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The global financial crisis of 2007 has had a long-lasting effect on the economies of many countries. In the epic financial and economic collapse, many lost their jobs, savings, and much more. When too much risk is restricted to very few players, it is considered as a notable failure of the risk management framework. Since the global financial crisis, there has been a rapid increase in the popularity of terms like “bankruptcy,” “default,” and “financial distress.” The process of forecasting distress/default/bankruptcy is now considered an important tool for making financial decisions and the main driver of credit risk. The Great Depression of 1932, the Suez Crisis of 1956, the International Debt Crisis of 1982, and the latest 2007 recession have pushed the world to try and figure out the prediction of the downfall as early as possible.

Source: Image by pikisuperstar on Freepik

Predicting financial distress and bankruptcy is one application that has received much attention in recent years, thanks to the excitement surrounding Financial Technology (FinTech) and the continuing rapid progress in AI. Financial institutions can use credit scores to help them decide whether or not to grant a borrower credit, increasing the likelihood that risky borrowers will have their requests denied. In addition to difficulties caused by data that is both noisy and highly unbalanced, there are now new regulatory reforms that must be met, like General Data Protection Regulation (GDPR). The regulators expect that the model interpretability is there to ensure that algorithmic findings are comprehensible and consistent. As the COVID-19 pandemic hurt the financial markets, a better understanding of lending is required to avoid a situation like 2007.

Balance: Interpretability and Accuracy

The credit scoring models or bankruptcy prediction models, which predict whether an individual will return the lender’s money or if a company will file for bankruptcy, must address high accuracy and interpretability. Two friends of mine from the same University in the US, with approximately the same salary, the same age, and many more equivalent parameters, applied for a home loan. One got his loan approved, while the other one faced a rejection. Don’t you think the rejected one deserves an answer? What if a corporation has applied for a loan for expansion, but the bank decided not to offer credit?


With the growth in several relevant independent variables (sentiment score from financial statements and Ratios), an obvious interpretation difficulty has emerged. In the past, a minimal number of independent variables and a basic model were sufficient for the model to be easily interpreted. As a result, research aiming to pick the most significant variables and simulate insolvency based on the chosen features and a simple statistical model gained much popularity. Using machine learning algorithms is another method for dealing with features that are not always limited in number. 

Source: Image by Guo et al (2024) – Research Gate

Accuracy Explainable AI for Humans – XAI

Source: Image by Caltech

Source: Image by freepik on Freepik

When presented with an ML-based model, XAI techniques may be used to audit the model and put it through its paces to see whether it can consistently provide accurate results across different use cases related to credit scoring and distress prediction. These techniques, for instance, assess the model’s prediction rules for consistency with previous information about the business problem, which may help reveal challenges that may hamper the model’s accuracy when applied to out-of-sample data. Problems in the dataset that are used to train the model, such as an incomplete or biased representation of the population of interest or training conditions that lead to the model learning faulty forecasting rules, may be uncovered by XAI techniques.

Post-Covid, XAI has taken massive strides to address credit-related business problems:


artificial intelligence-based technology allows lenders to tweak models for fairness by lowering the impact of discriminatory credit data without compromising performance. 


has publicly stated its intention to supplement human domain understanding with machine intelligence to enable the quick development of highly predictive and explainable credit risk scoring models.


frees lenders from biased datasets and helps them to analyze financial data more efficiently. Ocrolus’ software analyses bank statements, pay stubs, tax documents, mortgage forms, invoices, and other documents to determine loan eligibility for a mortgage, business, consumer, credit scoring, and KYC.

used non-linear algorithmic modeling to estimate lending risk in areas with minimal or no credit bureau use.


launched transparent XAI models delivered as SaaS to help banks and credit unions speed up digital onboarding and loan processing to tackle the Post Covid challenges about the lending industry.

Models: Modern vs. Conventional

The modern techniques of applying machine learning models to credit scoring for default prediction in the financial sector have undoubtedly resulted in greater predictive performance than classic models such as logistic regressions. New digital technologies have boosted the adoption of machine learning models, enabling financial institutions to acquire and use bigger datasets comprising many features and observations for model training. Machine learning approaches, as opposed to conventional models such as the logit model, may experimentally discover non-linearities in the connections between the outcome variable and its predictors and interaction effects among the latter. As a result, when the training dataset is big enough, ML models are thought to beat classical statistical models used in credit assessment regularly. As a result, higher accuracy in default forecasting may benefit lending institutions through fewer credit losses and regulatory capital-related savings.

