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An Event Study on Stock Market Reactions to Media Disclosures of FinTech Investments: The Case of Korea Cover

An Event Study on Stock Market Reactions to Media Disclosures of FinTech Investments: The Case of Korea

By: Youmi Lee and  Hongjoo Jung  
Open Access
|Dec 2025

Figures & Tables

FinTech and the Value Chain

Value-chain stageInsuranceSecuritiesBanking
1. Product & service developmentIoT-based usage-based insurance (Atzori et al., 2010)Big-data-driven structured products (Sironi, 2016)AI-based personalized financial products (Bose et al., 2021)
2. Marketing & customer managementBig-data-based customer segmentation (Wedel & Kannan, 2016)AI-based churn prediction (Lemmens & Croux, 2006)Chatbots and AI counseling systems (Ngai et al., 2009)
3. Product sales & contract executionBlockchain-based smart contracts (Tapscott & Tapscott, 2017)Online trading automation (Peters & Panayi, 2016)Robo-advisory deployment (Sironi, 2016)
4. Risk assessment & post-underwriting reviewIoT-based risk assessment (Atzori et al., 2010)Big-data-based transaction risk assessment (Fan et al., 2014)AI-based credit screening (Kou et al., 2021)
5. Service delivery & operationsBlockchain-based claims adjudication (Yermack, 2017)AI-driven portfolio management (Sironi, 2016)RPA-based process automation (Brynjolfsson & McAfee, 2017)
6. After-sales management & relationship maintenanceAI-based automated claims review (Davenport & Ronanki, 2018)Behavior-based recommendation systems (Gandomi & Haider, 2015)Sentiment-analysis-driven customer response (Huang & Rust, 2021)
7. Internal support & managementBig-data-driven managerial analytics (George et al., 2014)AI-based internal-control systems (Davenport & Ronanki, 2018)Blockchain-based auditing (Schatsky et al., 2018)

Potential Impacts of FinTech

Artificial IntelligenceBlockchainBig DataInternet of Things
Banking
  • Positive: Automated credit screening; personalized services.

  • Negative: Workforce reductions; algorithmic bias; lack of explainability.

  • Positive: Integrity of transaction records; cost reduction.

  • Negative: Scalability issues; regulatory uncertainty.

  • Positive: Customer segmentation; early risk detection.

  • Negative: Privacy-invasion concerns.

  • Positive: Automation of payment terminals; optimized ATM management.

  • Negative: Security vulnerabilities.

Insurance
  • Positive: Automated new-business underwriting and claims review; fraud detection.

  • Negative: Workforce reductions; potential erosion of customer trust.

  • Positive: Smart-contract-based automation of insurance claims.

  • Negative: Diffusion or ambiguity of legal liability.

  • Positive: Finer risk segmentation; price differentiation.

  • Negative: Potential controversies over premium discrimination.

  • Positive: Risk assessment using driving-behavior and health data.

  • Negative: Potential leakage of sensitive information.

Securities
  • Positive: Robo-advisory; optimization of high-frequency trading.

  • Negative: Heightened risk of market manipulation.

  • Positive: DLT-based innovation in clearing and settlement.

  • Negative: Frictions with incumbent market infrastructure.

  • Positive: Real-time investor analytics; risk forecasting.

  • Negative: Decision errors from over-analysis.

  • Positive: Real-time collection/use of market data.

  • Negative: Low data reliability; rising maintenance costs.

j_irfc-2025-0011_tab_007

Value-chain stageInsuranceSecuritiesBanking
1. Product & service development
  • Analyze market risk factors and design coverage structures aligned with customer needs. This process necessarily requires financial-engineering techniques, risk analysis, and profitability simulations.

  • Technological innovation and customer analytics are key drivers of product development; recently, open banking, ESG finance, and MyData-based personalized products have drawn attention.

  • Analyze market risk factors and design coverage structures aligned with customer needs. This process necessarily requires financial-engineering techniques, risk analysis, and profitability simulations.

  • Technological innovation and customer analytics are key drivers of product development; recently, open banking, ESG finance, and MyData-based personalized products have drawn attention.

  • Analyze market risk factors and design coverage structures aligned with customer needs. This process necessarily requires financial-engineering techniques, risk analysis, and profitability simulations.

  • Technological innovation and customer analytics are key drivers of product development; recently, open banking, ESG finance, and MyData-based personalized products have drawn attention.

