{
  "id": "AIBD-0492",
  "title": "Sustainable Financing Partnership",
  "summary": "MBRHE reports that AI credit and eligibility models automate housing-loan assessment and remove manual approval bottlenecks. AI’s separate effect is unmeasured.",
  "date_observed": "2025-06-01",
  "date_basis": "source information",
  "date_precision": "month",
  "benefit_status": "reported",
  "source_collection": "ITU AI for Good: Innovate for Impact (2025)",
  "source_entry": "ITU-109",
  "account": "MBRHE reports that AI models evaluate housing-loan eligibility and priority using credit, income and social criteria. It identifies logistic regression, decision trees and ensemble models trained with historical approvals and credit-bureau data. The models are described as removing manual approval bottlenecks within a service integrated with Emirates Islamic Bank. Full deployment is dated to Q1 2025. Bank financing, interest subsidies, APIs and digital identity also contribute to the overall program. Its loan volume, budget savings and workload totals do not isolate the models’ contribution.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "Reduce manual work and delays in housing-loan eligibility assessment.",
  "system": "MBRHE AI housing-loan credit and eligibility assessment",
  "model_version": null,
  "developer": null,
  "deployer": "Mohammed Bin Rashid Housing Establishment and Emirates Islamic Bank",
  "location": "Dubai, United Arab Emirates",
  "domain": "Finance",
  "segment": null,
  "benefit_type": "Reduced Task Time",
  "taxonomy_note": null,
  "registry_ids": {},
  "ratings": [
    {
      "group": "organizational",
      "depth": 1,
      "reason": "MBRHE reports that AI automates credit and eligibility evaluation and removes manual approval bottlenecks. AI’s share of the wider program’s reported 30% workload reduction is unknown."
    },
    {
      "group": "personal",
      "depth": 1,
      "reason": "MBRHE reports that automated eligibility evaluation helps applicants move through approval more quickly. Changes in household welfare attributable to the models are unmeasured."
    }
  ],
  "ai_role": {
    "status": "assessed",
    "statement": "Trained credit and social-vulnerability models automate eligibility assessment. MBRHE reports fewer manual approval bottlenecks. AI’s effect is not separated from financing and workflow changes."
  },
  "evidence": [
    {
      "kind": "report",
      "citation": "Project account in International Telecommunication Union, AI for Good: Innovate for Impact, 2025. Information current as of June 2025. Outcomes are reported by the project team. The account provides no independent outcome evaluation.",
      "title": "Use Case 8 Sustainable Financing Partnership",
      "url": "https://www.itu.int/epublications/publication/ai-for-good-innovate-for-impact#_idParaDest-665",
      "date": null,
      "written_by": "makers",
      "quote": null
    }
  ],
  "replication": {
    "produced_by": [
      "Credit-risk and eligibility models integrated into housing-loan assessment"
    ],
    "must_hold": [
      "Historical approvals, credit-bureau information, income and social-eligibility data",
      "Bank-system APIs and digital identity access integrated with the eligibility workflow"
    ],
    "echoes": []
  },
  "paired_incident": null,
  "headline": "AI assesses housing-loan eligibility in Dubai",
  "metric": null,
  "comparator": null,
  "later": null,
  "value": null,
  "developer_country": null,
  "deployer_country": null,
  "unrated_because": null,
  "brief_notes": null,
  "brief": null,
  "revisions": [
    {
      "date": "2026-10-22",
      "change": "Added the case.",
      "source": "https://www.itu.int/epublications/publication/ai-for-good-innovate-for-impact#_idParaDest-665",
      "editor": "AIBD"
    }
  ],
  "confidence": "claimed",
  "shade": "low",
  "claim_status": "The makers' claim",
  "claim_basis": "Sources: the makers' report. The cited evidence includes no independent source and no finding by a regulator or court.",
  "profile": {
    "case_id": "AIBD-0492",
    "catalog_version": "1.0.0",
    "source_fingerprint": "c64870f27169a31519927120b345b002ab5121d8836fcc35f4be679e35dad1a6",
    "collection_refs": [
      {
        "collection_id": "aibd",
        "version": "snapshot-8bf7cfec9cb185aa"
      },
      {
        "collection_id": "aibd",
        "version": "2026-10-25"
      }
    ],
    "source_ids": [
      "AIBD-0492-source-0001"
    ],
    "families": {
      "strategic assessments": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      },
      "evaluations": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      },
      "monetary observations": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      },
      "institutional decisions": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      }
    },
    "fields": {
      "actors[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "deployer": "Mohammed Bin Rashid Housing Establishment and Emirates Islamic Bank"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "deployer"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "actors[].ownership_control": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "ai_contribution": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ai_role": {
              "status": "assessed",
              "statement": "Trained credit and social-vulnerability models automate eligibility assessment. MBRHE reports fewer manual approval bottlenecks. AI’s effect is not separated from financing and workflow changes."
