Artificial Intelligence and the Case of Takasbank

Artificial intelligence (AI) is no longer a distant, technological frontier but a reality that is shaping industries, including finance. While AI has been adopted in trading, fraud detection and customer service, its application within Central Counterparties (CCPs) represents a profound shift with far-reaching implications. As financial markets evolve, CCPs are turning to AI to enhance risk management, optimise collateral processes and bolster regulatory compliance.

CCPs play a fundamental role in ensuring financial stability by mitigating counterparty risk and guaranteeing trade settlement. However, as financial instruments become more sophisticated and markets more interconnected, traditional models for risk assessment and market surveillance are being challenged. AI presents an opportunity to refine these processes, but its adoption raises important questions around transparency, regulatory oversight, and systemic resilience.

This article aims to summarise the use of AI in CCPs. It will provide an overview of AI’s evolution and examine its applications in clearing institutions, including associated risks and challenges. Finally, it will present existing and potential AI implementations at Takasbank, a key institution that has started to integrate this technology in Turkey.

The Evolution of Artificial Intelligence 

AI has emerged as one of the fastest-growing technological fields in recent years, impacting the financial sector significantly. The concept of AI first appeared in the 1950s and initially consisted of rule-based programmes that followed pre-defined instructions. However, with the advent of Machine Learning (ML) and Deep Learning (DL), AI has evolved into a sophisticated technology capable of solving complex problems.

AI’s evolution can be categorised into three main phases:

• Rule-Based Systems: The first generation of AI systems operated under strict rules, executing pre-programmed commands. These systems used fixed logic and algorithms to perform specific tasks but struggled to adapt to dynamic and complex data structures. The emergence of ML helped address this limitation. 
• Machine Learning (ML): Since the 1990s, AI entered a new phase with the advancement of data mining and statistical modeling. ML enabled systems to learn from large datasets, recognise patterns and predict trends. This opened up a wide range of applications, from financial analysis to customer behaviour prediction. 
• Deep Learning (DL): In the 2010s, AI advanced further with DL, which employs artificial neural networks to mimic human cognition. DL allowed AI to make sophisticated predictions, uncover hidden trends in data and operate autonomously with minimal human intervention.

Financial systems, which require rapid and precise decision-making due to high transaction volumes, dynamic market conditions and constantly evolving risk factors, have turned to AI. In this context, AI has become a powerful tool for CCPs, optimising processes related to risk management, transaction validation, collateral calculations, market analysis and data security. The following section will explore the general AI applications in CCPs.

The Use of AI in CCPs 

The growing complexity of financial markets necessitates more sophisticated risk-management mechanisms, and AI has emerged as a key enabler in this regard. One of its most valuable contributions is in early risk detection. By leveraging big data analytics, AI can assess vast streams of market information, identifying potential stress points before they materialise. This proactive approach enhances a CCP’s ability to mitigate market turbulence and maintain systemic stability.

Beyond risk forecasting, AI also strengthens market surveillance. With the proliferation of high-frequency trading (HFT) and algorithmic transactions, monitoring market activity is challenging. AI-driven surveillance systems analyse transaction patterns, flagging unusual behaviours indicative of potential manipulation or insider trading.

Unlike traditional rule-based monitoring, AI refines its detection capabilities, adapting to emerging risks in real time.

Another transformative application lies in collateral optimisation. AI enables more efficient collateral management by assessing margin requirements based on real-time market conditions. This not only reduces liquidity constraints for clearing members but also enhances overall market efficiency. Additionally, natural language processing (NLP) facilitates regulatory compliance by automating documentation reviews and ensuring adherence to evolving regulatory standards.

Although AI offers numerous benefits to CCPs, it also introduces risks related to transparency, data security, algorithmic bias and third-party dependencies. Transparency is a critical issue, as AI models often function as black boxes, making it difficult for regulators and market participants to understand how decisions are made. Furthermore, AI’s reliance on extensive datasets introduces cybersecurity and data-privacy concerns, while algorithmic biases can skew risk assessments if not managed properly. Finally, reliance on third-party AI vendors raises questions about dependency and control, necessitating careful oversight and strategic investment in in-house capabilities.

