Insights In Banking Analytics And Regulatory Compliance Using Ai
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Insights in Banking Analytics and Regulatory Compliance Using AI
The integration of artificial intelligence (AI) into banking analytics and regulatory compliance revolutionizes the financial industry, enhancing operational efficiency, improving decision-making, and strengthening regulatory adherence. AI-driven analytics enable banks to process data in real time, uncovering valuable insights that can drive personalized services, risk management strategies, and fraud detection. AI enhances the monitoring of financial transactions, automates compliance reporting, and helps identify potential risks related to money laundering, fraud, and illegal activities. By leveraging machine learning algorithms and natural language processing, AI tools can ensure that banks remain up to date with regulations, reduce human error, and mitigate the cost and complexity of compliance. The use of AI in banking analytics and regulatory compliance reshapes the way banks operate and fosters greater transparency, accountability, and trust within the financial ecosystem. Insights in Banking Analytics and Regulatory Compliance Using AI focuses on various aspects of use of AI on business analytics. It explores how AI reshapes the field of business analytics and drives more efficient, informed decision making. This book covers topics such as blockchain, data science, and artificial intelligence, and is a useful resource for business owners, policymakers, engineers, academicians, researchers, and data scientists.
Disrupting Finance
This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry.
Artificial Intelligence in Financial Services and Banking Industry
Author: Dr. V.V.L.N. Sastry
language: en
Publisher: Idea Publishing
Release Date: 2020-03-20
In the last couple of years, the finance and banking sectors have increasingly deployed and implemented Artificial Intelligence (AI) technologies. AI and machine learning are being rapidly adopted for a range of applications for front-end and back end processes to both business and financial management operations. Thus, it is quite significant to consider the financial stability repercussions of such uses. Since AI is relatively new, the data on the usage is largely unavailable, any analysis may be necessarily considered Preliminary1 . Some of the current and potential use cases of AI and machine learning in the finance sector include the following. Institutions use AI and machine learning methods to optimize scarce capital, back-test models, and analyze the market impact of trading large positions. Financial institutions and vendors use AI and machine learning techniques to evaluate credit quality for market and price insurance contracts, and to automate client interaction. Brokers, hedge funds, and other firms are using AI and machine learning to find pointers for higher (and uncorrelated) returns to optimize trading execution. Private and public sector institutions use these technologies for data quality assessment, surveillance, regulatory compliance, and fraud detection. This book seeks to map the use of AI in current state of affairs in the banking and financial sector. By doing so, it explores: The present uses of AI in banking and finance and its narrative across the globe.