Role of Regulators

Source: Image by freepik on Freepik

 Actionable Insights For The Customers

The XAI techniques for credit-related models easily accommodate binary consumer decisions, including “lend” and “don’t lend.” The explanations provided by the XAI techniques may not consider crucial facets of lending, such as the interest rate, the repayment schedule, credit limit changes, or the customer’s loan preferences. However, financial institutions using XAI would need to inform customers why their loan applications were denied and how to improve their credentials. Imagine the customer is suggested to get an increase in his salary or educational qualifications or is asked to pay his credit card bills on time for a few months to get the loan approval through. This is nothing but actionable information a customer can make use of and reapply for the loan. 

 Success is Not Linear, or Is It?

Numerous financial institutions are now investigating inherently interpretable algorithms, such as linear regression, logistic regression, explainable gradient boosting, and neural networks. Consistently, there is a desire to investigate new techniques for developing models that are transparent by design and require no post-hoc explanation. Explaining pre-built models in a post hoc fashion is the other part of the story. A deep neural network with, say, 200 layers or a black box model passes inputs to the explainer algorithm, which further breaks the complex model into smaller, simpler pieces that are easier to comprehend. These simpler outputs would also consist of a list of features and parameters that are significant to the business problem. In both the above scenarios, a trade-off between high accuracy and interpretability is the need of the hour. 

Source: Image by Ajitesh Kumar on Vitalflux

SHAP (SHapley Additive exPlanation) and LIME (Local Interpretable Model-agnostic Explanations) are widely used explainability approaches. SHAP uses Shapley values to score model feature influence. Shapley values consider all feasible predictions using all inputs. SHAP’s thorough approach ensures consistency and local correctness. On the other hand, LIME constructs sparse linear models around each prediction to describe how the black box model operates locally.

More importantly, what is more relevant, an accurate model or a model that can be easily understood by businesses as well as customers? If the data is linearly separable, we can create an inherently interpretable and accurate model. But if the data is complicated and the decision boundaries aren’t straight, we might have to look at the complicated model to ensure it’s right and then think about post-hoc explanations.


In an ideal world, explainability helps people understand how a model works and when to use it. Lending institutions, regulatory bodies, and government must collaborate to develop AI guidelines protecting customers’ interests. If different groups don’t make the goals of explainability clear, AI will serve the interests of a few, as during the 2007 Global Financial Crisis. A well-designed XAI solution considers stakeholders’ needs and customizes the explanation. AI and Machine Learning algorithms have already revolutionized the operations of lending institutions. Some of the key takeaways about XAI are as follows:

Model accuracy/performance versus interpretability – XAI aims to make AI models more intuitive and comprehensible without sacrificing accuracy/performance.

XAI implementation and scaling – Inexplicability has long prevented lending institutions from fully utilizing AI. However, XAI has helped institutions not only with smooth onboarding as well as personalization.

Responsible AITransparency – How the credit model reached an outcome and using what parameters.

Informativeness (giving humans new information to help them make decisions), and Uncertainty estimation (figuring out how uncertain a result is) (quantifying how reliable a prediction is).


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Bu Shares Credit For Big Discovery Of Small Particle

“I feel pretty excited and very, very lucky,” says Bilsback (CAS’13), who along with most of the other BU students opted to watch a live feed of the 9 a.m. announcement rather than queue up before dawn to hear it in person. Bilsback is fortunate in many respects—BU is the only university to offer an academic-year undergraduate internship program at the Large Hadron Collider (LHC), the firmament of particle physics research. Some people waited all night to get into the CERN auditorium to meet a lineup of particle physics luminaries, among them Peter Higgs, the University of Edinburgh professor emeritus who proposed the existence of the Higgs boson in the 1960s.

Kevin Black, a College of Arts & Sciences assistant professor of physics, was there too, and he describes an atmosphere of “elation and jubilation.” Black has been working on the LHC since 2005, three years before it was officially completed. “One thing I should point out is that in ‘big science’ there can often be 10, 20, or even 30 years between the conception, design, and execution of a successful experiment, many of which end in disappointment,” Black says. “When you get a big result—those are typically far and few between.” There are people who have searched for the Higgs for their entire professional careers. Black reports that Higgs himself, now 83, was in tears when he saw the data.

