Operational Risk Intelligence
Dynamic Credit Intelligence
Transform credit management from periodic review to continuous counterparty risk intelligence across customers, exposures, collateral, payment behavior, and market conditions.
Core decision question
Should bp increase, maintain, reduce, or block exposure?
Every signal, context package, evidence pack, policy result, recommendation, human approval, action, replay, and learning artifact should serve this question.
North Star KPIs
Bad debt reductionDSO improvementExposure qualityRisk detection speedCredit decision cycle timeAgent workforce
Credit Intelligence AgentExposure Intelligence AgentCounterparty Risk AgentCollection Intelligence AgentExternal Risk AgentPolicy Intelligence AgentEvidence Intelligence AgentEnterprise integrations
SAP S/4HANAAccounts ReceivableCredit systemsTrade systemsCustomer masterExternal rating agenciesCommodity feedsGeopolitical feedsTreasury systemsFuture UDP context layerMonitored domains
Customer exposureReceivablesPayment behaviorCommodity sensitivityExternal ratingsCountry riskIndustry riskConcentration riskReference signal ticker
Signal visibility must be more than the word signal. Each mission shows reference signal counts, severity, scenario REF-TICK ID, and current blocked/recommended state. These are scenario proof shapes until connected to live traversal.
Reference signal generated from the scenario library; REF-TICK IDs show the expected production trace shape and are not attested live events.
3838 reference signals / scenario
66 high-risk counterparties
ScenarioScenario event REF-TICK-20518 at 14:18
44 credit actions pending
Reference signals and recommended responses
| Signal | Source | Impact | Recommended response |
|---|---|---|---|
| Payment behavior deteriorated | SAP AR | Expected loss +$18M | Reduce limit and require collateral |
| External rating downgraded | Rating feed | Probability of default +9 pts | Escalate to Credit Director |
| Commodity price decline | Market feed | Distributor margin pressure | Trigger exposure review |
Demo sequence
- Customer payment behavior deteriorates
- External rating changes
- Commodity prices fall
- Exposure and expected loss recalculate
- Recommendation generated
- Human reviews and approves
Source-system traceability for major numbers
Every major value is tied to a source system, API object, data product, or source transaction. This answers where the number came from before BP asks.
Context pack: business unit: Group Finance · cost center: CC-1042 · entity code: BP-GROUP · owner: mission runtime.
| Number / metric | Source system or data product | Record / API object | Business context |
|---|---|---|---|
| Expected loss +$18M | SAP AR + credit exposure service | customer=ETIHAD-AIR-442 | Business unit: Aviation |
| External downgrade | Reference ratings feed shape | scenario_rating_event=REF-TICK-20518 | Steward: Credit Risk |
| Treasury exposure | Murex + treasury exposure store | portfolio=TRD-EXP-771 | Owner: Treasury Risk |
Evidence, policy, replay, learning, and action artifacts
Decision memory
| Runtime artifact | Identifier | Credibility proof |
|---|---|---|
| Evidence pack | EP-CR-20518 | data-evidence="pack" · source records and supporting documents |
| Policy ID | CR-LIMIT-2026-09 | Policy runtime gate evaluated before recommendation |
| Replay ID | REPLAY-CR-20518 | Replay available from signal through human judgment and action |
| Learning | patterns learned: 3 | Decision memory updates after accepted/rejected outcome |
| Action payload | Draft credit limit/collateral workflow | Next action: governed execution after approval limit $500K and human override check |
Alternative actions table
Alternative actions table. This is the concrete recommendation surface: bp Sphere shows what it recommends, what it rejected, the expected value, the risk, and the action path.
| Alternative action | Confidence | Expected value / impact | Risk / tradeoff | Action path |
|---|---|---|---|---|
| Maintain limit | 62% | Revenue preserved | $18M expected loss increase | Credit Director accepts risk |
| Reduce limit | 88% | $42M exposure governed | Customer friction | Credit workflow update |
| Require collateral | 91% | $15M unsecured exposure covered | Legal cycle time | Collateral request |
| Block exposure | 76% | Bad debt avoided | Commercial disruption | CFO escalation |
Workshop challenge proof
| Challenge | Visible answer |
|---|---|
| Named SOR proof | SAP AR, Refinitiv ratings, Murex exposure, treasury collateral, and customer master are named in the decision trace. |
| Credit alternatives | Maintain, reduce limit, require collateral, or block exposure are visible with confidence, value, risk, and action path. |
| Human accountability | Credit Director or CFO approval is required before commercial exposure is reduced or blocked. |
Runtime injection console
Use these controls during the workshop to show the mission changing state. Injection creates a named runtime event, updates mission KPI/readiness, adds a replay row, and opens the trace from signal to learning.
Mission KPI
Bad debt reduction
Awaiting scenario injection.
Mission readiness
Baseline
No injected blockers or opportunities yet.
