Building out scenarios that reflect how markets evolve helps us understand an increasingly competitive future. This whitepaper walks you through our methodology and four scenarios: baseline, growth, decay, and new equilibrium.
Read moreThe vast digital gap between those with access, privacy, and security - and those without - will only become more profound if the concept of trust architecture isn’t inserted into our developments. Building and maintaining this trust is what will enable digital-first marketplaces to thrive.
Read moreAs ATB Financial (ATB) increases its investment in Artificial Intelligence (AI) via operations and products, it is committed to translating ATB values and principles into the decision-making processes and optimizations of AI. AI presents considerable opportunities to better serve customers, but it also encompasses particularly intricate challenges to value-based decision-making because it introduces complexity, uncertainty, and scale. These features mean that harm is more challenging to evaluate and pre-empt, and that even the smallest decision may have a large long-term effect that is difficult to detect and fix. We are investing in the opportunity AI presents to enhance customer experiences. The intent with this work is to build our capacities to: identify, assess, mitigate, and monitor the ethical dimensions in AI uptake - ensuring ATB leads by example in the ethical deployment of emerging technology.
Read moreIn order to build and maintain customer trust, financial institutions are investing millions of dollars in preserving privacy while experimenting with exponential technology to bring best value to their customers. With rapid advancements in artificial intelligence on all fronts, however, there is a dire need in the financial sector to balance customer privacy with the use of transaction data in training robust machine models. This white paper provides a rationale focused on data privacy and also includes suggested algorithmic recipes related to how transaction data could completely eradicate the use of real customer data. A distinction is drawn between data simulation and synthesis. Two discussed synthesis techniques include a bootstrapping framework featuring dynamic time warping and a hidden Markov model fitted with a stochastic gradient version of the Markov chain Monte Carlo method.
Read moreStrategic foresight and scenario planning is the discipline of uncovering and analyzing the nuances of potential future outcomes in order to better equip businesses to recognize and adapt to unprecedented change. The scenarios outline the impacts of key uncertainties and they help scenario participants confront their personal blindspots in how they believe the path ahead will be laid out.
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