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.
Regulatory compliance is timeconsuming and expensive for both financial institutions and regulators. The volume of information that parties must monitor and evaluate is enormous. The rules are often complex and difficult to understand and apply. And much of the process remains highly labor-intensive, when even the most automated solutions are often incompatible with other systems and, even today, most still depend heavily on manual inputs. As a result, costs have risen significantly for financial institutions in recent years.
If your professional interests take you to the crossroads of financial services, regulation, compliance, and digital - especially data analytics and machine learning - which altogether is known as regtech, you are in the right place. You are part of statistically small and very geek-oriented professional community, but you know this, and though you might choose not to admit this to strangers at this year's festive parties for fear of causing great pain by boredom, you are in good company with this Contributor and my interviewee. I first met Jo Ann Barefoot when I was chairing the U.K. Financial Conduct Authority (FCA) Industry Sandbox Consultation, where she provided excellent guidance and insights. Jo Ann is one of the most dedicated and busiest advocates of the regtech space on the planet and is truly outstanding in both her knowledge and passion in this area. She dedicates her time to a number of global bodies and initiatives related to regtech: she is a Senior Fellow Emerita at the Harvard Kennedy School Center for Business & Government, a Senior Advisor to the Omidyar network, sits on the fintech advisory committee for FINRA, is an Executive Board Member of the International RegTech Association (IRTA), is a member of the Milken Institute U.S. FinTech Advisory Committee, and chairs the boards of the Center for Financial Services Innovation and FinRegLab.
Much of the software now revolutionizing the financial services industry depends on algorithms that apply artificial intelligence (AI)--and increasingly, machine learning--to automate everything from simple, rote tasks to activities requiring sophisticated judgment. These algorithms and the analyses that undergird them have become progressively more sophisticated as the pool of potentially meaningful variables within the Big Data universe continues to proliferate. When properly implemented, algorithmic and AI systems increase processing speed, reduce mistakes due to human error and minimize labor costs, all while improving customer satisfaction rates. Creditscoring algorithms, for example, not only help financial institutions optimize default and prepayment rates, but also streamline the application process, allowing for leaner staffing and an enhanced customer experience. When effective, these algorithms enable lenders to tweak approval criteria quickly and continually, responding in real time to both market conditions and customer needs. Both lenders and borrowers stand to benefit. For decades, financial services companies have used different types of algorithms to trade securities, predict financial markets, identify prospective employees and assess potential customers.
Data Push: Push-based strategies are the default model. Automated the delivery on pre-determined specification, a forwarder is installed close to the source of the data, or built into the data generator/collector and pushes the events to an indexer. Data Pull: This approach provides significant flexibility by letting you create reports from multiple data sources and multiple data sets, and by letting you store and manage reports with an enterprise reporting server. Pull based cannot be reliable for real-time reports and information. Also, Pull base system most tolerate, its lack of real-time information cannot be best fit for supervisory Financial Institution as they demand real-time reporting with greater insights to financial health conditions of FIs. Supervisors can use machine learning tools to create a "risk score" for supervised entities. FINTRAC, the Financial Transactions and Reports Analysis Centre of Canada, has created one such score, evaluating the risk factors related to an institution's profile, compliance history, reporting behavior, and more.