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What is the Lamfalussy approach in EU financial regulation?
Level 1, where the European Parliament and Council adopt general laws proposed by the Commission
Level 2, where financial experts develop detailed technical rules under the Commission's guidance.
→ This allows for quicker and more flexible updates to complex regulations.
what is ESMA
is an independent EU authority whose purpose is to improve investor protection and promote stable, orderly financial markets.
What are the main pillars of financial regulation that can reduce information asymmetry, moral hazard, adverse selection and financial contagion?
Transparency requirements to prevent information asymmetry
Capital requirements to mitigate moral hazard
Authorization to offer financial services to avoid adverse selection
Consumer protection to address information asymmetry
Systemic risk oversight to prevent financial contagion
what are SupTechs?
Supervisory Technology → used by financial regulators and supervisory authorities to enhance the efficiency and effectiveness of their oversight of the financial sector.
what are RegTechs?
Regulatory Technology → refers to the use of technology, particularly software and big data analytics, to help financial institutions comply with regulations efficiently and at a lower cost.
What is the role of RegTech and SupTech in financial regulation?
help reduce information asymmetries by giving supervisors better, faster, and more detailed data to monitor financial institutions. This means:
- Supervisors can spot risks or bad behavior earlier (thanks to real-time monitoring and AI tools)
- Firms are more likely to follow rules since they know they're being closely watched
- Investors and the public benefit from safer and more transparent financial markets
What does the Markets in Crypto-Assets Regulation (MiCA) aim to achieve?
establishes uniform rules for crypto-assets that are not already covered by existing financial services legislation
foster innovation
enhance market integrity, financial stability, and consumer protection
transparency, disclosure, authorisation and supervision requirements for issuers and service providers across the European Economic Area (EEA)
What is DORA and its significance in the financial sector?
creates a harmonized framework to strengthen the ICT security and operational resilience of financial entities. It ensures the EU’s financial sector can withstand, contain and recover from severe digital disruptions, including cyber-attacks
What are some benefits and challenges of using Natural Language Processing (NLP) in finance?
Helps reduce information gaps and increases transparency
Makes monitoring faster and more accurate
Identifies if firms leave out required information
Reading PDFs is hard for machines; better to use machine-readable formats like XHTML
More data and smarter tools need more computing power
What is the purpose of financial firms reporting transaction data?
to help reduce information gaps but if the data is poor or inconsistent it can create new problems instead of solving them
ESMA uses AI to detect errors and unusual trading patterns in financial data, which helps avoid big risks and improves market safety.
What can be problematic in credit rating agencies (CRAs) by ESMA?
to prevent issues like over-reliance on ratings, 'ratings shopping' (which company will give best rating), and conflicts of interest that can lead to poor investment decisions.
what credit agencies do?
give credit scores to companies or financial products showing how risky or safe they are to invest
help investors make decisions and reduces information asymmetry
what is greenwashing in a fund?
when a fund pretends to be green or ESG focused but its actual investments don’t match that claim
What methods does ESMA use to analyze potential greenwashing?
Counts the frequency of ESG-related terms in fund documents.
Compares the use of these terms to the actual content of the fund's investments.
high score = high mismatch between ESG words and actual green impact
What are the risks and costs associated with RegTech and SupTech?
Data Privacy and Security risks due to handling sensitive data.
High implementation and integration costs.
Over-reliance on automation may lead to poor decision-making.
Poor data quality can result in unreliable outputs.
Legal and ethical risks related to algorithm use.
Fragmentation and interoperability issues across jurisdictions.
Skill gaps and resistance to change among users.
Model risk and potential technical failures.