AI Applications at Takasbank 

AI initiatives at Takasbank can be categorised into two main areas. The first focuses on Takasbank’s in-house AI development process, aimed at minimising risks related to data security and third-party dependency. The second involves the integration of commercial AI solutions to enhance operational efficiency.

Takasbank has started integrating commercial AI applications into certain limited areas to reduce operational costs and risks. One of the critical aspects of this implementation is ensuring that commercial AI solutions are not used within Takasbank’s core systems. To achieve this, commercial AI applications have been deployed in separate environments, independent of Takasbank’s main infrastructure. In the initial phase, these AI applications have been designed to process publicly available information and generate outputs that can either be made public or accessed by members. For 2025, the following AI-driven projects have been prioritised.

Developing Infrastructure for Conditional Volatility Metrics in Collateral Calculations: Takasbank aims to improve the efficiency of the parameters used in collateral calculations by diversifying the volatility estimation methods it employs. In addition to the currently used historical simulation method, the institution is incorporating Exponentially Weighted Moving Average (EWMA), Generalised Autoregressive Conditional Heteroskedasticity (GARCH), and Expected Shortfall models to refine volatility predictions. The results obtained from these methods will be evaluated using criteria such as Root Mean Square Error (RMSE) and the number of breaches to determine optimal values.

To reduce development costs for the IT team, the AI system has been programmed to generate the necessary code for these calculations. The research phase for selected reference assets has been completed, and the testing phase for the calculations and processes is currently ongoing.

News Sentiment Analysis in Credit Rating Processes: Takasbank conducts credit-rating assessments for its members twice a year, primarily relying on financial data. However, news reports about these institutions are also included as an input in the evaluation process. Given Takasbank’s extensive network—covering more than 300 institutions, including banks, brokerage firms, and leasing companies — the manual tracking of news reports on all members presents significant operational costs.

To address this challenge, Takasbank is exploring the development of an AI-powered solution to automate news monitoring. Initially, AI will be used to scan and extract relevant news articles from pre-determined internet sources. In the next phase, the system will analyse the collected data and identify articles containing negative content, enabling more effective risk assessment in the credit rating process.

AI-Powered Chatbot for Central Counterparty Services: Takasbank provides CCP services for various financial markets, including equities, fixed income, derivatives, securities lending, money markets, and TL OIS contracts. One of the major operational challenges in this role is handling the high volume of inquiries from members, particularly concerning risk management, collateral requirements, and margin calls. A recent analysis of these inquiries revealed that most of them were rule-based questions, already covered in Takasbank’s operational guidelines. Based on these findings, Takasbank is developing an AI-powered chatbot to handle first-level customer support. This chatbot is expected to significantly reduce the workload of operational teams by automatically responding to frequently asked questions. The project’s initial phase involves uploading all CCP-related documents into the AI system, allowing it to generate automated responses to incoming queries. Over time, the chatbot will be enhanced to handle more complex inquiries, making AI an integral part of Takasbank’s customer service strategy.

AI for Market Stress Testing: As is well known, CCPs are required to conduct stress tests to assess the adequacy of their default management resources. These stress tests typically rely on historical and hypothetical market scenarios. Takasbank has launched a project to enhance stress testing capabilities by incorporating AI-driven scenario generation. Specifically, the AI model will analyse asset volatility and correlation structures to create more dynamic and data-driven stress test scenarios. Currently, the research and development phase of this initiative is ongoing, with further refinements expected as the project progresses.

Conclusion and Future Perspective

As AI continues to evolve, its role in CCP operations will deepen. Institutions that embrace AI proactively while addressing its risks will be better positioned to navigate the complexities of modern financial markets. The key to success lies in a balanced approach — one that leverages AI’s capabilities while ensuring transparency, security, and regulatory alignment.

For CCP professionals, the AI transformation presents both opportunities and challenges. AI has the potential to enhance financial stability, optimise risk frameworks, and improve market efficiency. However, its adoption must be accompanied by careful governance, ensuring that AI remains a tool for resilience rather than a source of unintended systemic risk.

To ensure responsible AI integration, collaboration among regulators, technology providers, and financial institutions is essential. AI’s role in financial markets will continue to expand, making it imperative for CCPs to adopt a balanced and strategic approach to AI implementation.