As consumers increasingly turn to social media to share experiences and seek information about financial products, fraudulent schemes and misleading advertising are spreading faster than traditional supervisory methods can detect. This blog proposes a SupTech solution for the Central Bank of Egypt using web scraping, AI-powered sentiment analysis, and NLP to proactively monitor social media for market conduct violations and consumer risks.

Author: Reham Ali Sharief, Head of Relationships with Licensees, Consumer & Competition Protection, Central Bank of Egypt 

This article represents the author’s personal perspectives based on research conducted during the Innovation Leaders Residency initiative. 

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Executive summary  

The rapid growth of digital financial services and the rise of social media challenge financial authorities in monitoring market conduct and protecting consumers. This research proposes the development of a suptech solution to enhance the Central Bank of Egypt’s (CBE) ability to monitor social media for risk-based consumer protection and market conduct supervision. 

By leveraging web scraping, artificial intelligence (AI) and natural language processing (NLP), suptech helps automate the collection and analysis of consumers’ social media posts and financial institutions’ marketing materials to detect potential legal and regulatory violations. Furthermore, AI-driven sentiment analysis and trend detection will help identify emerging risks such as fraudulent schemes, misleading advertising, and consumer dissatisfaction. Finally, the integration with CBE’s legacy systems will allow to cross-check social media data with other relevant data for consumer protection.  

Drawing on international experiences, this solution will modernize CBE’s financial supervision, ensuring proper market conduct and consumer protection in the digital age. 

Context 

The rapid evolution of financial markets, driven by technological advancements, has led to an increased adoption of digital financial services. While these innovations create new opportunities, they also introduce new risks and amplify existing ones. Therefore, financial authorities must continuously review and enhance consumer protection and market conduct supervisory frameworks to keep pace with market dynamics. 

To effectively implement consumer protection and market conduct principles, financial authorities rely on legal, regulatory and supervisory frameworks. In Egypt, the Banking Sector Law No. 194 of 2020, issued by the CBE, establishes the legal framework for safeguarding the rights and interests of financial consumers. Complementing this law, the Financial Consumer Protection Regulations and Internal Dispute Resolution Regulations set the regulatory and supervisory frameworks to promote transparency and fairness in the financial markets. These instruments empower the CBE to proactively monitor the market, addressing potential risks and ensuring that financial institutions remain accountable to consumers. 

However, traditional supervisory approaches must evolve to address the emerging dominance of digital channels in financial services. Supervising market conduct and protecting consumers require innovative technological solutions to effectively track and identify potentially harmful business practices and illegal activities. Enabling consumers to make informed decisions and protecting their best interests remains top priorities for the CBE, making the development of reliable monitoring systems essential to upholding the integrity of the financial sector. 

Current supervisory approach 

Current supervisory approaches relies only on data from consumer complaints submitted to directly banks or escalated to CBE. The process, though efficient in managing individual cases, spotting the root cause and defining and implementing corrective measures, yet lacks the capacity to timely mitigate potential risks that may take longer to be reported through formal complaints. Thus, it became essential within the evolving market dynamics to leverage an advanced AI-powered solution to automate data collection from of various online platforms, which significantly enhances the ability to proactively monitoring and responding to emerging risks in the financial sector.

Problem

Consumers increasingly rely on social media to seek for information about financial products and services and share their experiences—both positive and negative. However, fraudulent schemes and scams continue to spread unchecked on social media, putting consumers at risk. At the same time, some companies promote financial products with misleading claims or hidden fees, undermining consumer trust and violating transparency standards. Given these risks, robust monitoring systems are essential to track and analyse social media discussions about financial services. As fintech grows and reliance on digital financial services increases, the need for such systems has never been more urgent to protect consumers and uphold industry integrity. 

Global Experiences in Social Media Monitoring 

The emergence of supervisory technology (suptech) has enabled regulators across the world to leverage advanced technologies, such as web scraping and natural language processing (NLP), to monitor and supervise financial markets more effectively. This literature review presents an overview of various countries’ experiences in developing suptech tools, particularly focusing on web scraping and social media monitoring tools designed to enhance market surveillance. 

Financial Conduct Authority – The United Kingdom 

The Financial Conduct Authority (FCA) in the United Kingdom has developed an in-house web scraping tool designed to collect relevant information from the web programmatically. This tool allows users to specify search terms, automating the data-gathering process from a wide range of online sources. Hosted within the FCA’s cloud environment, the tool is developed with flexibility in mind, using Agile and DevOps frameworks for continuous improvement. The functionality of the tool is incrementally expanded to meet emerging regulatory needs, illustrating the FCA’s commitment to adapting its tools to evolving market conditions. This web scraping tool plays a key role in monitoring online content that could affect financial markets, ensuring that relevant data is accessible for analysis and decision-making. 