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ai_role"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "ai_relationship": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "category": "general benefit",
            "ai_role": {
              "status": "assessed",
              "statement": "Trained credit and social-vulnerability models automate eligibility assessment. MBRHE reports fewer manual approval bottlenecks. AI’s effect is not separated from financing and workflow changes."
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ai_role",
            "category"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].alternatives": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].assessor_date": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].depth": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "organizational",
                "depth": 1,
                "reason": "MBRHE reports that AI automates credit and eligibility evaluation and removes manual approval bottlenecks. AI’s share of the wider program’s reported 30% workload reduction is unknown."
              },
              {
                "group": "personal",
                "depth": 1,
                "reason": "MBRHE reports that automated eligibility evaluation helps applicants move through approval more quickly. Changes in household welfare attributable to the models are unmeasured."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ratings"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].display_confidence": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "confidence": "low",
            "evidence_word": "claimed",
            "claim_status": "The makers' claim",
            "basis": "Sources: the makers' report. The cited evidence includes no independent source and no finding by a regulator or court.",
            "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review."
          }
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Derived from the existing case display properties; not a new evidentiary judgment.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].rationale": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "organizational",
                "depth": 1,
                "reason": "MBRHE reports that AI automates credit and eligibility evaluation and removes manual approval bottlenecks. AI’s share of the wider program’s reported 30% workload reduction is unknown."
              },
              {
                "group": "personal",
                "depth": 1,
                "reason": "MBRHE reports that automated eligibility evaluation helps applicants move through approval more quickly. Changes in household welfare attributable to the models are unmeasured."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ratings"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].rubric_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].status_basis": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Reported benefit"
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Mapped from the existing case description without a new external source review.",
          "record_fields": [
            "benefit_status"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].target": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "organizational",
                "depth": 1,
                "reason": "MBRHE reports that AI automates credit and eligibility evaluation and removes manual approval bottlenecks. AI’s share of the wider program’s reported 30% workload reduction is unknown."
              },
              {
                "group": "personal",
                "depth": 1,
                "reason": "MBRHE reports that automated eligibility evaluation helps applicants move through approval more quickly. Changes in household welfare attributable to the models are unmeasured."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ratings"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "availability": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "brief": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "case_kind": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "case_links[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].evidence_links": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].evidence_strength": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].missingness": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].proposition": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "cross_border_flows[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "decisions[].action_outcome": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].grounds": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].institution_task": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].success_criterion": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "deployment_stage": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "economics[].amount": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].costs[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].counterfactual": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].currency_price_basis": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].gross_net": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].measure_type": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].period_unit": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].realization": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].recipient_payer": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].return_payback": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].valuation_method": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "evaluations[].ai_uplift": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].comparator": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].design": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].effect": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].exposure": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].id": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].measurement_method": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].metric": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].reproduction_materials": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].setting_sample": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].uncertainty_limitations": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "external_references[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "source_collection": "ITU AI for Good: Innovate for Impact (2025)",
            "source_entry": "ITU-109"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "source_collection",
            "source_entry"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "follow_up[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "human_oversight": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "id": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "id": "AIBD-0492"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "id"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "infrastructure_dependencies[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].beneficiaries": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].beneficiary_count": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].benefit_type": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "benefit_type": "Reduced Task Time"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "benefit_type"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].claim": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "MBRHE reports that AI models evaluate housing-loan eligibility and priority using credit, income and social criteria. It identifies logistic regression, decision trees and ensemble models trained with historical approvals and credit-bureau data. The models are described as removing manual approval bottlenecks within a service integrated with Emirates Islamic Bank. Full deployment is dated to Q1 2025. Bank financing, interest subsidies, APIs and digital identity also contribute to the overall program. Its loan volume, budget savings and workload totals do not isolate the models’ contribution.",
            "benefit_type": "Reduced Task Time"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "account",
            "benefit_type"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].distribution": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].duration": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].id": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].stakeholder_group": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "organizational",
                "depth": 1,
                "reason": "MBRHE reports that AI automates credit and eligibility evaluation and removes manual approval bottlenecks. AI’s share of the wider program’s reported 30% workload reduction is unknown."
