デジタルバンキング変革におけるESGと人工知能の統合：インドネシア・ラキヤット銀行に関する質的ケーススタディ
ESG and Artificial Intelligence Integration in Digital Banking Transformation: A Qualitative Case Study of Bank Rakyat Indonesia (原題)
Elsa Manora Simamora
🤖 gxceed AI 要約
日本語
本研究は、インドネシア最大の国有銀行BRIを対象に、ESG原則とAI能力がデジタル変革戦略の中でいかに統合されているかを、2021〜2023年の年次報告書・サステナビリティ報告書・規制文書47件の質的分析により検討する。AIによるデータ基盤がESGガバナンスを強化し、AI支援の与信審査がMSME向けESG社会目標と結びつき、独自AIエコシステムがデータ資産とネットワーク効果を通じて競争優位を生む一方、ESG–AIナラティブは規制当局・投資家・開発機関に対する正当性戦略として機能することを示す。著者はESG–AI戦略収束モデル（EASCM）を提案し、ESGとAI能力を相互補強的に共設計することが競争優位につながる可能性を論じる。
English
This qualitative single-case study examines how Bank Rakyat Indonesia, the country's largest state-owned bank, integrates ESG principles with AI capabilities in its digital transformation strategy, analysing 47 annual, sustainability and regulatory documents from 2021–2023. Four themes emerge: AI-driven data infrastructure strengthens ESG governance and reporting; AI-assisted MSME credit assessment links social ESG objectives to AI deployment; a proprietary AI ecosystem appears to generate competitive advantage via data assets and network effects; and ESG–AI narratives serve as a legitimacy strategy toward regulators, investors and development institutions. The authors propose an ESG–AI Strategic Convergence Model (EASCM), arguing advantage may arise when ESG and AI capabilities are co-designed as mutually reinforcing resources.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGX文脈では、SSBJ基準や有報でのサステナビリティ開示が進む中、AIを用いたESGデータ基盤・ガバナンス強化の実装例として参考になる。特に、開示ナラティブと実態の乖離（グリーンウォッシュ）をどう検証するかという論点は、日本企業の統合報告書・投資家対応を考える上で示唆に富む。新興国国有銀行の事例は、日本のメガバンクや地域金融機関のESG×DX戦略の比較対象としても有用である。
In the global GX context
This study speaks directly to the global AI×ESG intersection: it shows how AI infrastructure can be mobilised for ESG monitoring, reporting and credit assessment, and how such narratives function as legitimacy work toward regulators and investors. For ISSB/CSRD-era disclosure scholarship, it raises the critical question of whether ESG–AI integration described in corporate reports reflects substantive capability or strategic storytelling — a concern equally relevant to TCFD-aligned and transition-finance disclosures. It also adds an emerging-market, state-owned-bank perspective that is largely absent from the predominantly EU/US disclosure literature.
👥 読者別の含意
🔬研究者:AI×ESG統合を資源ベース理論・動的能力理論・制度理論で説明する枠組み（EASCM）と、開示文書をデータとする質的手法の限界を理解する材料になる。
🏢実務担当者:AIによるESGモニタリング・報告・与信審査の統合が、規制当局・投資家・開発機関への正当性確保と競争優位にどう寄与しうるかの実務的示唆を得られる。
🏛政策担当者:国有銀行や新興国金融機関において、ESGとAIの統合を促す規制・開示枠組みが、ナラティブと実態の乖離を生まないよう検証可能性をどう確保するかという論点を提供する。
📄 Abstract（原文）
Sustainability commitments and technological capabilities are increasingly expected to be integrated into a coherent strategic configuration rather than managed as parallel initiatives, particularly within digitally transforming financial institutions, yet limited evidence explains how this occurs in state-owned banks in emerging markets. This study examines how Bank Rakyat Indonesia (BRI), Indonesia’s largest state-owned bank, appears to integrate Environmental, Social, and Governance (ESG) principles with Artificial Intelligence (AI) capabilities within its digital transformation strategy. Using a qualitative single case study design, the study analyses 47 secondary documents — annual reports, sustainability reports, and regulatory publications issued between 2021 and 2023 — through content and thematic analysis guided by the Resource-Based View, Dynamic Capabilities Theory, and Institutional Theory. Because the analysis relies entirely on corporate and regulatory disclosures, the findings are presented as interpretations of these public narratives rather than verified organizational facts. Four interconnected themes emerge: AI-driven data infrastructure strengthens ESG governance through monitoring and reporting; AI-assisted credit assessment for micro, small, and medium enterprises links ESG social objectives with AI implementation; BRI’s proprietary AI ecosystem appears to generate competitive advantage through cumulative data assets and network effects; and ESG–AI narratives function as a legitimacy strategy toward regulators, investors, and development institutions. The study proposes the ESG–AI Strategic Convergence Model (EASCM), suggesting that competitive advantage may emerge when ESG and AI capabilities are co-designed as mutually reinforcing resources. The single-case, document-based design limits generalizability; future research should verify these propositions using primary data and cross-case comparison.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://ojs.uph.edu/index.php/MS/article/download/11214/5938first seen 2026-10-02 05:48:04
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