While blockchain and quantum computing continue to inch up the emerging technologies hype cycle, artificial intelligence (AI) is revolutionising the financial services industry by stealth. Fuelled by the three'V's of big data – velocity, volume and variety – AI tools are being deployed across the bank, from the customer front end to deep in the back office. AI is an umbrella term that covers robotic process automation, or the simple automation of processes such as data entry; natural language processing (NLP), most commonly used in chatbots; and machine learning, as is used in robo-advisory services and credit scoring. To date, much of the focus has been on customer benefits, such as improved experience and more personalised products; however, Cathy Bessant, Bank of America's chief operations and technology officer, believes that the more exciting application areas are in risk management and financial forecasting. "AI gives us the ability to take vast amounts of data and produce forecasts and risk assessments based on changing variables to understand their impact. The opportunity for fast, world-class risk management is huge," she says.
AI encompasses an array of technologies, from fully automated or autonomous intelligence to assisted or augmented intelligence. Financial firms are already deploying some relatively simple AI tools, such as intelligent process automation (IPA), which handles non-routine tasks and processes that require judgment and problem-solving to free employees to work on more valuable jobs. Banks have been using AI to redesign their fraud detection and anti-money laundering efforts for a while, and investment firms are starting to use AI to execute trades, manage portfolios, and provide personalized service to their clients. Insurance organizations, in turn, have been turning to AI--and especially machine learning (ML)--to enhance products, pricing, and underwriting; strengthen the claims process; predict and prevent fraud; and improve customer service and billing. But before financial institutions can reap all of AI's benefits, they must first overcome challenges, including security, privacy, bias, and regulatory issues.
Does your bank help you reflect on spending habits and help you make wiser decisions? Did it detect that it wasn't you who made that last purchase? By combining the powers of big data, artificial intelligence, and machine learning, banks can do much more than keep your money and pay you a nominal interest rate. "Banking is necessary, banks are not." That's something Bill Gates said way back in the 90s, but the true weight of that short sentence is being felt today.
Artificial intelligence (AI) continues to be one of the most popular technologies among business executives in decades. Increased pressures on financial services organizations is driven in part by investors' enthusiasm for digital capabilities. This has fueled the need for well-planned transformation strategies to innovate in order to enhance the customer service experience. Early adopters of AI tools such as chatbots for sales, forecasting, functions automation are already benefiting from an increase level of efficiency. An understanding up-front of what AI can do for the business is crucial.
I recently published a longer piece on security vulnerabilities and potential defenses for machine learning models. Today it seems like there are about five major varieties of attacks against machine learning (ML) models and some general concerns and solutions of which to be aware. Data poisoning happens when a malicious insider or outsider changes your model's input data so that the predictions from your final trained model either benefit themselves or hurt others. A malicious actor could get a job at a small disorganized lender, where the same person is allowed to manipulate training data, build models, and deploy models. Or the bad actor could work at a massive financial services firm, and slowly request or accumulate the same kind of permissions.
Use of AI within wealth management has the potential to revolutionise a sector struggling with digital change, but according to Tim Waterton, VP of UK Business at M-Files, any use of automation must be applied smartly and not simply for the sake of it. The wealth management sector is facing a growing need for technology-led change, driven by a new generation of wealth. AI could hold the answer, but many wealth managers are struggling when it comes to implementation. A recent poll amongst 500 private wealth executives shows that AI represents both a major challenge and opportunity, with over a third (36 per cent) of respondents admitting they have struggled to capitalise on the technology. Waterton agrees that AI has the potential to transform the way wealth management professionals deliver services to clients and streamline their processes, but also advises caution relating to over-automation.
Brighterion recently surveyed more than 200 financial institutions across the U.S. with assets ranging from $1 billion to more than $100 billion to determine how AI and machine learning are being used in the financial services industry. According to our findings, adoption of AI and machine learning is admirably high, with many financial organizations working to implement the technology to more effectively service their customers, manage new and ongoing investments, combat fraud and augment their workforce. However, our data also indicates that few financial organizations are successfully leveraging AI to the full extent of the technology's power. Too often, financial organizations fail to recognize the stark differences between various supervised and unsupervised learning technologies, and many neglect to consider which AI and machine learning functions are best suited to specific business objectives. To realize the full potential of AI and make the most of such a substantial technological investment, financial organizations need to resist getting swept up in the collective AI hype and instead focus on the technology's most fundamental capabilities.
Long an obsession of science fiction writers, "artificial intelligence" in the modern era of fast-paced technological innovation is a term that is as ubiquitous as it is nebulous. For the payments technology industry, however, the term describes advanced analytical technology that has an outsized potential to improve the payments ecosystem for banks, payments processors, merchants and consumers. In fact, financial services companies will spend US$11 billion on AI in 2020, according to an analysis by IDC -- more than any other industry cited. They'll stand to make a nice return on their investment as well, according to PwC estimates. In North America alone, AI is projected to increase the GDP of the financial and professional services industry as much as 10 percent by 2030, driven by increases in both productivity and consumption.
Employees then will need to assume new or expanded tasks that require a higher level of judgement and creativity, while tasks that are repetitive and rules-based will be automated. These jobs were projected for the financial services industry across key sectors that included banking, capital markets, asset management, and insurance, and were part of a study released by the Ministry of Manpower. Conducted by Ernst & Young, the report was commissioned by the Institute of Banking and Finance Singapore (IBF) and Monetary Authority of Singapore (MAS) to assess how data analytics and automation would likely transform the local financial sector over the next three to five years. Country's government has introduced initiatives to train 12,000 people in artificial intelligence skillsets, including industry professionals and secondary school students. In conjunction with the release, the IBF and Workforce Singapore (WSG) also introduced a programme that encompassed structured training and attachment schemes with financial institutions to help professionals start a career in technology.
One can argue that even the most innovative banking institutions are bureaucratic enough, and their slow decision-making causing banks to lose their premium over fintech applications, peer to peer lending marketplaces, and payment processors. At the same time, many expanded into the business of micro-lending. Banking services are no longer a monopoly of banks, and traditional financial institutions have to innovate in order to survive. The era of non-traditional financial services providers such as Amazon Payments, PayPal Payments and PayU, has risen. The launch of the Payment Services Directive II in Europe unlocks new dynamics for FinTech and Payment Services.