Otoritas Jasa Keuangan – Indonesia 

In Indonesia, the Financial Services Authority (OJK) has also embraced technology for regulatory purposes with a tool focused on monitoring financial services advertisements. This in-house suptech tool uses a dashboard that flags potential violations of the OJK’s Financial Services Advertising Guidelines. The tool currently processes information sourced from over 80 Indonesian print media outlets, which is manually entered into the system on a quarterly basis. The tool categorizes violations by specific types, such as misleading or inaccurate advertising. However, the OJK is working to enhance this tool by extending data sources to include social media and other online platforms. Moreover, OJK plans to automate data collection, transitioning from manual data entry to real-time data gathering through advanced AI methods, aligning with broader efforts to improve the efficiency and accuracy of financial market oversight. 

Central Bank of Ireland 

The Central Bank of Ireland (CBI) has developed a suptech tool for social media monitoring. In collaboration with external providers, the CBI has implemented a web scraping tool that monitors social media platforms, blogs, and online content in real time. This tool helps identify emerging issues affecting consumers, by tracking mentions of approximately 50 key terms. Real-time monitoring enables the CBI to stay ahead of potential risks or consumer concerns in the financial sector. The use of web scraping techniques for social media surveillance is an important step in the CBI’s broader efforts to modernize market conduct supervision and respond rapidly to emerging trends and issues. 

Autorité des Marchés Financiers – Canada 

Similarly, the Autorité des Marchés Financiers (AMF) in Canada has developed a suptech tool leveraging NLP and topic modelling to process large volumes of data from various sources, including complaints, news, and social media. The tool organizes and prioritizes topics by importance, offering insights into public sentiment and potential risks in the financial markets. This approach allows the AMF to efficiently analyse and categorize vast amounts of unstructured data, enabling a more proactive approach to market conduct supervision. NLP enables the AMF to extract meaningful patterns and trends from complex datasets, enhancing its ability to monitor the dynamic and rapidly evolving financial environment. 

Policy recommendations 

Workflow and procedures  

CBE’s overall workflow for social media monitoring must be well-structured and documented, including the operational procedures that details what data needs to be monitored, how it is gathered, and how it is analysed. Additionally, CBE must consider the required enhancements for a seamless transition into a suptech solution.  

1. Monitoring Scope: 

• Consumer sentiment: tracking consumer posts to identify dissatisfaction or negative sentiment regarding products or services provided by institutions supervised by the CBE. 

• Marketing/advertisement content: tracking advertisements and marketing materials posted on social media related to products or services provided by institutions supervised by the CBE to ensure compliance with CBE regulations and guidelines and detect misleading claims or poor disclosure. 

• Fraudulent schemes: tracking posts related to potential fraudulent schemes, scams, or other forms of market misconduct related to financial products or services. 

2. Data Sources: 

• Social media platforms: Facebook, Twitter, LinkedIn, Instagram, and other platforms used by financial institutions and consumers in Egypt. 

• Web-based platforms: Local media outlets, blogs, forums, and other online sources where financial discussions or advertisements are likely to occur. 

3. Data Collection and Classification Methodology 

Information Required from Social Media 

• Customer posts or reviews about financial institutions, products or services. 

• Marketing/advertisement content (ads, posts, sponsored content) related to financial products or services. 

Classification and Alerts 

Data will be classified based on the potential risk to consumer protection, such as:  

• Low risk (e.g., minor complaints). 

• High risk (e.g., potential fraudulent schemes or scams, potential violations of CBE regulations and guidelines). 

Alerts will be triggered for:  

• Consumer posts or reviews that indicate dissatisfaction but do not indicate potential violations of CBE regulations and guidelines (low risk). 

• Consumer posts or reviews that indicate potential violations of CBE regulations and guidelines (high risk). 

• Consumer posts or reviews that indicate potential fraudulent schemes or scams (high risk). 

• Marketing/advertisement content that includes misleading claims or indicates poor disclosure (low risk). 

• Marketing/advertisement content that indicates potential violations of CBE regulations and guidelines (high risk). 

Integration with Other Data Sources 

Social media data will be cross-checked with official customer complaints and other relevant sources (e.g., CBE reports) to provide context for analysis. 

4. Investigation and Action Plan 

Escalation Process 

When a high-risk issue is identified, CBE Consumer and Competition Protection Department (CCPD) will escalate it to other regulatory authorities, or the institution involved for further action. 

Reporting 

CCPD will generate internal reports about social media monitoring findings to inform the CBE Regulations Department, allowing them to take corrective measures. 

