If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Machine learning, machine intelligence, thinking machine, electronic brain – whatever you want to call it, artificial intelligence is here to stay. Although, machines haven't completely taken over, they have slowly but surely crept into our lives affecting the way we live, communicate and ultimately work. From voice-driven assistance on a mobile phone, suggestive searches to autonomous driverless cars, we will continue to see fast-evolving technologies in the coming years. At ACCA we have a deep interest in how technology impacts the accountancy profession and how it will continue to do so in the future. This year will see the 30th anniversary of the worldwide web – meaning we are firmly part of the digital revolution; technology is something accountants cannot shy away from or avoid.
Where once banks and credit unions routinely left technology to specialists, the subject now has become elevated to the highest-ranking issue impacting retail banking. Research by The Economist Intelligence Unit (EIU) for Temenos finds that coping with new technology is the top concern of retail bankers, ahead of changing consumer behavior, political and economic instability and dealing with bad loans, among other factors. No institution can afford to ignore the combination of new competition from fintechs and big technology companies, multiple new technologies, and soaring consumer expectations is bringing unprecedented change to retail banking that And few are ignoring it, as the EIU survey indicates. However, the how quickly and how extensively organizations respond varies sharply by institution and sometimes even by country. In a study of 161 publicly traded banking institutions around the world, Accenture found that just over half are "digital laggards," with no plans to go digital or just "half-hearted efforts."
Small and medium-sized businesses are the keystone of the modern-day labor market. In the United States alone, small businesses employ almost 50% of the private workforce, and recent data shows that companies with fewer than 20 employees have added 1.2 million net new jobs. But although their growth is vital to a sustainable global economy, SMBs continue to struggle to get the funding they need. The traditional lending system simply isn't set up to meet the smaller capital needs of these types of enterprises: taking into account the risks and the long review process, small business loans typically don't pay off for banks. Chances of being accepted are incredibly low for businesses that aren't already well-established, and they rarely have the structure to carry them through the long review process anyway.
AI is making its way into many areas of the payments and financial services industries from helping banking and credit card systems detect and spot fraudulent activity, to enhancing customer service, providing hyperpersonalized credit scores and offers, and driving new forms of transactions like stores with no cashiers. In this infographic from Cognilytica we explore 6 ways AI is enhancing payments.
The US labor market looks markedly different today than it did two decades ago. It has been reshaped by dramatic events like the Great Recession but also by a quieter ongoing evolution in the mix and location of jobs. In the decade ahead, the next wave of automation technologies may accelerate the pace of change. Millions of jobs could be phased out even as new ones are created. More broadly, the day-to-day nature of work could change for nearly everyone as intelligent machines become fixtures in the American workplace. Until recently, most research on the potential effects of automation, including our own, has focused on the national-level effects. Our previous work ran multiple scenarios regarding the pace and extent of adoption. In the midpoint case, our modeling shows some jobs being phased out but sufficient numbers being added at the same time to produce net positive job growth for the United States as a whole through 2030.
The financial services industry has seen an explosive growth in Artificial Intelligence (AI) to supplement, and often supplant, existing processes both customer-facing and internal. Given the potential created by rapid advancements in AI sophistication and functionality, more and more financial services firms are leveraging the technology to deploy new use cases for improved decision-making processes – particularly in the areas of anti-money laundering, fraud prevention, risk management, and lending. While the first wave of AI was generally focused on automating manually-intensive and repetitive tasks, banks are now turning to machine learning systems (ML) to uncover more dynamic ways of interpreting their vast swaths of customer data. Whereas AI, at a fundamental level, permits a machine to imitate intelligent human behavior, ML is a specific application (or subset) of AI that enables systems automatically to learn and improve – e.g., reduce errors or maximize the likelihood that their predictions will be true – without being explicitly programmed to make such adjustments. This development has an exciting potential to expand the products available to underbanked communities and improve services and customer experience as a whole.
Bias will continue to be a fundamental concern for businesses hoping to adopt artificial intelligence software, according to senior executives from IBM and Salesforce, two of the leading companies selling such A.I.-enabled tools. Companies have become increasingly wary that hidden biases in the data used to train A.I. systems may result in outcomes that unfairly--and in some cases illegally--discriminate against protected groups, such as women and minorities. For instance, some facial recognition systems have been found to be less accurate at differentiating between dark-skinned faces as opposed to lighter-skinned ones, because the data used to train such systems contained far fewer examples of dark-skinned people. In one of the most notorious examples, a system used by some state judicial systems to help decide whether to grant bail or parole was more likely to rate black prisoners as having a higher risk of re-offending than white prisoners with similar criminal records. "Bias is going to be one of the fundamental issues of A.I. in the future," Richard Socher, the chief scientist at software company Salesforce, said.
A variety of business processes under the umbrella of retail banking are the constructive consequence of AI and automation services. Not only the payment processing automation and fraud detection but banks are also getting benefitted by automated credit scoring and customer service chatbots. Fraud detection, credit scoring, and chatbots turn out to be the major beneficiation of the retail banking system. The banks install anomaly detection software to their system which is trained in real-time on a range of labeled data retrieved from transactions and loan applications. The ML algorithms analyze every single bit of data before it can be labeled under fraud case.
Machine learning is highly pervasive today so much so that we use it a dozen times a day without even realizing. Machine learning involves getting computers to learn, think, and act on their own without human interference. As described by Google, "Machine learning is the future." With an increasing number of humans becoming addicted to their machines, the future of machine learning looks very bright. We are indeed witnesses to a new revolution which is taking over the world owing to its immense potential.