Snippings

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In 2019, six Nordic banks decided to create such a utility and call it Invidem. On April 24th, 2023 they also decided to cease its operations and terminate its operation. Four years and EUR ~60mn later, according to the Swedish press, that effort came to a sad conclusion. How is that possible?

From The sad conclusion of an effort to make bank customers happier (and banks and regulators too!) | norbloc:

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Rohit Chopra

The CFPB is working to accelerate the shift to open banking through a new personal data rights rule intended to break down these obstacles, jumpstart competition, and protect financial privacy. To do this, the CFPB is formalizing an unused legal authority enacted by Congress in 2010. This authority gives consumers the right to control their personal financial data. These rights will become a practical reality after the CFPB implements a rule that sets expectations for the market. We expect to solicit comments on our formal proposal in a few months and finalize in 2024.

From Laying the foundation for open banking in the United States | Consumer Financial Protection Bureau:

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Westpac saw a 46 percent productivity gain, with no reduction in code quality, from software engineers that were aided by generative AI compared to a control group that performed the same tasks exclusively by hand, in a recent in-house experiment.

From Westpac sees 46 percent productivity gain from AI coding experiment – Finance – Software – iTnews:

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The issue of what exactly these things are, in legal terms, led the Law Commission to recommend legislation to define them. They are not physical “things in possession” such as cars and nor are they “things in action” like stocks and bonds. They are what I would call “things in wallets” but the Law Commission uses the term “third category things”.

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To make an ML model learn the wrong thing, adversaries take aim at the model’s training data, any foundational models, or both. Adversaries exploit this class of vulnerabilities to influence models using methods, such as data and parameter manipulation, which practitioners term poisoning. Poisoning attacks cause a model to incorrectly learn something that the adversary can exploit at a future time. For example, an attacker might use data poisoning techniques to corrupt a supply chain for a model designed to classify traffic signs. The attacker could exploit threats to the data by inserting triggers into training data that can influence future model behavior so that the model misclassifies a stop sign as a speed limit sign when the trigger is present

From The Challenge of Adversarial Machine Learning.

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Explainable AI is one of the key requirements for implementing responsible AI, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability.³ To help adopt AI responsibly, organizations need to embed ethical principles into AI applications and processes by building AI systems based on trust and transparency.

From What is Explainable AI (XAI)? | IBM.

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A bank customer was left stunned when a teller told her they did not have any cash within the branch to meet her withdrawal request, prompting her to close her account on the spot.

Taryn Comptyn had gone to her bank to withdraw $3,500 to pay for renovations

From ANZ Australia no longer keeping cash at some branches | Daily Mail Online.

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Soon, though, the web might consist not only of what was there in the pre-AI era, but all the stuff created by current and future chatbots. Which raises the intriguing possibility of an online world populated by bots inhaling the textual exhaust of their mechanical peers, and a consequent spiral into the infinite recursion that programmers call “stack overflow”!

From Chatbots are social media on steroids – trapping us in an even more tangled web | John Naughton | The Guardian.

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Boston Consulting Group reports that asset tokenization can generate annual savings of $20 billion in just the global clearing and settlement costs. By 2030, it could unlock a $16 trillion global market for tokenized illiquid assets, which would account for less than two percent of the total notional value of public and private assets.

From Asset Tokenization – A Trillion Dollar Market Opportunity.

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Non-Blockchain Tokenization is used to safeguard data. It substitutes sensitive data with random tokens that hold no inherent meaning or exploitable value. These tokens act as references or identifiers, facilitating the mapping back to the original sensitive data using a tokenization system. The method is widely used to secure diverse types of sensitive data, including financial transactions, health records, criminal histories, vehicle driver details, loan documents, stock trading, voter registration, and more.

 

Blockchain Tokenization, on the other hand, converts assets into digital token securities, which act as a proxy for whole or fractional ownership of the underlying asset. Blockchain, smart contracts, and token securities are pivotal in enabling illiquid asset fractionalization by programmatically enforcing the required rules and restrictions, allowing for enhanced mobility of asset ownership, efficient payments and distributions, and a range of financial benefits across the lifecycle of the transactions.

From Asset Tokenization – A Trillion Dollar Market Opportunity.

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