ENHANCING DATA PROTECTION IN BANKING: IMPLEMENTATION AND EVALUATION OF AN AI-DRIVEN SYSTEM
| dc.contributor.author | Sugianto, Sugianto | |
| dc.contributor.author | Amin Soetomo, Mohammad Achmad | |
| dc.contributor.author | Ipung, Heru Purnomo | |
| dc.date.accessioned | 2026-04-22T09:32:50Z | |
| dc.date.issued | 2025-02-04 | |
| dc.description.abstract | This research aims to implement an AI-driven system for managing Records of Processing Activities (RoPA) and Data Protection Impact Assessments (DPIA) at Bank XYZ to comply with Indonesia’s Personal Data Protection Law (PDPL). The research uses a qualitative approach, incorporating case studies and interviews to investigate the AI system’s impact on improving compliance, efficiency, and data security within the banking sector. Initial results show significant improvements in the accuracy and speed of processing personal data protection documents. The AI system simplifies the management of RoPA and DPIA and promotes a robust compliance environment by adhering to national and international data protection standards. It is recommended to continually advance AI and provide ongoing training to address emerging data security and privacy challenges. | |
| dc.identifier.uri | https://dspace-repository.sgu.ac.id/handle/123456789/58 | |
| dc.language.iso | en | |
| dc.publisher | Swiss German University | |
| dc.subject | Artificial Intelligence (AI) | |
| dc.subject | Data Protection | |
| dc.subject | Record of Processing Activities (RoPA) | |
| dc.subject | Data Protection Impact Assessment (DPIA) | |
| dc.subject | Personal Data Protection Law (UU PDP) | |
| dc.subject | Compliance | |
| dc.subject | Machine Learning (ML) | |
| dc.subject | Natural Language Processing (NLP) | |
| dc.subject | Data Management Automation | |
| dc.subject | Banking Sector | |
| dc.subject | Regulatory Compliance | |
| dc.subject | Data Security | |
| dc.subject | Operational Efficiency | |
| dc.subject | Risk Mitigation. | |
| dc.title | ENHANCING DATA PROTECTION IN BANKING: IMPLEMENTATION AND EVALUATION OF AN AI-DRIVEN SYSTEM | |
| dc.type | Thesis |
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