Currently being hailed, deciphered, analyzed, and demystified in scientific and lay press around the world, last Wednesday’s news might best be summed up as the result of supercomputer number-crunching on an epic scale. The data in the Higgs boson quest have been long in coming and will continue to flow, but according to CERN, “the 2012 LHC run schedule was designed to deliver the maximum possible quantity of data to the experiments before the ICHEP conference, and with more data delivered between April and June 2012 than in the whole 2011 run, the strategy has been a success.” And the confirmation level of the data consistent with Higgs boson is 5 sigma, which in the particle physics world means that the chance that the information is wrong is only one in 3.5 million. The massive LHC is the flagship project of CERN (Conseil Européen pour la Recherche Nucléaire), now officially called the European Organization for Nuclear Research, a joint venture recognized by the scientific community as the world’s largest particle physics center. The LHC, a 16-mile underground vacuum tube lined with 4,000 of the world’s most powerful superconducting magnets straddling the Franco-Swiss border, can accelerate two beams of protons so they collide at close to the speed of light, creating explosions of particles similar to the immediate aftermath of the Big Bang. Researchers analyze the debris of the fleeting particles as they decay.

Exceeding its design specifications, the LHC computing grid has analyzed an unprecedented torrent of data to pick out Higgs-like events from the millions of collisions occurring every second. In fact, in the two weeks preceding last week’s announcement, researchers analyzed about 800 trillion proton-proton collisions that had occurred over the last two years.

Lawrence R. Sulak, David M. Myers Distinguished Professor of Physics and director of BU’s internship program, who is on sabbatical at CERN, was among the 3,000 signatories to the July 4 research update that shook the world. “If the accelerator performs as anticipated, by the end of this run in February 2013, we hope to verify whether the new particle is the boson of the Standard theory,” says Sulak.

The Standard theory Sulak refers to is the Standard Model of particle physics, for which Sheldon Glashow, Arthur G. B. Metcalf Professor of Mathematics and Science, shared the 1979 Nobel Prize in Physics. Glashow’s theory has been extended by colleagues Andrew Cohen and Kenneth Lane, CAS physics professors, and Martin Schmaltz, an associate professor. The Standard Model of particle physics, sometimes called “the theory of everything,” concerns the interactions that mediate the behavior of subatomic particles. Long-standing questions revolve around shortcomings in the Standard Model, which lists the simplest particles known to exist (such as electrons, muons, and quarks) and describes how three fundamental forces—electromagnetism, the strong force that holds together the nuclei of atoms, and the weak force that underlies radioactive decay—act on them. But the Standard Model neglects gravity, and it offers no explanation for “dark matter,” a phenomenon indicating that most of the universe’s mass is invisible because it doesn’t emit light. The existence of the Higgs boson and the related Higgs field would provide the missing piece of the model and solve one of physics’ persistent mysteries—why some subatomic particles, like the quarks that make up protons and neutrons, have mass and others, such as electrons, are super-light.

Tulika Bose, a CAS assistant professor of physics and a 2012 Sloan Fellow, is also working at CERN and called the July 4 developments groundbreaking. If the particle proves to be something more exotic than the Higgs boson, that would be “particularly exciting,” says Bose, “since it would revolutionize our current understanding of particle physics.” It may also help answer some of the fundamental questions facing particle physics today, she says, and in any case, the LHC results will inspire physicists to arrive at “a more complete theory that includes everything.”

The idea of sending undergraduates to be part of this sweeping effort was born five years ago, when, “with the nexus of particle physics having moved from the United States to Geneva,” Sulak says, “we realized that BU students should be trained at CERN, where all the action is.” Funded by BU and the U.S. Department of Energy, the eight-month stints (Bilsback and several others have opted to stay on at CERN through the summer) constitute the only such program in the world, according to Sulak. In the program’s three years of operation, 23 BU undergraduates have participated, according to Sulak.

Bilsback, who plans to earn a PhD in physics, is the only BU intern working on CERN’s ATLAS detector, the LHC experiment providing most of the data for Higgs. And with the LHC being the Mecca of particle physics, great minds are everywhere. “It was a bit intimidating at first; everyone is a doctoral or postdoc student,” says Bilsback, who like fellow CERN interns has lunched with international Nobel laureates. “But everyone has been so welcoming and helpful.”

Back at BU, Glashow explains that in the world of particle physics, each new discovery opens the door to many others. The Higgs boson is “the last of the particles predicted by the theory I shared the Nobel Prize for,” says Glashow, “but everyone agrees there have to be other particles, and other structures as well.” As for headlines touting discovery of “the God particle,” Glashow’s distaste is pronounced. “That’s not a term we use,” he says. The catchy term is traced to the title publishers gave to a 1993 book about the Higgs boson by Leon Lederman, The God Particle: If the Universe Is the Answer, What Is the Question? Lederman was “not too happy” about the title, Glashow says. “Nobody likes to use that term. It has nothing to do with God. It’s just a very important particle.”

