Artificial intelligence is not a technology, but a group of related languages - natural language processing, machine learning (computer programs that can "learn" when exposed to new data) and expert systems (software programmed to provide advice) - that make machines understand, understand, and operate in ways similar to the human brain. These technologies are behind innovations such as virtual agents (animated characters that serve as computer-generated, online customer service representatives), identity analytics (solutions that combine big data and sophisticated analytics to help manage user access and authentication), and recommendation systems (matching algorithms). Consumers and goods and services providers) have already changed the way companies view the entire customer experience.
Artificial intelligence helps the finance teams of banks to redesign and redesign operating models and processes. Big banks need to process huge amounts of data in order to generate financial statements and meet regulatory and compliance requirements. These processes are largely standard and normative, but still involve large numbers of people who perform low-value-added tasks (often in reconciliation and integration). This makes them ideal candidates for Robotic Process Automation (RPA). Software used in RPA is coded to meet the "bots" rules and some exceptions, but it is an additional layer of machine learning in more complex challenges and frequently changing tasks, which makes the combination of RPA and AI particularly powerful.
In the next few years, AI will be used to transform many central functions in finance such as financial analysis, asset allocation and more strategic functions, as well as intercompany reconciliation and quarterly "close" and earnings reporting. Expected. AI provides speed and accuracy - the entire reporting and disclosure process, for example, can be undertaken in real (or almost real) time. Instead of waiting until the end of the quarter, the finance team, empowered by AI, can detect and adjust issues more quickly than is possible today, increase accuracy and eliminate periodic endeavors.
Financial institutions are well aware of the potential of AI. They have seen major disruption in other industries, as digital startups and Internet giants use AI to streamline operations and entice consumers with more personalized, relevant offerings and experiences developed on new, technologically enabled platforms. As a result, banks are investing heavily in recruiting and developing new technologies and talent needed to implement and work effectively with AI solutions.
Artificial intelligence provides immensely powerful tools for banks, capital market companies and insurers to change and streamline their most basic financial processes. However, the challenge for many is not just identifying and adopting the best AI technologies, but also re-designing and rethinking their operating model and talent development to leverage AI's transformative capabilities.
Artificial intelligence can help dramatically improve operational efficiency and gain a clearer understanding of where they are going, but it is still up to humans to decide on a course for AI and related technologies to help them make big strategic decisions and drive profitable growth.
AI and machine learning can also help automate other investment classes, a trend that has been growing over the past year. A recent report by PWC states, "Some financial institutions are investing in AI, while other companies are now thanks to the progression of big data, open source software, cloud computing and faster processing speeds." Arthena not only makes such technological advances, but is also using AI's willingness in investing to create the first automated art market investment platform, generating calculated investment opportunities that can return more than 15% y0y.
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