Banks and financial services institutions face increased competition not only from peer organizations within the industry, but also now from FinTech startups, Neobanks, and others. The way to compete is to deliver highly personalized services and innovative offerings. And increasingly, the way to do that is using AI/ML to derive data-driven insights upon which those services and offerings can be based. Financial services institutions increasingly have sought to use new data sources to expand their traditional risk analysis and make more personalized offerings to a larger customer base. Many have gone beyond traditional methods (e.g., using FICO scores, credit history, salary, and more) and developed new risk models and highly individualized creditworthiness ratings based on the analysis of additional data sources.

In recent years, players within Canada's financial services industry, from banks to Fintech startups, have shown early and innovative adoption of artificial intelligence ("AI") and machine learning ("ML") within their organizations and services. With the ability to review and analyze vast amounts of data, AI algorithms and ML help financial services organizations improve operations, safeguard against financial crime, sharpen their competitive edge and better personalize their services. As the industry continues to implement more AI and build upon its existing applications, it should ensure that such systems are used responsibly and designed to account for any unintended consequences. Below we provide a brief overview of current considerations, as well as anticipated future shifts, in respect of the use of AI in Canada's financial services industry. At a high level, Canadian banks and many bank-specific activities are matters of federal jurisdiction.

The rise of technology and digital services has led to increasing customer demands for simplicity and speed. Banks and financial services institutions are continuously searching for new ways to retain and attract customers while aiming to respond to heightened consumer demand for personalized services. For this reason, customer-centric offerings continue to dominate the financial technology (FinTech) landscape. Personalization takes advantage of real-time data and cutting-edge technologies to deliver product or service information to customers. In an extremely competitive financial services sector, there is more pressure than ever for FinTech companies to provide customers with a better experience.

Artificial Intelligence (AI) continues to gain traction as a significant business driver for large organizations across major industries. The traditional financial services industry was known to be slower in adopting new technologies, with some organizations using software running on COBOL or Fortran, which was invented in the 50s and 60s. Recently, thanks to the rise of fintech and more AI applications becoming available, the industry is starting to accelerate investment in AI across all key business functions. But how should the right AI use cases in FinServ be identified? While internal efficiency use cases might be great short-term wins, zeroing in on the customer experience may prove to be a competitive advantage in the future.

The financial landscape has been rapidly evolving with the rise of financial technology (fintech) companies and startups that are more agile and technologically advanced. This has led financial services institutions (FSIs) to revise their business models and evaluate how they can integrate technology into their operations. Robotic process automation (RPA) is no longer a foreign term in the financial field. Pairing RPA with artificial intelligence (AI) creates intelligent process automation (IPA) that works as a catalyst in digital transformation in FSIs. Like many other industries, the financial field is heavily reliant on documents and legacy systems.

Ayn began her career in journalism and went on to work in corporate communications at Accenture for seven years before joining the content and research team at Emerj. Banks are in one of the best positions for leveraging AI in the coming years because the largest banks have massive volumes of historical data on customers and transactions that can be fed into machine learning algorithms. We recently completed our Emerj AI in Banking Vendor Scorecard and Capability Map in which we explored which AI capabilities banks were taking advantage of the most and which they might be able to leverage in the future. When it comes to big data in banking, banks might be primed to think about using their customer data to build a conversational interface or chatbot to improve the customer experience and, perhaps most importantly, attract millennial customers who are used to getting their needs met quickly over the internet. Despite this, banks are unlikely to leverage their customer data nowadays. In fact, although over 35% of the press releases we explored for our report mentioned conversational interfaces, they represent only 8% of the total funding for AI vendors selling into banking.

When people talk about artificial intelligence (AI), the discussion commonly turns to flashy robotics – how they support manufacturing production lines, disable explosives, or even vacuum the floors. Major steps in AI were made in the late 1950s and early '60s, with flagship examples like the ELIZA computer program that demonstrated the superficiality of communication between humans and machines. From then until the 1980s, there was great promise that AI could revolutionize businesses, but there was no major disruption. Today it feels as if AI is born again, and it is much more than robots. Innovative new AI technologies are delivering benefits to a wide variety of industries, including financial services.

When people talk about artificial intelligence (AI), the discussion commonly turns to flashy robotics – how they support manufacturing production lines, disable explosives, or even vacuum the floors. Major steps in AI were made in the late 1950s and early '60s, with flagship examples like the ELIZA computer program that demonstrated the superficiality of communication between humans and machines. From then until the 1980s, there was great promise that AI could revolutionize businesses, but there was no major disruption. Today it feels as if AI is born again, and it is much more than robots. Innovative new AI technologies are delivering benefits to a wide variety of industries, including financial services.

The success of artificial intelligence (AI) algorithms hinges on the ability to gain easy access to the right kind of data in sufficient volume. Put more simply, AI depends on good data. Even Google--which is famous for the pioneering work in AI that underpins its standard-setting search-based advertising business--makes no bones about the critical role of data in AI. Peter Norvig, Google's director of research, has said: "We don't have better algorithms, we just have more data." Companies increasingly realize that data is critical to their success--and they are paying striking sums to acquire it. Microsoft's US$26 billion purchase of the enterprise social network LinkedIn is a prime example. But other technology companies are also seeking to acquire data-related assets, typically to acquire more than just identity-linked information from social media sources by focusing instead on vast troves of anonymized consumer data. Think, for example, of Oracle pursuing an M&A-led strategy for its Oracle Data Cloud data aggregation service, or IBM buying, within the past two years, both The Weather Company and Truven Health Analytics. Early returns for companies making such investments are promising. Still, to unlock the full value of AI algorithms, companies must have access to large data sets, apply abundant data-processing power, and have the skills to interpret results strategically.

To ride the rising wave of AI, financial services companies will have to navigate evolving standards, regulations and risk dynamics--particularly regarding data rights, algorithmic accountability and cybersecurity. The success of artificial intelligence (AI) algorithms hinges on the ability to gain easy access to the right kind of data in sufficient volume. Put more simply, AI depends on good data. Even Google--which is famous for the pioneering work in AI that underpins its standard-setting search-based advertising business--makes no bones about the critical role of data in AI. Peter Norvig, Google's director of research, has said: "We don't have better algorithms, we just have more data." Companies increasingly realize that data is critical to their success--and they are paying striking sums to acquire it. Microsoft's US$26 billion purchase of the enterprise social network LinkedIn is a prime example. But other technology companies are also seeking to acquire data-related assets, typically to acquire more than just identity-linked information from social media sources by focusing instead on vast troves of anonymized consumer data. Think, for example, of Oracle pursuing an M&A-led strategy for its Oracle Data Cloud data aggregation service, or IBM buying, within the past two years, both The Weather Company and Truven Health Analytics.