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Explainable Ai (Xai) In 2023: Guide To Enterprise

Explainable AI (XAI) comes in to solve this black box problem. It explains how models draw specific conclusions and what the strengths and weaknesses of the algorithm are. XAI widens the interpretability of AI models and helps humans to understand the reasons for their decisions.

What is XAI?

XAI is the ability of algorithms to explain why they come up with specific results. While AI-based algorithms help humans to make better decisions in their businesses, humans may not always understand how AI reached that conclusion. XAI aims to explain how these algorithms reach these conclusions and what factors affected the conclusion. In Wikipedia, XAI is defined as:

Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that human experts can understand the results of the solution. It contrasts with the concept of the “black box” in machine learning, where even their designers cannot explain why the AI arrived at a specific decision.

Why is it relevant now?

Shortly, XAI creates a transparent environment where users can understand and trust AI-made decisions.

Businesses use AI tools to improve their performance and make better decisions. However, besides benefiting from the output of these tools, understanding how they work is also vital. The lack of explainability prevents companies from making relevant “what-if” scenarios and creating trust issues because they don’t understand how AI reaches a specific result.

Gartner states that the lack of explainability is not a new problem. However, AI tools become more sophisticated to deliver better results in businesses, and this problem draws more attention now. These more complex AI tools are performed in a “black box,” where it is hard to interpret the reasons behind their decisions.

While humans can explain simpler AI models like decision trees or logistic regression, more accurate models like neural networks or random forests are black-box models. The black box problem is one of the main challenges of machine learning algorithms. These AI-powered algorithms come up with specific decisions, but it is hard to interpret the reasons behind this decision.

XAI is relevant now because it explains to us the black box AI models and helps humans to perceive how AI models work. Besides the reasoning of specific decisions, XAI can explain different cases to reach different conclusions and strengths/weaknesses of the model. As businesses understand AI models better and how their problems are solved, XAI builds trust between corporates and AI. As a result, this technology helps companies to use AI models in full potential.

How does it work?

Today’s AI technology delivers a decision or recommendation for businesses by benefiting from different models. However, users can’t easily perceive how the results are achieved or why the model didn’t deliver a different result. Besides delivering accurate and specific results, XAI uses an explainable model with an explanation interface to help users to understand for the model works. We can categorize XAI under two types:

Explainable models like Decision Trees or Naive Bayes

These models are simple and quickly implementable. The algorithms consist of simple calculations that can even be done by humans themselves. Thus, these models are explainable, and humans can easily understand how these models arrive at a specific decision. To observe the reasons behind that specific decision, users can quickly analyze the algorithm to find the effects of different inputs.

Explaining black box models

Explaining more complex models like Artificial Neural Networks (ANNs) or random forests is more difficult. They are also referred to as black box models due to their complexity and the difficulty of understanding relations between their inputs and predictions.

Businesses use these models more commonly because they are better performing than explainable models in most commercial applications. To explain these models, XAI approaches involve building an explanation interface that has data visualization and scenario analysis features. This interface makes these models more easily understandable by humans. Features of such an interface include:

Visual analysis

Source: Google Cloud

XAI interfaces visualize outputs of different data points to explain the relationships between specific features and the model predictions. In the above example, users can observe X and Y values of different data points and understand their impact on inference absolute error from the color code. In this specific image, the feature represented in the X axis seem to determine the outcome more strongly than the feature represented in the Y axis.

Scenario analysis

Source: Fiddler Labs

XAI analyzes each feature and analyzes its effect on the outcome. With this analysis, users can create new scenarios and understand how changing input values affect the output. In the above example, users can see the factors that positively/negatively affect the predicted loan risk.

Improved explainability and transparency: Businesses can understand sophisticated AI models better and perceive why they behave in certain ways under specific conditions. Even if it is a black-box model, humans can use an explanation interface to understand how these AI models achieve certain conclusions.

Faster adoption: As businesses can understand AI models better, they can trust them in more important decisions 

Improved debugging: When the system works unexpectedly, XAI can be used to identify problem and help developers to debug the issue.

Enabling auditing for regulatory requirements

How does XAI serve AI ethics?

