Outsourcing for AI in Automating Routine Financial Tasks

Overview

I'll provide an overview of the most recent research, practice, and policy-related literature on AI. This involves utilizing computer science, economics, and finance. Literature in addition to publications pertaining to regulations. In particular, I go over four ways AI is transforming the financial services sector:

1. Fraud detection (the use of AI to prevent illicit funds from entering the financial system)

2. Chatbots for banks

3. Algorithmic trading

4. Regulatory and policy considerations

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Overview of AI, ML, and DL Taxonomy and History

The Appendix provides a history of the evolution of the field of artificial intelligence. The timeline makes it evident that logic-based algorithms were among the rules that were the focus of early AI efforts. Turing (1950) describes the Turing Test, an operational test for intelligent behavior. Turing's groundbreaking work offered the language, reasoning, knowledge, learning, and comprehension that are key elements of future AI work.

Turing established the foundation for machine learning, genetic algorithms, and reinforcement learning with the Turing Test. The "symbol processing hypothesis" refers to the effort to use symbol processing to mimic the logical progression of human decision-making (Newell, Shaw, and Simon, 1957; Newell and Simon, 1961, Gilmartin, Newell, and Simon, 1976).

The development of a significant body of work on Bayesian statistics, which would later be applied in machine learning, began in the 1960s. The foundation of deep learning, neural networks, were created in the 1960s and expanded quickly.

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Financial Services Value Proposition

Although financial institutions and banks have been implementing applications of artificial intelligence (AI) for many years in a range of use cases, such as fraud and credit risk management, it has the capacity to completely transform a far wider range of corporate operations.

Many applications, including redefining the future of financial advice, processing insurance claims, and customer marketing, engagement, and servicing, have been identified as potentially useful. Consideration is also given to internal applications like contact center operations, compliance monitoring, application development, and maintenance.

The notable improvements in the capabilities provided by this technology, especially in parsing and making use cases, are one factor driving the growth of use cases. Comprehension of unstructured data, like text. The ability to query data in a Q&A or natural human interaction format and offer natural language instructions to produce or improve new business content opens up even more possible applications.

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In Conclusion

The increasing use of AI in finance will present additional difficulties, such as ethical, legal, and financial ones. Social barriers. Additionally, AI will keep adding new complexities to the global financial system. AI programs will get increasingly complex as computing power rises and more data becomes available.

However, are AI, ML, and DL so distinct from earlier developments that they have the potential to defy financial laws? This question is posed in an intriguing article published in the Financial Times in 2018. For instance, depreciation accounted for roughly one-third of operating cash flows worldwide in 2017. However, what would happen to an AI-enabled computer that genuinely gets smarter over time?