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.
Provenir, a global leader in AI-powered risk decisioning software for the fintech industry, has found in its latest study that fraud prevention is the biggest driver for investments in AI-enabled risk decisions this year. The survey, which offers the views of 100 decision-makers from fintechs and financial services firms across Europe, found that other major drivers for investments in AI-enabled risk decisioning include automating decisions across the credit lifecycle (68%), competitive pricing (65%) and cost savings and operational efficiency (61%). The survey highlighted the role that alternative data can play in the fight against fraud, with 68% of those surveyed choosing to incorporate alternative data for the purpose of improving fraud detection. It also found that access to data is the biggest challenge to an organisation's risk strategy (88%), closely followed by a lack of a centralised view of data across the customer lifecycle (74%). "The risk of fraud has heightened across the entire financial services landscape, and with attacks only becoming more sophisticated and widespread, it is positive to see that more firms are turning to AI-enabled technologies to minimise these threats," said Carol Hamilton, SVP, Global Solutions at Provenir.
AI allows Financial service providers to strike the right balance between performance, underlying logic, accuracy, performance, and compliance with regulatory requirements! The recent pandemic accelerated the budding digitization trend around the use of AI. This stimulated global spending on AI to double up over the period 2020-24, growing from USD 50 Billion in 2020 to more than USD 110 Billion in 2024. AI creates a rush of opportunities in the financial sector for in-house, outsourced, or ecosystem-based projects but there are some inherent risks in the use of this technology. Such Fintech with AI has encouraged many mergers and acquisitions among financial service providers and wealth managers as they dredge with volatility, uncertainty, complexity, and ambiguity.
Arria NLG, a leading provider of natural language generation (NLG) technologies, has appointed Managing Director and Innovation Strategist, Mark Goodey, to cement Arria Investment Analyst as the Banking, Financial Services, and Insurance (BFSI) industry leader. Arria Investment Analyst uses natural language technologies to bring 100 percent accuracy to investment analysis and to create data-driven investment commentary. "I am excited to lead this initiative," said Goodey. "Arria's Investment Analyst uses natural language technology to analyze investment portfolio performance. It's a technology uniquely placed to support asset managers, asset owners, and the financial services industry, so what used to take hours or days can now be accomplished in seconds."
The graph represents a network of 1,483 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 20 March 2022 at 01:39 UTC. The requested start date was Sunday, 20 March 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 3-day, 6-hour, 59-minute period from Tuesday, 15 March 2022 at 03:22 UTC to Friday, 18 March 2022 at 10:22 UTC.
In recent times, technology has paved the way for automation and transformation in the financial services industry in India. Advancements within the fintech space significantly fosters innovation and powers financial institutions to offer digital services and retain relevance in the market. The fintech segment alone is projected to grow to $150-160 billion by 2025 and has enormous growth potential. To put it simply, this amalgamation of finance and technology in recent times has encompassed all kinds of technology leveraged with finance to service both - businesses and consumers. If one part of the economic ecosystem seemed averse to change but has embraced fintech in a big way is the traditional banking system.
Quantum Computing is a relatively new industry with much of what is known about the industry being theoretical. This hasn't stopped the individuals and companies on this list from driving the application of quantum computing in their various industries in order to solve problems, as well as to drive innovation and industry growth. Amira Abbas had always loved mathematics, but one YouTube video comparing stock market movement to particle movement in space and time via the core equation of quantum mechanics fascinated Abbas so much, that she spiraled into thinking about quantum physics all the time. After quitting her role in the financial services industry, Amia went back to school for a master's degree in Physics. Today, she works as a research advocate on IBM's Quantum Machine Learning (QML) Team where she conducts research with the rest of the IBM QML team on applying the potential of quantum computing to boost the performance of machine learning systems.
In this special guest feature, Kumesh Aroomoogan, Co-founder and CEO of Accern, believes that data is increasingly being used and desired and financial professionals need faster ways to take advantage of it. Founded in 2014, Accern accelerates AI workflows for financial enterprises with a no-code development platform and has raised $16m to date. In 2018 Kumesh was named to the Forbes 30 Under 30 Enterprise Technology list. Previously, he was the co-founder and CEO of BrandingScholars, an advertising agency, a General Accountant at the Ford Foundation, an Executive Board Member, Chairman of Public Relations at ALPFA, Equity Researcher at Citigroup, and a Financial Analyst at SIFMA. Companies are relying on data scientists to access the benefits of AI – which has only gotten more difficult as talent has become harder and harder to come by.
Digital transformation was taking place in fits and starts before the pandemic, but now digital initiatives are accelerating rapidly. Digitalization using new technologies including artificial intelligence and hybrid cloud are at the heart of this acceleration and this has been more rampant in the financial services sector, also driven by rapid technological innovation and quickly shifting customer preferences. During the COVID-19 global pandemic, the number one business priority was the safety and well-being of employees, businesses also worked overtime to meet the changing needs of clients. Companies turned to AI and machine learning to deliver novel digital customer journeys and eliminate unnecessary interventions in the most routine, repetitive, and paper intensive tasks. Virtual assistants became a critical tool for large organizations and governments during the pandemic. Nearly 43% of businesses globally accelerated their rollout of AI over the last year, according to IBM's 2021 Global AI Adoption Index, as organizations looked to virtual assistants to manage swelling call volumes and other similar pathways to automation.
The graph represents a network of 1,214 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 20 February 2022 at 02:41 UTC. The requested start date was Sunday, 20 February 2022 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 21-hour, 13-minute period from Thursday, 17 February 2022 at 03:16 UTC to Sunday, 20 February 2022 at 00:30 UTC.