Explainability and transparent

Human-centric and socially beneficial


Secure and safe


One of the primary purposes of XAI is to help AI models to serve these five components. Humans need to have a deep understanding of AI models to understand if they follow these components. Humans can’t trust an AI model that they don’t know how it works. By understanding how these models work, humans can decide if AI models follow all of these five characteristics. 

What are the companies providing XAI?

Most XAI vendors present different explanation interfaces to clarify complex AI models. Some example vendors include:

Google Cloud Platform: Google Cloud’s XAI platform uses your ML models to score each factor to see how each of them contributes to the final result of predictions. It also manipulates data to create scenario analyses 

Flowcast: This API based solution aims to unveil black box models by being integrated into different company systems. Flowcast creates models to clarify the relationships between input and output values of different models. To achieve that, Flowcast relies on transfer learning and continuous improvement.

Fiddler Labs: This US startup provides users different charts to explain AI models. These charts include similarity levels between different scenarios. By using available data, Fiddler analyzes impact of each feature and creates different what-if scenarios.

Here is a list of more AI-related articles you might be interested in:

If you have questions on Explainable AI (XAI), feel free to contact us:

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Fixing Constant Validation Accuracy In Cnn Model Training


The categorization of images and the identification of objects are two computer vision tasks that frequently employ convolutional neural networks (CNNs). Yet, it can be difficult to train a CNN model, particularly if the validation accuracy approaches a plateau and stays that way for a long time. Several factors, including insufficient training data, poor hyperparameter tuning, model complexity, and overfitting, might contribute to this problem. In this post, we’ll talk about a few tried-and-true methods for improving constant validation accuracy in CNN training. These methods involve data augmentation, learning rate adjustment, batch size tuning, regularization, optimizer selection, initialization, and hyperparameter tweaking. These methods let the model acquire robust characteristics and generalize to new data more effectively. To identify the optimum answer, experimenting is necessary, and the approach chosen may depend on the particular issue at hand.

Fixing Methods

Convolutional Neural Networks (CNNs) have proven to be remarkably effective in the deep learning space for a variety of computer vision applications, including segmentation, object identification, and picture categorization. Yet, it might be difficult to train CNNs when the validation accuracy reaches a plateau and stays that way for a long time. The performance of the model will be enhanced by addressing some of the typical causes of this in this article.

Data Augmentation

The lack of sufficient training data is one of the most frequent causes of accuracy in constant validation. CNN models need a lot of data to generalize properly since they include millions of parameters. As a result, if the training dataset is short, the model might not be able to pick up enough characteristics to correctly categorize unobserved data. Techniques for enhancing data can be used to fictitiously expand the training dataset. These methods could involve moving, flipping, rotating, and zooming the photos. We may generate extra training examples that are somewhat different from the original photos by using these changes. As a result, the model may acquire stronger features and be better able to generalize to new data.

Learning Rate

The learning rate is another often-cited factor in constant validation accuracy. The gradient descent step size used to update the model’s weights is dependent on the learning rate. The model may exceed the ideal weights and fail to converge if the learning rate is too high. On the other side, if the learning rate is too low, the model may converge too slowly and become trapped in an unfavorable solution. As just a consequence, it’s indeed needed to adjust the learning rate to an adequate point. One approach to solving this is to use a schedule that gradually slows down learning. This can help the model’s delayed convergence and keep it from becoming stuck in unsatisfactory solutions.

Model Complexity

CNNs are capable of learning a wide range of properties from the data because of their tremendous flexibility. This flexibility, though, is sacrificed for a more intricate model. The model could overfit the training set and be unable to generalize to new data if it is very complicated. Reducing the complexity of the model by eliminating pointless layers or lowering the number of filters in each layer is one technique to deal with this problem. This can enhance the model’s capacity to categorize unknown input and help it learn more generalizable features.

Batch Size

The amount of samples needed to update the model’s weights during each gradient descent iteration depends on the batch size. The model might not learn enough features to correctly identify the data if the batch size is too small. Nevertheless, if the batch size is too big, the model might not be able to adapt to new data. Hence, setting the batch size to the right amount is crucial. Using a batch size that is both large enough to capture data variability and small enough to fit into memory is one technique to do this.


Regularization strategies can be used to prevent the model from overfitting the training data. L1 and L2 regularization, dropout, and early halting are all regularization strategies. A penalty term that is added to the loss function by L1 and L2 regularization pushes the model to learn sparse weights. To prevent the model from overfitting the training set, dropout randomly removes certain neurons during training. When the validation loss stops improving, early halting terminates the training process. By doing so, the model will be less likely to overfit the training set and will be better able to generalize to new sets of data.