              },
              {
                "group": "personal",
                "depth": 1,
                "reason": "MBRHE reports that automated eligibility evaluation helps applicants move through approval more quickly. Changes in household welfare attributable to the models are unmeasured."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ratings"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].status": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "benefit_status": "reported"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "benefit_status"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].tradeoffs": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].unexpected": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "unexpected": false,
            "purpose": "Reduce manual work and delays in housing-loan eligibility assessment."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "unexpected",
            "purpose"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "places[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "location": "Dubai, United Arab Emirates"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "location"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "purpose": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "purpose": "Reduce manual work and delays in housing-loan eligibility assessment."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "purpose"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "record_dates": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "date_observed": "2025-06-01",
            "date_basis": "source information",
            "date_precision": "month",
            "revisions": [
              {
                "date": "2026-10-22",
                "change": "Added the case.",
                "source": "https://www.itu.int/epublications/publication/ai-for-good-innovate-for-impact#_idParaDest-665",
                "editor": "AIBD"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "date_observed",
            "date_basis",
            "date_precision",
            "revisions"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "replication.attempts[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "replication.conditions": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "replication": {
              "produced_by": [
                "Credit-risk and eligibility models integrated into housing-loan assessment"
              ],
              "must_hold": [
                "Historical approvals, credit-bureau information, income and social-eligibility data",
                "Bank-system APIs and digital identity access integrated with the eligibility workflow"
              ],
              "echoes": []
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "revision_history[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "revisions": [
              {
                "date": "2026-10-22",
                "change": "Added the case.",
                "source": "https://www.itu.int/epublications/publication/ai-for-good-innovate-for-impact#_idParaDest-665",
                "editor": "AIBD"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "revisions"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sector": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "domain": "Finance"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "domain"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].access_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "sources[].citation_locator": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "evidence": [
              {
                "kind": "report",
                "citation": "Project account in International Telecommunication Union, AI for Good: Innovate for Impact, 2025. Information current as of June 2025. Outcomes are reported by the project team. The account provides no independent outcome evaluation.",
                "title": "Use Case 8 Sustainable Financing Partnership",
                "url": "https://www.itu.int/epublications/publication/ai-for-good-innovate-for-impact#_idParaDest-665",
                "written_by": "makers"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "evidence"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].id": {
        "state": "value_present",
        "value": {
          "kind": "identifiers",
          "identifiers": [
            "AIBD-0492-source-0001"
          ],
          "role": "Existing evidence items, in their original order"
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Stable within the fingerprinted record version; no source content or source review has been added.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
      "sources[].language": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "sources[].relationship": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "evidence": [
              {
                "kind": "report",
                "citation": "Project account in International Telecommunication Union, AI for Good: Innovate for Impact, 2025. Information current as of June 2025. Outcomes are reported by the project team. The account provides no independent outcome evaluation.",
                "title": "Use Case 8 Sustainable Financing Partnership",
                "url": "https://www.itu.int/epublications/publication/ai-for-good-innovate-for-impact#_idParaDest-665",
                "written_by": "makers"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "evidence"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].upstream_sources": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "strategic_assessments[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "strategic assessments"
      },
      "summary": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "summary": "MBRHE reports that AI credit and eligibility models automate housing-loan assessment and remove manual approval bottlenecks. AI’s separate effect is unmeasured.",
            "account": "MBRHE reports that AI models evaluate housing-loan eligibility and priority using credit, income and social criteria. It identifies logistic regression, decision trees and ensemble models trained with historical approvals and credit-bureau data. The models are described as removing manual approval bottlenecks within a service integrated with Emirates Islamic Bank. Full deployment is dated to Q1 2025. Bank financing, interest subsidies, APIs and digital identity also contribute to the overall program. Its loan volume, budget savings and workload totals do not isolate the models’ contribution."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "summary",
            "account"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].autonomy_permissions": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].component_role": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].deployment_configuration": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].inputs_outputs": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_name": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].product_name": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "system": "MBRHE AI housing-loan credit and eligibility assessment"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "system"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].provider": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "taxonomy_crosswalks[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "title": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "title": "Sustainable Financing Partnership",
            "headline": "AI assesses housing-loan eligibility in Dubai"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "title",
            "headline"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "version": {
        "state": "value_present",
        "value": {
          "kind": "identifiers",
          "identifiers": [
            "c64870f27169a31519927120b345b002ab5121d8836fcc35f4be679e35dad1a6"
          ],
          "role": "SHA-256 of normalized original case data"
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Deterministic content version assigned by the local migration.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
      "workflow": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "MBRHE reports that AI models evaluate housing-loan eligibility and priority using credit, income and social criteria. It identifies logistic regression, decision trees and ensemble models trained with historical approvals and credit-bureau data. The models are described as removing manual approval bottlenecks within a service integrated with Emirates Islamic Bank. Full deployment is dated to Q1 2025. Bank financing, interest subsidies, APIs and digital identity also contribute to the overall program. Its loan volume, budget savings and workload totals do not isolate the models’ contribution.",
            "replication": {
              "produced_by": [
                "Credit-risk and eligibility models integrated into housing-loan assessment"
              ],
              "must_hold": [
                "Historical approvals, credit-bureau information, income and social-eligibility data",
                "Bank-system APIs and digital identity access integrated with the eligibility workflow"
              ],
              "echoes": []
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "account",
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      }
    },
    "summary": {
      "total": 91,
      "addressed": 91,
      "known": 29,
      "reviewed": 0,
      "record_mapped": 29,
      "not_assessed": 62,
      "complete": false,
      "by_state": {
        "value_present": 29,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 62,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}