Latest replay event
None
Click an injection below to create a replayable runtime event.
| Time | Event | Scenario | Decision impact |
|---|---|---|---|
| --:--:-- | Waiting | No scenario injected | No mission state change |
Decision portfolio
| Decision | Impact | Owner | Status |
|---|---|---|---|
| Reduce aviation customer limit | $42M exposure governed | Credit Director | Pending approval |
| Require additional collateral | $15M unsecured exposure | Regional Controller | Recommended |
| Trigger collection action | $8.4M overdue | Collections Lead | Draft action |
Vertical operating-model audit
This validates that the mission is useful from frontline work through enterprise steering: Agent Runtime → Analyst → Supervisor → Function Leader/Controller → FB&T Leader → CFO.
| Role layer | What this layer can do | Executive credibility proof |
|---|---|---|
| Agent Runtime | Credit, exposure, counterparty, collections, external risk, policy, and evidence agents monitor risk continuously. | Signal-to-credit-decision trace with exposure, expected loss, policy, and evidence. |
| Analyst | Review customer risk, exposure, signals, and recommended actions. | Can explain why risk changed and what evidence supports the recommendation. |
| Supervisor | Monitor collections, disputes, escalations, and analyst queues. | Can see which cases need escalation and which customers are deteriorating. |
| Function Leader / Credit Manager | Manage portfolio, concentrations, emerging risks, and policy exceptions. | Can see top exposures, concentration risk, and expected-loss movement. |
| FB&T Leader | View enterprise exposure, credit value-at-risk, cash impact, and portfolio risk. | Can connect credit risk to working capital and cash outlook. |
| CFO | Understand credit impact, cash impact, and strategic exposure. | Can decide whether to maintain, reduce, collateralize, or block exposure. |
Executive credibility scenarios
These are the scenarios BP can challenge live. The mission must show signal, context, evidence, policy, decision, action, replay, learning, and enterprise impact for each.
| Scenario | Inject | Expected runtime proof | Action |
|---|---|---|---|
| Customer deterioration | Late payments + external downgrade + commodity weakness | Risk score, exposure, expected loss, recommendation, and human approval update. | |
| New credit request | Large order | Credit evaluation, policy, exposure, decision recommendation, and evidence are shown. | |
| Geopolitical event | Country risk increase | Portfolio exposure, affected customers, and recommended actions update. |
12+ dimension BP credibility audit
| Dimension | Visible proof requirement | This mission's answer |
|---|---|---|
| Signal | Business event or market movement is visible and linked to a mission decision. | Payment behavior deteriorated · Scenario event REF-TICK-20518 at 14:18 |
| Context | Entity, business unit, owner, system object, policy domain, and history are resolved. | Resolved through enterprise context graph: owner, system object, business unit, history, and policy domain. |
| Evidence | Source records, documents, approvals, and source-system snapshots are inspectable. | Evidence pack includes source system record, curated data snapshot, policy reference, and supporting document where applicable. |
| Policy | Authority, control, risk, accounting, tax, treasury, or investment policies are referenced. | Policy/runtime gate is evaluated before recommendation or action. |
| Decision | The mission produces a named decision object, not just a metric or chart. | Reduce aviation customer limit |
| Recommendation | Recommended response and alternatives are shown with impact. | Maintain limit · confidence 62% · impact Revenue preserved |
| Human Override | Human approval, rejection, override, or escalation path is explicit. | Approval, rejection, override reason, and escalation owner are retained in replay. |
| Action | Action payload is traceable to workflow, system update, notification, hold, hedge, or approval. | Pending approval |
| Replay | Event, context, evidence, policy, recommendation, human judgment, action, and outcome can be replayed. | Replay captures signal, context, evidence, policy, agent output, human judgment, action, outcome, and learning. |
| Learning | Accepted/rejected outcomes are captured as future pattern memory. | Outcome becomes pattern memory after human decision and realized impact are known. |
| Business Impact | Financial, risk, cash, control, or value impact is quantified. | $42M exposure governed |
| Enterprise Integration | Systems of record, data products, APIs, and future UDP context layer are named. | SAP S/4HANA · Accounts Receivable · Credit systems · Trade systems · example: SAP AR + credit exposure service |
| Source Drill-down | Users can drill from decision to source transaction, document, or curated data product. | Source drill-down points to SOR/API record, Databricks/UDP data product, document, or market feed behind the decision. |
| Confidence | The decision carries confidence and uncertainty drivers. | Confidence is shown in decision formation and decomposed into data freshness, evidence completeness, policy clarity, and model uncertainty. |
| Alternatives | At least two alternative actions or scenarios are available. | Maintain limit | Reduce limit | Require collateral |
| Audit Trail | Every material step is retained with timestamp, actor/agent, evidence, and policy result. | Timestamped audit trail spans agent, human, policy, evidence, and action events. |
| Executive Narrative | The mission can produce an executive answer: what happened, why, what to do, and expected outcome. | Narrative answers what happened, why, recommended action, value, risk, and next owner. |
| Mission KPI | A mission-level KPI changes when the scenario changes. | Bad debt reduction |
| Readiness | Readiness or confidence updates when blockers, policies, or evidence status change. | Mission readiness/confidence updates when signals, evidence, policies, or blockers change. |
| Enterprise Impact | The mission shows enterprise-level consequence beyond one transaction. | Should bp increase, maintain, reduce, or block exposure? |
Runtime architecture
SignalBusiness event, market movement, policy breach, or performance variance.
ContextEntity, asset, customer, project, policy, historical pattern, and owner resolution.
Evidence + PolicySource records, documents, controls, authority, and data-quality checks.
Decision + ActionAlternatives, confidence, financial impact, owner, action payload, and approval path.
Replay + LearningFull trace, outcome, accepted/rejected recommendation, and pattern memory.