Optimizer Initialization

The performance of the model can be impacted by the weights’ starting settings. Whenever the starting weights are very large or little, the model may not converge. Thus, it is essential to initialize the weights to the correct value. Xavier initialization is one popular initialization method that establishes the initial weights according to the quantity of input and output neurons in each layer. His initialization is a different method that, while similar to Xavier’s initialization, performs better for deeper networks.

Hyperparameter Tuning

Many hyperparameters, including learning rate, batch size, number of filters, and number of layers, can influence how well CNN models perform. To identify the combination that works best for the particular situation, it is crucial to test out several hyperparameter settings. A grid search or random search can be used to sample various hyperparameter values and assess their performance.


The use of data augmentation, adjusting the learning rate, reducing model complexity, adjusting the batch size, utilizing regularization techniques, testing various optimizers, appropriately initializing the weights, and adjusting the hyperparameters can all be used to address constant validation accuracy in the CNN model training. The strategy might need to be modified to address the particular issue because no one answer suits all problems. To identify the optimum answer, it is crucial to experiment with several strategies and gauge their effectiveness.

Current Verizon Customers Should Be Eligible For A $200 Credit Towards The Iphone 4

If you purchased a new smartphone from Verizon Wireless between November 26, 2010 and January 10, 2011, you should be eligible for a $200 credit towards an iPhone 4 upgrade. This credit should be available as long as your purchase the iPhone on Verizon before February 28.

Verizon’s FAQ has some interesting answers for current (and potential) customers who might want to use their available upgrade on the iPhone 4…

From the Verizon FAQ:

“Q: Do I need to sign up for a 2 year agreement?

A: When purchasing iPhone at the 2 year promotional price a new agreement is required. However, you will also have the option to purchase iPhone at full retail price, which will not require you to sign a long-term agreement.

Q: If I’m an existing Verizon Wireless customer and not currently eligible for an upgrade, will Verizon Wireless offer any special programs to allow upgrades to iPhone at a reduced price?

Q: If I’m an existing Verizon Wireless customer and I’m eligible for an annual upgrade, will I have to pay the standard $20 early upgrade fee to purchase iPhone 4?

A: No, if you are currently eligible for an annual upgrade, the standard $20 early upgrade fee will be waived for customers purchasing iPhone 4 until the end of March.

Q: I just purchased a new smartphone during the holiday season, but if I knew that iPhone 4 was going to be available soon I would have waited. What are my options now?

It’s pretty cool that Verizon is able to offer such a promotion for the launch of the iPhone on its network. AT&T hasn’t really had any luck with offering promotions or bundle deals since they introduced the iPhone on their network almost five years ago.

Verizon is probably taking the financial hit for such a promotion. I highly doubt Apple would ever give any similar type of offer to a carrier. But with all the traffic that the iPhone will bring to Verizon in the U.S., it’s not hard to see why the network would want to try and muster spirit around the product’s launch as much as it can.

A couple other interesting facts from the FAQ:

“Q: Can I go to an Apple store to purchase and activate the phone?

A: Yes, beginning on February 10th, you can visit an Apple Retail store to purchase and activate. However, prior to visiting, please make sure to check your upgrade eligibility if you are an existing Verizon Wireless customer.

Q: I am an existing customer that upgraded my device between 11/26/10 and 1/10/11. I would like to exchange my upgrade for an iPhone but my return/exchange deadline is prior to the iPhone launch. What can I do?

A: You would need to return the device that you purchased in accordance with our standard return policies and reactivate your original device. A restocking fee of $35 ($70 for netbooks and tablets) would apply. You will then be eligible to upgrade to the iPhone when it becomes available for purchase.”

Verizon also has a helpful checklist of items you may want to consider when moving to the iPhone on Verizon.

What do you think about the possibility of a $200 iPhone credit for already-existing Verizon customers? If you are already on Verizon, does that make you want to go ahead and upgrade?

[Verizon Wireless]

Metavisa: Promoting Development Of Decentralized Identity And Credit System

On November 9 of this year, Discord founder and CEO Jason Citron shared an image on Twitter suggesting that Discord may be testing the functionality of linking Ethereum addresses to Discord pages.

Many community members immediately proposed that Discord might soon allow users to display their NFT collections.

As soon as the news came out, the market responded enthusiastically. As a result, many social network giants now plan to link with Ethereum addresses, which is bound to trigger a new encryption boom.

