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HOW TO BUY USDT AND AVOID THE HIGH VOLATILITY OF CRYPTO

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Understanding and breaking down all the different types of crypto can feel like a huge task—there are so many variations of each coin. To make things easier, we’re going to break down one main form of crypto, Tether. Tether has a lot of benefits, but there are also some important facts you should be aware of. This article will explain what you need to know about the popular stablecoin known as USDT.

 

What is Tether?

Tether is a stablecoin pegged to the US Dollar and it’s said to mirror the dollar with a 1:1 ratio. This means that 1 USDT is equal to 1 USD. Before we go down the Tether “rabbit hole,” we need to break down the definition of a stablecoin.

In the simplest terms, a stablecoin is a cryptocurrency that ties (or tethers) its market value to another currency or asset, such as gold, oil, or in this case, the US Dollar.

You can think of Tether as a cross between a cryptocurrency and a fiat currency. The reason stablecoins peg themselves to other commodities is to ensure price stability and to maintain their value. Tether and their owner, Tether Limited, run the currency through blockchain technology, yet they are stable like the dollar.

Another way to wrap your head around Tether is by thinking about balloons. Imagine a balloon is tethered to someone. While the balloon is free to float around, its limitations are based on the moving person. Now, imagine that the balloon is USDT and the person is the dollar. The person or dollar is stable, and it’s not moving very much. This means the balloon or USDT is also not moving around either. However, the balloon is still free to make some movements while being tied to the person.

 

Why is Tether Used?

USDT is often used to avoid price jumps and volatilities associated with crypto—we’ll touch on this. In fact, many crypto investors find that Tether is a good alternative to Bitcoin since it is less volatile, but it’s still considered a cryptocurrency. Other crypto traders use USDT because it’s easier and cheaper to transfer Bitcoin into Tether than the dollar. With USDT, there are also little to no transaction costs and fewer trade delays.

 

Advantages and disadvantages of Tether

Since USDT and stablecoins are unique forms of currency, it’s important to understand the advantages and disadvantages before buying them.

 

Advantages of Tether:

  • Borderless: Like with all cryptos, you can send and receive USDT anywhere. This crypto doesn’t follow any borders, making transactions seamless and quick compared to the dollar.
  • Stability: USDT is always trying to maintain its value to one dollar. This makes it less volatile and more stable than cryptocurrency.
  • Protection: Since USDT is a cryptocurrency yet is valued like a fiat currency, traders can easily convert their crypto balance into USDT and back again. Some traders convert to USDT to protect their non-stablecoin crypto against a high influx in prices. Then when the cost of crypto stabilizes, traders will convert their USDT back.
  • Liquidity: USDT is said to have a high rate of liquidity, unlike its fiat currency counterpart. However, it’s worth mentioning that there have been some transparency issues associated with Tether Limited and its reserves. Tether Limited is the company that created Tether, and they have yet to complete an audit at the time of writing. Furthermore, Tether is not held to the same supervisory standards as traditional banks are. The lack of transparency and supervision means that Tether’s liquidity and its ability to be issued could be questionable.

 

Disadvantages of Tether:

  • Anonymity: A known issue with USDT is its lack of anonymity. To issue or redeem USDT from Tether Limited, you must input your personal info, such as your banking info. This means the company will have access to your personal data. Alternatively, you can buy USDT from the exchange site. However, these exchanges often have a process called KYC (know your customer), where users must verify their identity to buy crypto.
  • Decentralized: Tether Limited owns and controls USDT. This means the coin is not decentralized, and it even depends on the company to function. This has been an issue for some crypto investors that prefer their crypto to be completely free of influence.
  • Transparency: Despite USDT being owned by one company, there has been a lack of clarity about how the company operates. Also, Tether Limited has yet to complete a full and public audit. Due to their lack of transparency, it’s hard to ensure that USDT maintains a 1:1 ratio with the dollar. Tether Limited also does not 100% guarantee that its crypto is the same value as the dollar.

Now that we’ve explained all the basics of Tether let’s go into how to buy it.

 

How to buy Tether

Buying USDT is easy to do on popular crypto exchanges, such as Paxful. Or you can buy it from Tether limited.

 

Here is the process to buy USDT from Tether Limited:

  1. Deposit the amount of money you’d like to spend in a Tether Limited bank account.
  2. Tether will generate and credit the specified amount of USDT to a user’s account.
  3. If you want to exchange your USDT for another currency, you can deposit your tokens into a Tether Limited account.
  4. Then, Tether Limited will destroy the tokens and will send you the converted currency.

