Great piece! Nike and Under Armour also seem to be heavily focused on additive manufacturing (https://www.baltimoresun.com/business/under-armour-blog/bs-bz-under-armour-customize-20171206-story.html), which makes me believe that additive manufacturing will become the new norm. While going all in would push them further ahead of the competition, I do think there is a strong possibility that jumping in could cause them to crash and burn. An incremental transition would allow them to test and learn cost efficient ways to produce while still ensuring quality. The industry is also relatively new, which means that the costs of machinery could also start to decrease over time as new competition enters the market.
Another concern I have is the level of customization. As 3D printing becomes more prevalent and customers become more adept at designing their own shoes, is there the potential to completely displace brands like Adidas, Nike, and Under Armour? Or for professional or amateur designers to offer and easily produce their own designs, increasing consumer choice? I wonder if 3D printers could start just printing shoes designed by customers themselves, based off base models (sneaker, high heel, boot, etc), and thus making brands irrelevant.
I hadn’t realized that Amazon was using open innovation to maintain its competitive advantage in the home smart speaker market. As Apple demonstrated with its app store, I think opening Alexa up to developers that can further Alexa’s capabilities and thus create its utility will be a key advantage as it looks to keep its leader position. However, I do wonder if open innovation is the correct approach when it comes to improving Alexa’s language capabilities. Should Amazon instead explore an M&A acquisition of start-ups that already have the capabilities to do real-time language translation/processing? While Google and Microsoft are definitely at the forefront of these technologies, start-ups like SayHi (https://www.sayhitranslate.com/) and TripLingo (http://www.triplingo.com/) may offer a great foundation from which Amazon can build on to quickly expand the number of languages Alexa can support. From my point of view, acquiring the capability may be a better way for Amazon to accelerate development.
Excellent insight into open innovation as a defensive tool for entrenched players to protect against potential disruptors. You noted the importance of “fintech challenges” to bring external, out-of-the-box thinking to the company. I also wonder whether the “fintech challenges” could also be used as a tool to identify and recruit top talent that think differently from the typical bank employee. Rather than just providing funding, Aval could incubate “fintech challenge” winners in-house or recruit any identified top talent to “entrepreneur-in-residence” or an innovation departments. Another consideration for Aval given the openness of regulation to Fintech innovation would be open its APIs to encourage innovation by engaging deeper with the broader Fintech community. Open banking could be an additional source of revenue for Grupo Aval, improve the customer experience, and create additional exposure to out-of-the-box thinking . The model has exploded across the US, Europe, and Asia . I think opening up its APIs and using the “fintech challenges” to recruit top talent will help Grupo Aval sustain its leadership position.
 Brodsky, L. and Oakes, L. Data Sharing and Open Banking. McKinsey & Company. https://www.mckinsey.com/industries/financial-services/our-insights/data-sharing-and-open-banking (September 2017). Accessed November 14, 2018.
Thank you for your great insight into machine learning at American Express! While I found American Express’ efforts to reduce fraud impressive, I do wonder if this is a go-forward requirement to stay competitive in the market. Capital One, for instance, is also leveraging machine learning to reduce fraud (https://partners.wsj.com/aws/capital-one-rethinking-fraud-protection-machine-learning/ ). Amex also seems to be exploring broader applications of machine learning via its investments arm. For instance, Amex Ventures has made investments in Enigma, which leverages data and machine learning to connect internal and external data. In Financial Services, this data can help with compliance programs and underwriting. Their investment in RetailNext, a startup focused on using deep learning and AI to support physical store retailers, showcases a dedication to provide broader support to struggling retail clients.
Excellent piece on Machine Learning in the Financial Services Industry! I agree with your assessment that banks need to ensure that their machine learning algorithms are not violating fair lending laws with any type of implicit discrimination; an additional consideration would be whether to allow machine learning algorithms to “experience” all types of loan outcomes, so as to build better decision making capabilities in the future. In other words, should Santander temporarily accept all loan applications, knowing they would incur higher losses in the immediate future, so that their machine learning capabilities can learn what a good application looks like, potentially improving longer term financial performance? The alternative, is to start by configuring the algorithm according to its current underwriting standards, which may risk incorporating Santander’s own specific biases. Small business lending startups have taken the approach of initially accepting a very broad pool and found success in relatively low loss rates as the algorithms adjust, after a brief period of high losses.