For example, Twitter is developing a feature that may allow users to add BTC and ETH addresses to their account; Facebook changed its name to Meta, foreshadowing the gradual integration of its products to create a “meta-universe platform beyond reality,” and TikTok is considering entering the Metaverse. 

We have seen this kind of sudden bust of interest and innovation after the launch of a new concept before.

A brief review of DeFi’s evolution

DeFi did not get much attention at first, but with the addition of traditional venture capitalists and firms like Andreessen Horowitz, more and more traditional venture capital institutions and enterprises began to look to crypto and DeFi for opportunities. 

The emergence of DeFi eliminates the intermediaries in traditional financial services and establishes a faster, more inclusive, and transparent financial system.

Buyers and sellers do not need a centralized “middleman” to conduct transactions. The applications of this are many and varied, from traditional products to more complex financial tools such as MakerDao and Compound and even the development of token value capture mechanisms and oracles.

The birth of RioDeFi, Chainlink’s development of the world’s first comprehensive platform for a decentralized oracle network, and the beginning of futures market transactions such as Bitpool all show how more and more traditional financial products are being decentralized. This raises the issues of regulation, user experience, and scalability solutions, as the magnitude of transaction users is constantly expanding. Additionally, ever more numerous consumer-scale products are being produced, and many Dex platforms, such as Futureswap, have gained the favor of both the market and capital.

The value of NFTs restated for the GameFi era

The value of NFT is based on the proof of authenticity and the verifiable proof of ownership. In the traditional market, being able to prove that something is authentic is a highly valued commodity.

However, even senior experts in an industry, such as art or antiquities, can be deceived by superb counterfeit manufacturing technology. 

The emergence of NFTs solves this problem. The use of smart contract storage greatly simplifies the resolution of issues such as authenticity and ownership disputes, and the items can be effectively traded or transferred without the need to worry about the process. The art will neither be at risk of destruction or loss while, at the same time, being more sharable than ever.  

NFTs satisfy the collector’s pursuit of aesthetics or beliefs and allow a person to demonstrate ownership as needed.

The emergence of GameFi seems to introduce DeFi gameplay into blockchain games, and players can earn revenue by playing games. From the gradual maturity of the background ecology, the combination of DeFi and NFT has been implemented in the way of games, making GameFi run the blockchain financial system in a more intuitive way. In addition to investment enthusiasts, it also attracts more game players and game companies enter the crypto market.

GameFi’s interactive, entertaining, social, and fairness features, in addition to enabling players to make money, and everyone can participate in the game fairly, without being suppressed by local tyrants, and it also breaks the tradition that game assets belong only to development companies convention.

The emergence of Axie Infinity has also brought GameFi into a higher climax, attracting more traffic, and its daily income is more than three times the Arena of Valor.

SocialFi promotes the development needs of Web3.0

Jassem Osseiran, the founder of MetaVisa, indicates that the emergence of SocialFi may be much larger than that of DeFi, NFTs, or GameFi. While a great deal of attention has been paid to certain applications, SocialFi is not limited to fan or social tokens. The entrance of the existing social media giants into the space will introduce large-scale traffic into the market.

The status quo in the social media era has also laid the foundation for this development. Examples of how this has already worked out abound. For example, consider the consumer output value brought about by the emergence of KOLs or Elon Musk’s influence on the price of Bitcoin and Dogecoin through his social media accounts. 

Mark Zuckerberg said the announcement of Facebook’s official name change to Meta helped drive the price increase of many coins related to the Metaverse, suggesting that a self-sustaining economic system can be formed through the tokenization of social influence. At the same time, this system can help people of different levels of social influence share the benefits.

Distributed digital identity is particularly important in DeFi, GameFi, and SocialFi. In the physical world, identity certificates such as government-issued ID cards are issued by centralized institutions based on the identity of different people and are used to prove the ownership of certain assets or as a qualification to enjoy certain rights- such as the right to purchase alcohol. 

In the modern social system, the verification of identity is the foundation of establishing trust. In the Internet world, trust or verification principally relies on user names and passwords. As long as the correct information is entered, it means that the identity verification is passed. 

However, as many of us know, passwords are easily stolen, and the control of user information is not in the hands of the individual using a website but rather with those who run a centralized platform.

In other words, if the centralized platform is closed or the information is stolen, individuals will not be able to maintain the ownership of personally identifiable information, and the rights and certifications that information grants may be lost.  If we lose the certification of the centralized platform, how can we prove that we are ourselves?

