Are You Preparing to Fail on AI? Here's A Handy Checklist for Companies Artificial Intelligence (AI) is now everywhere in our daily lives. It makes new products and business models possible through deep analysis of mountains of data, predicting things that humans cannot foresee, and relieving employees of grunt work. For example, AI underpins the intelligent automation software in the logistics industry that is improving capacity planning. Pharmaceutical companies are working to improve the efficiency of research and clinical trials through AI systemic analysis of facts and extrapolations of likely outcomes. In the financial industry, AI technology locates better borrowers for loan companies, identifies money laundering patterns, and improves compliance reporting.

Fintech Limeglass has launched to'atomise' research by using technology to tag reports in real-time so that they can be easily and quickly searched by the recipient at a granular level. Rowland Park, chief executive and co-founder of Limeglass, said in a report this week that the research market needs innovation as the majority of reports are still being consumed by the buy side as multiple page PDF and HTML documents sent by email, despite advances in technology. Park has more than 30 years' experience in the research industry and founded and grew start-ups IDEA Global and 4CAST, which focused on macroeconomics, policy and financial markets intelligence. Park wrote in the report that financial decision making rests on a three-legged stool – market data; breaking news; and research, which provides wider context for decision making. "The development of tools to better handle market data and breaking news have transformed, and continue to transform, the way activity in the financial markets is conducted," he added.

Artificial intelligence is finding a new home throughout UBS far beyond just developing quantitative trading strategies. "Its applicability depends on the desired use, end objective, and problem it is trying to address," Beatriz Martín Jiménez, investment bank COO and UK CEO at UBS, told Markets Media. "At UBS, we extensively use AI across the entire operating cycle." Advances in technologies, such as machine learning, have made it possible for the bank to reimagine services and process delivery in a holistic fashion, she added. "In the past, we may have focused on solving distinct problems for an immediate fix. While looking at the broader and more permanent solutions may be harder, it will set us up for long-term success."

Two thirds of financial services firms currently deploy machine learning and expect to increase their use of the technology within the next three years. The Bank of England and the UK Financial Conduct Authority conducted a joint survey this year on the current use of machine learning, a methodology where computer programmes fit a model or recognise patterns from data, without being explicitly programmed and with limited or no human intervention. This contrasts with'rules-based algorithms' where the human programmer explicitly decides what decisions are being taken under which states of the world. The study said the median firm uses live machine learning applications in two business areas and this is expected to more than double within the next three years. Bank of England's machine learning paper is also interesting.

Ethical artificial intelligence and machine learning may sound like an undergraduate elective, but it is a topic that financial institutions need to address urgently. Firms are exposing themselves to a new type of risk as they either develop AI and machine-learning models or rely on the growing number of third-party model providers. Do these new models harm a specific subset of the population or unintentionally use practices that market regulators have deemed illegal? It can be hard to tell since AI and machine learning engines are good at dealing with black and white, but are horrible when it comes to shades of gray. These engines are only as good as the data that feeds them.

For all of the hype that machine learning has generated over the past few years, there is one question on every budget approver's mind: When will we see our return-on-investment? Organizations will not see an immediate return, according to Valentino Zocca, vice president, data science at Citi and who moderated a panel at the AI in Finance Summit in Midtown Manhattan. "It's a journey, and it takes time." A significant governing factor on the pace of ROI is how the firm organizes its machine-learning resources, added Kamalesh Rao, a senior data scientist at Société Générale and who participated on the panel. Centralized and decentralized approaches each have their strengths and weaknesses. The quickest way to see an ROI would be to embed one or two data scientists into existing data practices, according to Rao. "It might not be the entire organization, but an individual siloed practice that can deliver results on a platform, which can scale quickly," he said.

Artificial intelligence has established a toehold on institutional fixed income trading desks, but the real action will be in coming years. "Data, data science and AI," BlackRock Head of Global Trading Supurna VedBrat said when asked what's next in fixed income. Speaking Thursday morning at WBR's Fixed Income Leaders Summit in Philadelphia, VedBrat said emerging technology has the potential to change trading strategies on the buy side as well as the sell side. "AI gives us the ability to truly augment human intelligence with computing power, and be able to do that at scale," VedBrat said in a conversation with Tradeweb President Billy Hult. BlackRock, which VedBrat likened to a microcosm of the buy side as a whole, is in the preliminary stages of deploying AI, with much of the focus so far on small, "low-touch" transactions.

In a market where learning the last price can be a lift, what is the value of learning the next price? The emerging fintech firm's BondDroid Auto-Pricer goes beyond traditional pre-trade and post-trade data collection and utilization efforts, by using artificial intelligence to predict the next bid and ask price for the majority of the investment grade and crossover bonds traded in the market. The corporate bond market has for years been known for its fragmentation and unique liquidity challenges. Data quality is improving, but outside of the most heavily traded issues -- which represent a small fraction of the overall market -- pricing can still be more art than science. This is an opportunity for a solutions provider such as 7 Chord.

How Artificial Intelligence will revolutionize financial services is a hot topic among Wall Street technologists. But, just as a home renovation might be delayed by the discovery of extensive foundation problems that need to be repaired first, the financial services industry must improve the quality of its data before it can realize the benefits of AI. Early front-office AI applications, such as Deutsche Bank using AI to predict equity pricing, have won headlines. But there is also significant momentum around applying AI to post-trade processing, compliance and risk management: Instead of manually searching for a needle in a haystack to learn why a trade failed to identify a real issue, AI can pre-emptively flag problems and solve the exception. However, while many capital markets firms have explored AI initiatives, few of these pilot programs (less than 15% according to Forrester Research) have made it into production to yield real business value.

Duncan Higgins, head of electronic products at ITG, said using a machine learning approach in the broker's implementation shortfall algorithm in the US has been a'game changer'. Higgins told Markets Media that the industry needs to finish with MiFID II changes and move to business as usual, with reinvestment in algorithms and infrastructure. MiFID II regulations went live in the European Union at the start of this year after a multi-year investment and implementation process. He added that ITG has a big program of work including changing its implementation shortfall algorithm to use a machine learning approach. "The algo is much less constrained in its decision making and uses the state of the market and past experience to decide on the best approach to execute an order, determining how much and how to trade across lit and dark markets and auctions," said Higgins.