Suptech Solution Design

The suptech solution for social media monitoring should incorporate the use of advanced technologies including web scraping, AI/ML, and NLP to monitor, analyse, and report on consumer risks and market misconduct. 

1. Suptech Stack 

Real-Time Data Collection: 

• The suptech solution will leverage web scraping techniques to gather social media data from the platforms prioritised in the monitoring scope (e.g., Facebook, X, etc.). 

• The solution will process near real-time data from these platforms to capture and analyse posts and comments related to financial products and services.

Cloud-Based Infrastructure: 

• The suptech solution will be cloud-based (e.g., AWS, Azure) for scalability, allowing it to handle large volumes of data from social media platforms. 

AI/ML-Powered Sentiment Analysis and Trend Detection: 

• AI/ML models (e.g., NLP) will be integrated into the suptech solution to analyse consumer sentiment, detect trends, and identify potential risks or violations. 

• Trend detection can automatically flag rising dissatisfaction or misleading ads based on predefined criteria. 

Automated Alerts and Categorization: 

• The suptech solution will automatically categorize content and trigger alerts based on the potential risk to consumer protection as defined in the Classification and Alerts subsection. 

• The alerts will be prioritized based on risk levels (low, high), with high-risk cases triggering immediate actions. 

Data Integration: 

• The solution will be integrated with CBE legacy systems (e.g., customer complaint databases, CBE’s reporting tools) to cross-check social media data with other financial consumer protection data sources. 

2. Development Stages 

Phase 1: Requirements and Architecture Design 

• Define the functional and technical requirements (monitoring scope, alert criteria, etc.). 

• Design the architecture for the system, including cloud infrastructure, data flows, and integration with legacy systems. 

Phase 2: Solution Development 

• Develop the web scraping functionality for real-time data collection. 

• Implement the NLP models to process text. 

• Develop the alert system and categorization features. 

Phase 3: Testing and Feedback 

• Conduct user testing with CBE stakeholders and validate the functionality and usability of the tool. 

• Fine-tune the alert system and classification methods based on feedback. 

Phase 4: Deployment and Integration 

• Deploy the solution on the cloud and ensure integration with the CBE’s legacy systems. 

• Ensure real-time data flow and monitoring capabilities are operational. 

Phase 5: Continuous Improvement 

• Use Agile/DevOps methodologies to implement continuous updates and improvements, including adding new platforms or refining AI models based on new trends and regulatory needs. 

3. Data Governance and Security 

• Data Privacy: Ensure that all collected social media data is handled according to Egyptian data protection laws and international best practices (e.g., GDPR). 

• Security: Implement data encryption and role-based access controls to safeguard sensitive consumer information. 

Conclusion

The global development of suptech tools for monitoring social media and web content illustrates a growing recognition of the importance of technology in financial supervision. Financial authorities in the United Kingdom, Indonesia, Ireland, and Canada have implemented innovative solutions leveraging web scraping and NLP to monitor online content, detect violations, and assess public sentiment. As the regulatory landscape evolves, these tools will likely continue to improve through advancements in data analytics, real-time monitoring, and automation. The experiences of these countries provide valuable insights for other regulators considering similar technological interventions in their market supervision practices. 

As digital financial services and social media continue to influence the development of the financial sector, government agencies must adapt their regulatory and supervisory frameworks to effectively oversee market conduct and protect consumers. While CBE has established a strong foundation through the Banking Sector Law No. 194 of 2020 and complementary regulations, traditional supervisory methods must evolve to address the challenges posed by digital finance. International experiences demonstrate that leveraging suptech solutions for social media monitoring enhances market oversight, strengthens enforcement capabilities, and improves consumer trust in financial markets. 

To operationalize a robust social media monitoring solution, CBE must develop a structured workflow that clearly defines what to monitor, how data is collected and classified, and how supervisory and enforcement actions are triggered. A suptech-driven approach incorporating AI/ML-powered sentiment analysis, trend detection, and automated alerts will allow CBE to proactively identify fraudulent activities, misleading advertising, and consumer dissatisfaction. Integration with CBE’s legacy systems will ensure that insights from social media monitoring contribute to a comprehensive consumer protection and market conduct supervision strategy. 

Moving forward, the successful implementation of this suptech solution will require a phased development approach, starting with requirements definition and system architecture. Subsequent phases will focus on solution development, testing, deployment, and continuous refinement through Agile/DevOps methodologies. By embracing advanced technologies, CBE can enhance its supervisory capabilities, ensure legal and regulatory compliance, and proactively protect financial consumers in an increasingly digitized economy. 

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For further information, we encourage you to read the State of SupTech Report 2025, access session recordings and engage in discussions on GovSpace.io.