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  1. Sensibill: Canadian fintechs hope U.S. pandemic partnerships will set an example at home (The Logic)

“Corey Gross, CEO of Sensibill, a Toronto fintech that uses AI to digitize financial management that works with hundreds of financial institutions in the U.K and U.S., sees the pandemic forcing banks to reallocate budgets to innovation, and pushing them to look to fintechs “to bolster the customer experience” that address needs beyond banking.

In June, Sensibill announced a partnership with Chase to provide digital receipt management to its 38 million active mobile users — about equivalent to the population of Canada. The deal had been in the works for a long time, but indicated a larger shift for Gross: that banks want to “find cost-effective and time-effective ways of delivering relief and more.”

“There is going to be a look towards fintechs to help people that are particularly susceptible to cash flow issues as a result of the pandemic,” Gross said. “I think everybody wants to know-how solutions that are available are tailored to solve their problem…. We shouldn’t wait for the pandemic end to hasten our delivery of innovative solutions to customers, because now is when they’re going to need it.”

Radical Commentary: Our portfolio company Sensibill has partnered with JP Morgan Chase to provide its 38 million mobile customers with Sensibill’s digital receipt management solution. The product has seen strong adoption, reflective of broader digital adoption trends during COVID.

Sensibill’s machine learning reads physical and digital receipts and automatically organizes the data to generate useful insights and auditing tools. Global financial institutions are turning to Sensibill’s data-driven, customer-facing solution to help SMB and consumers manage their purchase receipts, reconcile their bank statements and file their taxes.

Global adoption of FinTech services for consumers has moved steadily upward, from 16% in 2015 to 64% in 2019, mainly driven by the mass adoption of smartphones and the creation of new digital markets. COVID is accelerating this trend as surveys suggest that 28% of digital banking customers in the U.S. adopted digital tools for the first time in response to the pandemic.

2) Generative Models: Swapping Autoencoder for Deep Image Manipulation (Arxiv))

“Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging. We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to encode an image with two independent components and enforce that any swapped combination maps to a realistic image.”

Radical Commentary: Using generative models, researchers from UC Berkeley and Adobe Research have created a photo editing tool that can make changes such as weather, time of day, and surface material, to a high-resolution photograph. While this is generally the technology used in deep fakes, it also has many creative applications. In this setting, we can easily envision such a tool enhancing the abilities of photo editors, video game designers, ad agencies, animators and creatives in many industries.

3) AI Fraud Detection: Researchers propose AI for detecting fraudulent crowdfunding campaigns (Venture Beat)

“…as crowdfunding platforms have risen to prominence, they’ve also attracted malicious actors who take advantage of unsuspecting donors. Last August, a report from the Verge investigated the Dragonfly Futurefön, a decade-long fraud operation that cost victims nearly $6 million and caught the attention of the FBI.

…researchers at the University College London, Telefonica Research, and the London School of economics devised an AI system that takes into account textual and image-based features to classify fraudulent crowdfunding behavior. …This is a significant step in building a system that is preemptive (e.g., a browser plugin) as opposed to reactive.”

Radical Commentary: Employees monitoring websites and social platforms cannot keep pace with the amount of data being generated on the Internet. However, AI can automatically detect fraud signals to narrow the funnel for overburdened analysts. Although the Kickstarter team was able to suspend 354 projects in 2018 for violating the company’s rules and guidelines, malicious actors quickly learn which behaviours are appearing as signals and adapt to bypass detection.

We’ve seen that fraud detection and prevention is an ongoing adversarial problem for businesses. More companies are using AI approaches in their identity management and KYC solutions to establish a baseline for both common and individual user behaviour. These solutions are dynamic, allowing for anomaly detection that is capable of incorporating new behaviours into detection mechanisms. In the future, the vast majority of fraud detection will be performed by AI.

4) Remote Work and Access to Talent: Will Facebook’s salary-by-location move set precedent for tech (Financial Times)
 
“Facebook announced in May that up to half its workforce is likely to be working from home within the next five to 10 years. There is one caveat: staff salaries could be adjusted to align with the cost of living in their chosen location, meaning potential pay cuts for those considering moving away from its expensive Palo Alto base and other global hubs…Does it matter if your team lives in California or Utah if they are expected to deliver similar results?”

Radical Commentary: With Big Tech embracing the idea that working from home is here to stay, there are questions about the impact on global talent pools and ultimately employee salaries. While it seems likely that trends towards working from home will discourage talent clustering in expensive locations such as Silicon Valley, reductions in compensation for those who choose to move may create an opportunity for startups to hire that talent.

From our perspective, this decentralization of talent is only going to accelerate the emergence of world-class start-ups outside of Silicon Valley. Ecosystems with top universities, a critical mass of tech companies and technical and business talent, available capital and markets will continue to thrive and grow. With the third-largest tech workforce in North America after the Bay Area and the Seattle area, we believe Toronto/Waterloo (and Canada generally) is primed to continue its tech ascent.

Editor’s Note: We will continue to use this platform to share without commentary articles focused on data and the use of it to illustrate and illuminate racial injustice. Because you cannot fix problems you cannot see or understand.

5) What the data say about police brutality and racial bias — and which reforms might work (Nature News Feature)

“As protests have spread around the globe, the pressure is on police departments and politicians, particularly in the United States, to do something — from reforming law-enforcement tactics to defunding or even abolishing police departments.

And although researchers are encouraged by the momentum for change, some are also concerned that, without ample evidence to support new policies, leaders might miss the mark. Many have been arguing for years about the need for better data on the use of force by the police in the United States, and for rigorous studies that test interventions such as training on how to de-escalate tense interactions or mandating the use of body-worn cameras by officers. Those data and studies have begun to materialize, spurred by protests in 2014 after the deadly shooting of Michael Brown in Ferguson, Missouri, and the death by chokehold of Eric Garner in New York City.”

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