Nick Tap

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On November 29, 2018, Nick Tap commented on Alexa, What’s in my Wallet? Open Innovation at Capital One :

I don´t have much background knowledge into the limitations and control that can be configured in an API when making it open. I started reading this article because i felt reluctant at first about the idea of using open innovation for banking, being one of the industries where security is needed the most. After reading some of the initiatives, that i relate more to peripheral services around the baking platform I completely changed my mind and appreciated the value of their initiative.
With my limited understanding i’d say that at the beginning not much limits are needed, but as time goes by and more and more ideas and apps appear, there needs to be some sort of moderation given the importance of “no errors” and security in the industry.

I loved Lego, and it was a crucial part of my childhood, so the article captured my attention immediately. I didn´t even know they struggled at some point, but the way they overcome that difficulty brought me back in time even more! I’d have loved to be able to comment on the ideas to be launched or to propose new ideas that i wanted to see in future Legos.
Still i’d love to know more about the process of how they captured those ideas and brought them to life, as not every famous character can easily be reproduced into a Lego.

I found the article very interesting, considering the restrictions and flaws that the credit industry has historically faced. Predicting borrowers behavior is per se an extremely challenging task, not only because it involves many external factors out of the control of the lender (such as economic conditions or regulators, competitors, and borrowers unexpected behaviors), but also many internal ones that rely on the effectiveness of the credit approval processes and the objectiveness of the analysts involved, among others. On the one hand, introducing machine learning to the credit assessment process can effectively address, at least, the flaws and variability of the internal factors, by reducing the dependence on the judgment of the analysts, therefore eliminating possible biases. On the other, machine learning can also be extremely useful to better predict the external factors, mainly those related to borrower behavior during financial stress, but the effectiveness of the LendingClub algorithms will not, for me, still fully proven until we can see the accuracy during crises times. Additionally, other challenges that the system may face if the company plans to escalate and expand, relate to the replicability of the model to markets with significant cultural differences, lack of reliable historical data, or with volatile economies.

Very interesting to see how 3d printing can be used for mass customization, given the big trend driven by millennials who require a customized experience in almost everything they do.
To the questions you’ve asked, yes I do think this is a great product to be produced through 3d printing and they eventually should develop it in-house. The cost will eventually go down and be even more competitive than any other way of producing razors and the “customization” concept is a great marketing strategy.
My main concern around this is if this top notch technology adds any value to the product itself. It is clear that customizing your razor will look cool and specially millennials would love it, but is there any room for improvement in the razor quality? It seems that regardless the improvements done within these razors, safety razors provide a better shave in the end and the difference across current razors is not much, so how much can we improve the product isn´t clear.

On November 29, 2018, Nick Tap commented on Streamlining Humanitarian Aid with Additive Manufacturing at Oxfam :

You mixed two topics that I’m extremely interested about, in a way haven´t heard before. While I was aware of 3d printing and many of its uses and advantages, i’ve never thought about it to solve natural disaster emergencies or re-build places after a war. The datapoint about 80% of the aid spent in logistics really made your main point about the advantage of 3d printing much stronger. Based on the characteristics of 3D printing, I see it can have a long term impact similar to internet and the computer, allowing the next level of “globalization”. I have two main questions that come to my mind when thinking about how 3d printing will shape our future. First, what is going to happen with the uses of 3d printing where it replaces manual labor. Secondly, how will the logistics industry will be affected, as we could easily reach a point where moving the machine is cheaper than moving the products.

The article is very illustrative and relevant in today’s context, where governments around the world are investing resources and regulation to push bancarization in the pursuit of increasing more equality of opportunities, lower predatory lending, and mainly, more efficacy to tackle tax evasion and money laundering. Implementing a machine learning based system to address compliance related issues is therefore not only positive for the bank but also for the national authorities that will see this change with positive eyes.
As the article states, the Commonwealth Bank of Australia and more broadly all the financial system, has been both 1) investing a lot of money in managing the compliance processes, heavily monitored by regulators, and 2) spending significant resources in fines when their KYC processes are not effective. I believe that introducing this system will very positive to the bank, not only by increasing the efficacy in the evaluation/control of the operations, but also the efficiency, allowing to streamline the tedious processes of multiple background checks and interpretation of vast and changing regulations in all the different markets it operates, among others. For me, the main remaining open question lies on whether the analysts in charge of the risk management processes will provide the system with the correct and full inputs necessary for a satisfactory evaluation, and more importantly, whether the analysts fully understand the underlying of the algorithms and the meaning of the system outputs. If the bank is able to tackle these potential issues, and make sure that trained users will be able to effectively use the system outputs and invest their time in value-added activities that will enhance the analyses, the efforts of innovating in the risk management arena will pay off.