Tether is growing fast. With plans to make a Japanese and British version of USDT, this coin is evolving rapidly. When exploring your crypto options keep in mind that the crypto world is always changing. It’s always a good idea to stay up-to-date with the latest news. Lastly, remember to do your research before making any final plans.

 

*The content of this article is for informational purposes only, you should not construe any such information or other material as legal, tax, investment, financial, or other advice.

 

Finance

AIRBANK SELECTS YAPILY TO BUILD A FINANCIAL MANAGEMENT SOLUTION FOR SMBS

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Airbank, a financial management solution for European startups and SMBs, has selected open banking infrastructure provider Yapily to help its users manage their finances with ease.

Airbank provides a simple financial management solution that aggregates all bank accounts in one place and delivers more control, visibility, and automation to modern finance teams. Startups & SMBs use Airbank to access bank accounts, monitor cash flow in real-time, create reliable forecasts, and make business payments.

Airbank matches bank transactions with merchant and category data to give finance teams complete visibility into revenues and expenses, thus helping make their lives easier with cash flow budgeting, forecasting, and reporting.

Yapily’s API infrastructure provides Airbank users with a smooth, simple way to connect to more than 1,500 banks across the UK and Europe including Deutsche Bank, Commerzbank, Sparkassen, Volksbanken and neobanks. Airbank selected Yapily for its strong coverage in Europe, with a specific focus on Germany, France, Spain, and the UK. Yapily’s European bank connectivity enables Airbank’s customers to scale and grow across Europe, delivering forecast visibility anywhere they go.

The partnership with Yapily alleviates Airbank’s customers from spending time and resources managing their finances – giving them direct access to all the financial and contextual data they need in one tool. Historically, most businesses created budgets and cash flow forecasts in manual spreadsheets which is time-consuming and error-prone. With Airbank, customers save time and costs to focus on value-adding business tasks.

The partnership also enables Airbank’s customers to use its data enrichment platform and transaction categorisation engine to turn the raw data from bank accounts into meaningful and actionable insights. Airbank reconciles account balances, forecasts financials and helps business owners make smarter business decisions every day. Harnessing Yapily’s leading open banking infrastructure, Airbank can accelerate its adoption of digital banking services.

Airbank’s vision is to simplify financial management for SMBs and to create a unified platform that helps its users with the full cycle of financial management from cash flow analysis and forecasting, to accounts receivables and payables management, and more. Airbank has raised $3m seed funding from leading VCs, and counts hundreds of users in Germany, Austria, France, Spain and the UK.

Open Banking has enabled smooth integrations with banks, which we utilize to offer richer banking and payments experiences for our users. We’re building a business banking solution that connects all your financial accounts in one place. Our partnership with Yapily gives users a smooth and simple way to connect to thousands of banks in Europe, unlocking real-time insights into their cash flow. We eliminate the pains of finance admin so business owners can focus on what’s really important — growing their business.

Christopher Zemina, Co-founder and CEO of Airbank

Airbank helps simplify the daily routine of banking and finance management for small and medium sized businesses. By leveraging Yapily’s open banking infrastructure, Airbank can provide actionable insights to businesses – at a time where it’s needed. As a small yet fast growing company, Yapily is committed to supporting the SMB community and we are excited to see how Airbank delivers the benefits of open banking to many businesses across Europe.

Comment by Chris Scheuermann, Commercial Lead DACH at Yapily

 

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AI AND HOW IT’S LEADING THE FIGHT AGAINST FRAUD IN THE FINANCIAL SECTOR

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Geoff Clark, Managing Director, Aerospike EMEA

Much like many other sectors financial institutions have accelerated their digital transformation projects since the beginning of the pandemic. Lockdown meant that customers could no longer visit local branches or meet in person with their financial advisor. Financial institutions have no choice but to find alternative ways to serve their customers.

We saw banks quickly adapt and improve their automation tools to interact with their customers online.  Technologies that enable chatbots, credit card brokerage, contactless payment cards, digital verification for onboarding, online insurance applications, mobile apps, recommendation engines, robo-investing and robotic process automation (RPA) were just some of the many solutions deployed. Here in Europe, Ernst and Young (E&Y) reported an increase of 72% increase in the use of FinTech apps since the start of COVID-19.

