Treasury & Liquidity"]
        LOAN["Loan Lifecycle"]
        REVENUE["Revenue & Product Reco"]
        OPRISK["Operational Risk"]
    end

    CBS --> SCHEMA
    SFSC --> SCHEMA
    LOS --> SCHEMA
    MKT --> SCHEMA
    DMS --> SCHEMA
    AML --> SCHEMA

    SCHEMA --> WF
    POL --> WF
    INT --> SCHEMA

    WF --> C360
    WF --> CREDIT
    WF --> FRAUD
    WF --> REG
    WF --> TREASURY
    WF --> LOAN
    WF --> REVENUE
    WF --> OPRISK

Ontology Components¶

The ontology consists of four interconnected layers, all stored in enterprise-knowledge/:

Layer	Files	Format	Purpose
Schema	`finance-banking-schema.yaml`	YAML	12 domains, ~50 tables with fields, types, risk levels, constraints, and relationships
Workflows	`workflows/*.yaml` (10 files)	YAML	Triggered process flows with steps, rules, SLAs, and dependencies
Policies	`policies/*.md` (8 files)	Markdown	Business rules, thresholds, approval chains, and compliance constraints
Integrations	`integrations/*.yaml` (6 files)	YAML	Field-level sync mappings between source systems with conflict resolution

Relationship Types¶

Entities in the schema are connected via typed relationships that form a knowledge graph:

Relationship	Meaning	Example
`triggers`	Entity event initiates a workflow	`Loan_Account` DPD change triggers `ECL Calculation` workflow
`syncs_to`	Entity data flows to another system	`Customer_Master` syncs_to `SF_Financial_Account` via integration
`constrained_by`	Entity operations governed by a policy	`Loan_Application` constrained_by `lending-policy`
`depends_on`	Entity requires a parent/related entity	`Repayment_Schedule` depends_on `Loan_Account`
`validates`	Entity validates another in a business process	`KYC_Document` validates `Customer_Master` for onboarding

Cross-Application Entity Heatmap¶

Shows how many of the 8 finance apps require each entity. Higher usage = more foundational.

Entity	Count	Applications
`Customer_Master`	8	All apps
`Deposit_Account`	5	Customer 360, Credit Risk, Treasury, Revenue, Operational Risk
`Loan_Account`	5	Credit Risk, Loan Lifecycle, Regulatory Compliance, Customer 360, Revenue
`Transaction_Ledger`	5	Customer 360, Fraud/AML, Treasury, Revenue, Operational Risk
`Credit_Score`	4	Credit Risk, Loan Lifecycle, Customer 360, Revenue
`SF_Financial_Account`	4	Customer 360, Revenue, Loan Lifecycle, Treasury
`Collateral`	3	Credit Risk, Loan Lifecycle, Regulatory Compliance
`AML_Alert`	3	Fraud/AML, Customer 360, Regulatory Compliance
`Capital_Component`	2	Regulatory Compliance, Treasury
`Loss_Event`	2	Operational Risk, Regulatory Compliance
`Yield_Curve`	2	Treasury, Revenue

Customer_Master is the most connected entity in the finance ontology — equivalent to Account in CRM or Material_Master in Supply Chain.

Schema Statistics¶

Metric	Count
Domains	12
Tables	~50
Source Systems	6 (CBS, Salesforce FSC, LOS, Market Data, DMS, AML)
Workflows	10
Policies	8
Integrations	6
Relationship Types	5 (triggers, syncs_to, constrained_by, depends_on, validates)
Basel Pillars Covered	3/3 + Liquidity

Documentation¶

Document	Description
Schema Reference	Complete domain and table reference with fields, types, and relationships
Workflows	All 10 AI workflow definitions with triggers, steps, and dependencies
Policies	All 8 governance policies with rules, thresholds, and approval chains
Integrations	All 6 system integration mappings with field-level detail
App-Object Mapping	Minimum required objects per application with relationship matrix
Mapping Architecture	Schema-first architecture, Semantic RAG pipeline, and ReAct tools

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