Photo credit: Alex Krugli
McKinsey recently released a report on Building the AI Bank of the Future, detailing how banks must use data and AI technologies to improve customer engagement and satisfaction – key metrics for long-term shareholder returns. It is a timely report as most traditional banks are in the midst of profound digital transformations that touch their entire capability stack.
To a certain degree, banks are playing catch-up with consumer tech. As anyone who has glanced at their Netflix recommendations knows, customers leave behind signals in their digital journeys. When captured and understood, these signals can be used to create superior personalized experiences. By automating the customer journey, banks are looking to surface insights more readily, while engaging customers and reducing costs.
Many fintech startups are seizing upon this opportunity, providing specialist services within the capability stack, including data platforms and modern architecture to provide a 360-degree view of customers, and AI algorithms to power decision making. While banks are seeking AI technologies that are deployable at scale to remain relevant, some digital-first players are discovering that the data and network moats generated by products such as digital payments are opening the door to providing more traditional financial services. Whether it is created by incumbents or startups, the foundations of the bank of the future will be built upon technologies that better understand the customer.
5 Noteworthy AI and Deep Tech Articles: week of May 31, 2021
1) Development and validation of a machine learning model using administrative health data to predict onset of type 2 diabetes (JAMA Network Open)
A research group including Radical Ventures and Vector Institute for AI co-founder Tomi Poutanen, Vector Faculty Affiliate Laura Rosella, and data scientists at Layer 6, were able to use AI to predict type 2 diabetes five years before onset using administrative health data. With this research, population health planning tools can leverage administrative data to determine high-risk groups and to guide targeted interventions for diabetes prevention.
2) Opinion: AI has a compute problem. There’s a Canadian approach to solving it (The Globe & Mail)
AI has a bottleneck – soaring demand for computing power. Garth Gibson, President and CEO, and Rod Bodkin, Vice-President of AI Engineering and CIO, of the Vector Institute discuss the cause and consequences of soaring AI compute demand and how Canada is set up to solve the problem.
3) Getting AI to scale (Harvard Business Review)
Too often organizations try to adopt AI in long lists of discrete use cases or in areas that have little relevance to main business objectives. The alternative – overhauling the whole organization with AI all at once – is simply too complicated to be practical. There are four steps to successfully scaling AI discussed in this article: identifying an area where AI will make a big difference reasonably quickly, staffing the team with the right people and mandates, reimagining business as usual, and supporting new AI-based processes with organizational changes.
4) Can your enterprise benefit from no-code AI? (Entrepreneur)
The low and no-code movement aims to help users develop applications without knowing how to code. This makes development more accessible and affordable as you don’t need to have a computer engineering background. Users in all industries can implement strategies for extracting data and using it to reach their business objectives while serving customers. Cohere, a Radical Ventures portfolio company, is a powerful example of a low-code platform, providing every business access to the most advanced natural language processing for machines via its API.
5) Alán Aspuru-Guzik talks about the launch of Digital Discovery (Royal Society of Chemistry)
Alán Aspuru-Guzik, a Radical Ventures Scientific Advisor, introduced Digital Discovery, an open access journal aiming to capture the top research at the intersection of chemistry, materials science and biotechnology. Drawing on topics related to chemistry, biology, physics and materials, the journal will cover the application of machine learning to solve scientific problems.
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