Niccolo Mejia covers AI applications across industries at Emerj. He holds a bachelor's degree in Writing, Literature, and Publishing from Emerson College. HSBC Holdings is a multinational banking and financial services holding company and is ranked 99th on the Fortune 500 list. The bank has worked with multiple AI vendors and provided evidence of success that other banks may be able to study and take advantage of. In this article, we cover the bank's work with two vendors in particular: We begin our dive into HSBC's AI initiatives with its anti-money laundering solution from Ayasdi.

The 2008-09 financial collapse led to a Federal Reserve directive that banks with consolidated assets over $50 billion have additional risk assessment frameworks and budgetary oversight in place. To assess a bank's financial foundation, the Federal Reserve oversees a number of scenarios (company-run stress tests). Referred to as the Comprehensive Capital Analysis and Review (CCAR) process, these tests are meant to measure the sources and use of capital under baseline as well as stressed economic and financial conditions to ensure capital adequacy in all market environments. As Ayasdi reports, Citi consistently struggled to pass its annual stress test, failing two of the first three stress tests. The bank was in need of a way to rapidly create accurate, defensible models that would prove to the Federal Reserve that they could adequately forecast revenues and the capital reserve required to absorb losses under stressed economic conditions.

ONCE THE STUFF OF APOCALYPTIC SCI-FI tales, killer robots capable of choosing and taking out our nation's enemies are now within reach--if companies and the Pentagon decide to go that far. Defense officials have so far stopped short of developing Lethal Autonomous Weapons Systems (the government's official term), which could theoretically strike without a human order as easily as Facebook can tag friends in your photos without your say-so. But the A.I.-driven technology that could form the basis for such attacks is well underway. Project Maven, the Pentagon's most high-profile A.I. initiative, aims to use machine-learning algorithms to identify terrorist targets from drone footage, assisting military efforts to combat ISIS (more than 20 tech and defense contractors are reportedly involved, though they have not all been publicly named). Although supporting war efforts is nothing new for the defense industry, the Pentagon has increasingly looked to Silicon Valley for expertise in A.I. and facial recognition.

Improving models for risk and effective compliance yields tremendous benefits. Current technology investments in this space are already in the tens of billions. How can AI and machine learning augment existing investments in analytics and evolve these systems? Alex Baghdjian, Financial Services Strategy Lead at Ayasdi, will lead us through some real examples of machine intelligence in risk and compliance at financial institutions. Please note the location at Betaworks Studios.

Flagler Hospital, a 335-bed hospital in St. Augustine, Florida, had a problem. "It's the same story that every hospital in the country has faced in the last 50 years: trying to tackle clinical variation," Dr. Michael Sanders, the chief medical informatics officer of the hospital, said in a recent phone interview. "We tried to do the best we could in tackling this problem of clinical variation." But none of its tactics for addressing the issue were yielding great results. It was time for a change.

Artificial intelligence has the potential to dramatically re-arrange our relationship with technology, hearkening a new era of human productivity, leisure, and wealth. But none of that good stuff is likely to happen unless AI practitioners can deliver on one simple request: Explain to us how the algorithms got their answers. Businesses have never relied more heavily on machine learning algorithms to guide decision-making than they do right now. Buoyed by the rise of deep learning models that can act upon huge masses of data, the benefits of using machine learning algorithms to automate a host of decisions is simply too great to pass up. Indeed, some executives see it as a matter of business survival.

Dr. Dudley is supported by the following grants from the National Institutes of Health: National Institute of Diabetes and Digestive and Kidney Diseases grant R01DK098242; National Cancer Institute grant U54CA189201; Illuminating the Druggable Genome; Knowledge Management Center sponsored by the National Institutes of Health Common Fund; National Cancer Institute grant U54-CA189201-02; and the National Center for Advancing Translational Sciences and Clinical and Translational Science Award UL1TR000067. Dr. Shameer has received consulting fees or honoraria from McKinsey, Google, LEK Consulting, Parthenon-EY, Philips Healthcare, and Kencore Health. Dr. Dudley has received consulting fees or honoraria from Janssen Pharmaceuticals, GlaxoSmithKline, AstraZeneca, and Hoffman-La Roche; is a scientific advisor to LAM Therapeutics, NuMedii, and Ayasdi; and holds equity in NuMedii, Ayasdi, and Ontomics. Dr. Ashley is founder of Personalis Inc. and Deepcell Inc; and is an advisor to Genome Medical and SequenceBio. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Artificial Intelligence has caught the attention of the world, including financial institutions. While AI-based FinTech solutions may not get the same attention than autonomous cars or robot dogs, its impact will undoubtedly be felt. AI-based FinTech solutions will both save financial institutions billions in cost and create billions in additional revenues, potentially creating more than a trillion in additional profits in the financial services industry. A study done by Accenture showed that the implementation of AI in the financial sector could lead to a 31% increase in profitability rates by 2035.[1] Moreover, AI will allow to customize financial services delivered to clients, leading to an enhanced customer experience. Fraud detection and management is imperative for financial institutions now more than ever, as they are faced with new and more sophisticated threats to client data and security breaches.

"The 2008-09 financial collapse led to a Federal Reserve directive that banks with consolidated assets over $50 billion have additional risk assessment frameworks and budgetary oversight in place. To assess a bank's financial foundation, the Federal Reserve oversees a number of scenarios (company-run stress tests). Referred to as the Comprehensive Capital Analysis and Review (CCAR) process, these tests are meant to measure the sources and use of capital under baseline as well as stressed economic and financial conditions to ensure capital adequacy in all market environments." As Ayasdi reports, Citi consistently struggled to pass its annual stress test, failing two of the first three stress tests. The bank was in need of a way to rapidly create accurate, defensible models that would prove to the Federal Reserve that they could adequately forecast revenues and the capital reserve required to absorb losses under stressed economic conditions.

Ayasdi offers an enterprise-grade artificial intelligence platform that leverages big data to make intelligent business applications; for instance, Ayasdi has an application that powers parts of HSBC's anti-money laundering technology stack. Headquartered in California, Ayasdi has further offices in London with global expansion demanding a third office potentially coming to Singapore for 2018. Lots of stuff is going on with AI. Broadly speaking there are two major ways of thinking about problems addressable by AI today. One side is around perception based problems – self driving cars and virtual assistants – these rely on data such as imaging and sensing the environment.