Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field.
The article was written by Amber Zhou, a Financial Analyst at I Know First. Artificial Intelligence (AI), was once the domain of fanciful science fiction books and films. But now the drive to eliminate human fallibility makes the technology stormily take the world across all industries, from self-driving cars to virtual assistants like Siri. Companies are significantly benefited from the cost saving from a variety of automated processes. Now programmers and data scientists are setting their sights on financial services.
Artificial intelligence (AI) has become a critical aspect in financial services. Financial institutions around the world are making efforts to adopt AI for task automation, customer services, behavior analysis, as well as fraud finding, and are making large-scale investments in related technologies. The World Economic Forum (WEF) estimates the number to reach US$10 billion by 2020. In financial services, applications for AI technologies exist across nearly the entire spectrum of business, from algorithmic stock trading applications and credit card fraud detection, to auto investment advisors and market research and sentiment analysis. The following 10 AI fintech companies are some of Europe's rising stars to watch very closely: Swiss startup Parashift develops AI-based accounting document management technologies which it offers through a SaaS platform and APIs.
The Russian subsidiary of the Austrian lender Raiffeisenbank has run the country's first ever mortgage deal on blockchain. It could be a taste of more to come in the nation. In the transaction, a mortgage contract was issued as an xml document containing all relevant information, including data on the mortgage loan issuer, the borrower, date and place of signing the deal, the total amount of the loan, and the repayment period. The use of blockchain for mortgage loan issuance is set to increase the safety of data storage, cut depository costs, and speed up transactions for both borrower and lender, Raiffeisenbank said in announcing the deal. Normally, after sealing a mortgage deal, the borrower has to visit the bank again to deposit the mortgage contract, while the application of blockchain allows the borrower to do it remotely, also cutting the amount of paper documents.
Technology is and has always been a crucial part of finance. From the first promissory notes (banknotes) in the Netherlands and China, there was a race with counterfeiters that parasitically undermined trust. As in political communication, technology is the message, rather than merely "a tool": when it comes to money, trust is not just instrumental, it is fundamental. With cashless payments being the norm and social media platforms weαving an additional layer of involvement in our social data web – Amazon, Google, Facebook, Apple – Artificial Intelligence (AI) is already in our wallets, business, and financial affairs. In a non-western setting, one may refer to the Chinese "social rating" system, which allows the state to value and evaluate social behaviour patterns, creating a link to individual credit rating.