2. Marketing & customer managementData-driven marketing and CRM systems are actively utilized.Marketing automation via digital channels and algorithmic investment recommendations are becoming increasingly important.Providing tailored financial solutions along the customer life cycle is a critical strategy; performance hinges on building sophisticated CRM systems through digital channels.
3. Product sales & contract executionThe duty to explain and the suitability principle are legally and ethically material. fintech-based platforms automate this stage.As digital transformation advances, automation and streamlining of contract execution are key tasks.As digital transformation advances, automation and streamlining of contract execution are key tasks.
4. Risk assessment & post-underwriting reviewPost-issuance reviews help prevent intentional risk exposure and moral hazard; use of AI-based underwriting techniques is expanding.Multiple risk factors (market, credit, and liquidity risk) are reflected; guidance from the FSS and BIS regulations are considered.Processes include credit screening, credit scoring, anti-money-laundering (AML), and internal control procedures.
5. Service delivery & operationsOperating efficiency depends on both internal process management and on the quality of IT infrastructure and customer communications.User experience of digital platforms, API connectivity, and fast trade-execution systems are decisive factors for customer satisfaction.Automation technologies such as RPA (robotic process automation) and AI chatbots are core capabilities at this stage.
6. After-sales management & relationship maintenanceMaintaining ongoing relationships after contract inception affects renewal rates and cross-selling. Churn-prediction analytics and AI-based retention strategies are also used. To secure customer loyalty and long-term profitability, firms employ periodic financial counseling, portfolio rebalancing, and event-driven marketing, and other approaches.
7. Internal support & managementGovernance and compliance frameworks are managed at this stage. Talent acquisition and organizational culture, IT infrastructure, and ESG management are closely tied to long-term competitiveness. Digital-transformation strategy, ESG management, and organizational-culture innovation are the main tasks at this stage.

j_irfc-2025-0011_tab_008

EffectStatisticValueFHyp. dfError dfSig.
IndustryPillai’s trace0.0556.2424.000878.0000.000
Wilks’ lambda0.9456.319b4.000876.0000.000
Hotelling’s trace0.0596.3954.000874.0000.000
Roy’s largest root0.05812.834c2.000439.0000.000
FinTechPillai’s trace0.0866.5486.000878.0000.000
Wilks’ lambda0.9166.539b6.000876.0000.000
Hotelling’s trace0.0906.5316.000874.0000.000
Roy’s largest root0.0547.963c3.000439.0000.000
Industry × FinTechPillai’s trace0.1274.95512.000878.0000.000
Wilks’ lambda0.8745.092b12.000876.0000.000
Hotelling’s trace0.1445.22812.000874.0000.000
Roy’s largest root0.13810.081c6.000439.0000.000

Event Day Mean Abnormal Returns

Financial IndustryInsurance IndustrySecurities IndustryBanking Industry

ARCARARCARARCARARCAR
Any Fintech0.0482* (4.447)0.0502* (3.164)0.0611 (1.973)0.0236 (0.650)0.0105 (0.975)0.0244 (1.370)0.1423* (4.873)0.1399* (3.250)
Artificial Intelligence0.0771* (5.043)0.0880* (4.593)0.1140* (2.2021)0.0653 (1.197)0.0315* (2.458)0.0978* (4.664)0.2065* (4.259)0.0685 (1.255)
Blockchain−0.0236 (−0.737)−0.1241* (−2.514)−0.0076 (−0.087)−0.1612 (−1.235)−0.0641* (−2.096)−0.1511* (−2.317)0.0333 (0.954)0.0292 (0.413)
Big Data0.0274 (1.513)0.1066* (2.922)0.0267 (0.698)−0.0123 (−0.315)−0.0089 (−0.397)0.0013 (0.042)0.1055* (2.730)0.4666* (4.501)
Internet of Things−0.0040 (−0.133)−0.1090* (−2.130)0.0651 (0.689)0.2634* (2.171)−0.0257 (−0.666)−0.2093* (−3.758)0.0195 (0.525)−0.0460 (−0.444)

Two-Way ANOVA Results, Event-Day CAR

SourceType III SSdfMSFSig.
Industry2.18021.09011.3690.000
FinTech2.28930.7637.9600.000
Industry × FinTech4.45660.7437.7460.000

Classification of Events

AIBCBDIoTTotal
Insurance301125773
Securities180194731277
Banking5516228101
Total265469446451

Two-Way ANOVA Results, Event-Day AR

SourceType III SSdfMSFSig.
Industry0.60920.3046.2060.002
FinTech0.84030.2805.7080.001
Industry × FinTech0.14860.0250.5020.807
DOI: https://doi.org/10.2478/irfc-2025-0011 | Journal eISSN: 2508-464X | Journal ISSN: 2508-3155
Language: English
Page range: 77 - 93
Submitted on: Oct 25, 2025
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Accepted on: Dec 16, 2025
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Published on: Dec 31, 2025
In partnership with: Paradigm Publishing Services

© 2025 Youmi Lee, Hongjoo Jung, published by International Academy of Financial Consumers
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.