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Category: Ai in Finance

Executive Summary of AI, Machine Learning and Big Data in Finance | Chapter 1

Artificial intelligence (AI) in finance  Machine-based systems with varying levels of autonomy are called Artificial Intelligence (AI) systems. They can make predictions, recommendations or decisions for a given set of human-defined objectives. AI techniques increasingly use massive amounts of alternative data sources and data analytics, known as ‘big data’. These

Ai Machine Learning and Data in Financial Services | Chapter 2

1.1. Introduction  AI systems are computer-based systems that operate at varying degrees of autonomy. They can make predictions, provide recommendations, or make decisions based on human-defined objectives. These systems rely on large amounts of data, commonly called “big data”, and use data analytics to perform these tasks. Machine learning models

Artificial Intelligence, Machine Learning and Data in Financial Services | Chapter 3 | Part 2

AI/ML, big data in finance: advantages and effect on business models/activity of financial sector participants 2.3. Credit intermediation and assessment of creditworthiness  Banks and fintech lenders increasingly rely on AI-based models and big data to evaluate the creditworthiness of potential borrowers and make underwriting decisions, both of which are fundamental

Artificial Intelligence, Machine Learning and Data in Financial Services | Chapter 3 | Part 3

AI/ML, big data in finance: advantages and effect on business models/activity of financial sector participants  2.4. Integration of AI in Blockchain-based financial products   Distributed ledger technologies (DLT) such as blockchain have become increasingly popular in various industries, particularly finance. This growth can be attributed to the advantages of speed, efficiency,

Ai, Machine Learning and Big Data in Financial Services | Chapter 4 | Part 1

Emerging risks from the use of AI/ML/Big Data and possible risk mitigation tools (Chapter 4 | Part 1) The use of AI/ML technology is expanding in the financial markets, and as a result, various challenges and risks associated with this practice are being revealed. These challenges are visible at different

Ai, Machine Learning and Big Data in Financial Services | Chapter 4 | Part 2

Emerging risks from the use of AI/ML/Big Data and possible risk mitigation tools (Chapter 4 | Part 2) 3.4. Explainability  Explainability is a significant challenge when it comes to Machine Learning models. It is difficult to understand why and how the model generates results, and this lack of transparency is

Ai, Machine Learning and Big Data in Financial Services | Chapter 4 | Part 3

Emerging risks from the use of AI/ML/Big Data and possible risk mitigation tools (Chapter 4) Part 3 3.6. Governance of AI systems and accountability Solid governance arrangements and transparent accountability mechanisms are crucial when deploying AI models in high-value decision-making scenarios such as credit access and investment portfolio allocation. The

Ai, Machine Learning and Big Data in Financial Services | Chapter 5

4. Policy responses and implications  4.1. Recent policy movement around AI and finance AI has had a significant impact on special demands, and policymakers have recognized the potential risks associated with its services. Consequently, AI regulation has become a priority in recent years. In May 2019, the OECD introduced its