An example of what might happen to a person when such a thing occurs at a larger scale can be seen in the film “The Terminal,” starring Tom Hanks. In the film, a man finds that a coup d’état in his homeland has caused his travel documents to no longer be recognized by the US Immigration Bureau.

While few of us will have to fear living in an Airport for two decades due to a problem with centralized identity, we do have to worry about other issues related to the possibility of the centralized authority having problems or simply failing at its tasks. 

The emergence of decentralized identity will solve these problems and shift the control of user information from the platform to users.

Decentralized identity will be used to prove relevant rights and interests, but it will not form a perfectly overlapping relationship with physical identity. Instead, it will be based on indelible blockchain data such as Defi credit history, blockchain activity records, asset holdings, address correlation, and other related factors.

Taken together, they will provide a snapshot of a person, but it may not be the only snapshot they go around with- multiple decentralized identities will be the rule of the day. 

Different people can provide different identity information on different platforms in different scenarios, times, and conditions. Related rights and asset owners will be bound to different decentralized identities and need only be used when the owner decides they are needed rather than when a platform wants to gather more data about you.

This allows users to engage in interactive behaviors in the Metaverse safely- they can reveal as much or as little information about their hobbies, community participation, asset-level classification, industry attributes, or other aspects as they choose. 

MetaVisa serves as a Web 3.0 middleware protocol and is committed to promoting the development of the best Metaverse identity. Towards that end, it has created the MetaVisa Credit Score system (MCS). Developers in areas such as DeFi, GameFi, or SocialFi can use MetaVisa’s credit system to improve their users’ experience. 

In addition to providing a decentralized Metaverse identity (MID), this system also allows for effective interactions with other applications in the Metaverse by providing a single, trustworthy, easily accessed credential to prove the trustworthiness, assets, and identity of the user. 

The optimization of these interactions also allows for better services in the development and application of SocialFi. Additionally, MCS can be used as a store of personal value. In order to demonstrate a higher personal social influence, MID holders will have to be more active on-chain and earn more value in ecosystems.

The MetaVisa credit score consists of two parts. The first part is calculated by human-designed features and human-designed formulas. The second part is given by the carefully designed machine learning algorithm.

For the human-designed part, we consider the following three aspects.

Address activity: The timing and frequency of transactions with an address are used to describe the activity. The more frequent the transactions are, the more active the address is. 

Address balance: Addresses with more assets should have higher credit. We adopt the following rules when calculating address balance.

Convert different tokens into one unit:

Take the timing into consideration:

Take the debt into consideration: 

With the above rules, we can sample the balance (asset – debt) for each address in each day, and maintain a exponential time weighted average balance for each address as: Bal_avg[t] = a * Bal_avg[t-1] + (1 – a) * Bal[t]. Bal_avg[t] is the exponential average balance in the t-th day, and Bal[t] is the balance in the t-th day.

Interaction with typical smart contracts: We make statistics of smart contracts interactions mainly on three fields, which are DeFi, NFT and GameFi. For each field, we filter out a typical set of applications to construct an applications pool. In the future, as more web3 Apps appear, we will include more fields and typical applications. For each address, the interaction frequency with the applications in the pool is counted.

For each address, a weighted sum of the above features is calculated to obtain the human-designed part of the credit score.

For the machine learning algorithm, we construct a graph. in the graph, each node is an account address. If two addresses have some interaction during the past period of time, there exists an edge between them. For each node, its features in the graph consist of the following parts:

The features of the address itself, including its activity and balance.

The features of its transactions with other addresses.

The features of its neighboring addresses.

To predict the probability that an address will liquidate, we collect the liquidation events in the typical DeFi platforms. For each address, we label it as positive if liquidation events happen in the following period, and negative if not.

We conduct the following machine learning algorithms to predict the liquidation probability: GCN (Graph Convolutional Network), logistic regression, random forest. Based on each single machine learning algorithm, we develop an ensemble algorithm, which is more robust and can generalize better.

Some recent major leaps into SocialFi include these major projects and network expansions. 

Mask Network helps users transition from Web2.0 to Web3.0, allowing users to send encrypted messages, cryptocurrencies, or even NFTs on traditional social platforms. Open-source software development incentive platform Gitcoin promotes the development of an open-source movement.

The Solana Foundation, Audius, and Metaplex jointly launched a US$5 million creator fund to attract artists and musicians into the crypto industry, which has helped promote the development of SocialFi and the arrival of the Web3.0 era. 

We look forward to watching SocialFi as the next hot spot in the market. With luck, it will be as common a term as “DeFi” in the near future.

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