The Digital Transformation Of Accounting And Finance: AI, Robots And Chatbots - Bernard Marr
Abstract It turns out that we might be at the perfect storm where how we store and access financial information combined with the maturation of tech capabilities are all in place to accelerate the digital transformation of accounting and finance. Just as others who faced the prospect of machines taking over jobs that used to be done by humans, accounting and finance professionals might anticipate the reality of 4th Industrial Revolution with fear. Actually, when machines take over repetitive, time-consuming and redundant tasks, it will free human finance professionals to do higher level and more lucrative analysis and counseling for their clients. Let’s take a look at just of the few opportunities that are now available thanks to the digital transformation of accounting and finance.
Artificial Intelligence in Finance - Paul Dravis
Abstract Artificial intelligence is reshaping how we work, interact and share information and content. AI technologies are increasingly being incorporated into many of our daily activities. AI opportunities in financial services are broad-based addressing needs in risk assessment, financial analysis, portfolio management, credit approval process, know your client (KYC) & anti-money laundering (AML) systems, various operational and customer interaction processes and system/data security. AI can present a threat to incumbent financial firms as well as create opportunities. A competitive challenge is that technology firms (Alibaba, Amazon, Apple, Baidu, Facebook, Google, IBM, Microsoft, Tencent and others) could expand their reach into financial services by leveraging their global presence, technical expertise, innovative platforms, customer data sets and brand loyalty. Momentum for AI will continue to build as data becomes more available, algorithms improve, developer skill sets broaden and computing power accelerates. However, concerns about AI include limited transparency about how systems work, bias in application logic, data quality and negative impact to employment.
Machine Learning in Finance: Present and Future Applications - Daniel Faggella
Abstract Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google’s Tensorflow).
Advances in Financial Machine Learning - Lopez de Prado
Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
Redefine Banking with Artificial Intelligence - Accenture
Abstract Artificial intelligence (AI) is creating the single biggest technology revolution the world has ever seen. The technology – which enables machines to simulate and augment human intelligence – has finally come of age. Across all industries, it’s being used to address a wide range of challenges, large and small, by making interactions with machines and systems simple and smart. Financial services companies, too, are entering the intelligence age. And they’re doing so while already under intense pressure on multiple fronts. Rapid advances in AI are coming at a time of widespread technological and digital disruption. Competition is fierce. More than half of Fortune 500 companies have gone out of business since 2000. And AI is set to take this disruption to a new level.
A deep learning framework for financial time series using stacked autoencoders and long-short term memory - Wei Bao, Jun Yue & Yulei Rao
Abstract The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day’s closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.