Geoff Clark

Cybercriminals typically opt for the lowest hanging fruit and as financial institutions clambered to expand their digital services the cybercriminals looked to identify and exploit any weakness in the infrastructure providing the backbone for these technologies. Exploiting the vulnerabilities of financial institutions is not new as they have long been a coveted target for fraudsters. In the main, that’s due to the wealth of sensitive personal and financial information they hold. Throw into the mix pandemic relief funds, increased unemployment benefits, and stimulus payments, and you have the perfect playground for fraudsters.

A recent report found that every dollar lost to fraud costs financial service companies as much as $3.78 — an increase from $3.25 in 2019. But fraud’s impact is much deeper than financial loss. It drains company resources to investigate and prosecute fraud, damages reputations, and puts customer retention at risk. For these reasons alone, it is imperative that the appropriate systems and processes are in place to combat fraud.

 

Analysing Fraud

The majority of financial institutions still rely on dated rule-based systems to mitigate fraud risk. These systems can consist of thousands of predefined rules that store, sort, and manipulate data to find fraud patterns. For example, a rule could say, if there is a credit card transaction in one state and another transaction in a different state within a 30-minute time frame, then this is likely a fraudulent transaction and therefore it declines the transaction.

Rule-based systems are static, hard-coded, and time-consuming to update, and are often one step behind the sophisticated techniques fraudsters use. When fraud occurs, the typical response is to create another rule that prevents another attack, but it’s often too late.

Fraudsters continue to find new ways to commit fraud that rules don’t capture.

The trend we’re seeing from financial institutions is to replace rule-based systems with AI and machine learning-based systems as they’re more effective. These systems are largely self-learning and there is so much more data available and the more information they’re fed the more effective they can be. Rather than using tens of data attributes with rule-based systems, AI and machine learning-based systems can analyse hundreds of data attributes over enormous data sets and longer time frames to automatically detect with higher accuracy unusual behaviours that indicate fraud. For example, Barclays Bank has implemented AI systems to detect and mitigate fraud improving the customer experience in the process through the reduction of false positives and false negatives.

AI and machine learning-based systems are heading toward explainable AI (XAI), an emerging sector in machine learning that addresses how AI systems arrive at their black-box decisions. Financial institutions know the inputs and outputs of these systems, but they lack visibility into how they reached the results.

Building XAI into AI systems enables banks to understand how decisions are made and create better models to improve their systems by removing bias. For example, suppose a fraud system declines a legitimate customer’s credit card transaction. In this situation the financial institution needs to understand why the false positive has occurred so it can further refine its model.

XAI also has data privacy in its favour particularly when it comes to compliance. Under the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA)—and with other data privacy laws coming—financial institutions need to comply with specific mandates. They must be able to explain how they use a customer’s personal information and how they came to decision such as declining a credit card transaction. Overlaying XAI on top of their AI systems, ensures they have far great visibility into how decisions are being made by AI/ML systems.

 

Constructing a Fraud System Architecture

To emulate some of the industry’s more innovative organisations financial institutions must understand and pursue best practices when building their AI-based fraud systems. They should work alongside technology organisations but also work with their line of business managers to understand how fraud is impacting their business, what their greatest weaknesses are, how customer satisfaction can be improved, and how they can incorporate customer fraud/risk metrics into their customer analytics to improve their omnichannel marketing campaigns. Customer data collected and analysed by fraud teams are some of the most robust depositories of customer information making them invaluable to marketers.

When looking to build a world-class system, financial services firms should consider the following steps:

  • The fraud system needs to likely consume hundreds of terabytes of data, perhaps even petabytes for the largest firms.
  • Data must be continuously updated in real time from many sources such as internal customer and transaction data from storefronts, web pages, and mobile devices, as well as third-party demographic, behavioural, geo-location, identity management, credit bureau, and other data types.
  • This data will usually need to be prepared, e.g., cleansed, standardised, and normalised, to convert it into a form that AI/ML models can more easily digest and understand.
  • The data needs to move back to the central data platform to be further enriched.
  • At this point those financial institutions can fine-tune the model parameters, test and select the optimal machine learning algorithms, feed them with data to learn the underlying patterns, and validate the model’s accuracy to make good decisions using data that was not part of the training set.

After the above steps are completed and they are satisfied the model can be deployed to act in the microsecond moments that are necessary to fight fraud.

As technology evolves at such a fast pace all organisations must aim to implement a fraud solution that can combat the increasingly sophisticated fraudsters while implementing the following key elements

  1. Large data sets (TeraBytes, PetaBytes) consisting of both internal company data supplemented with third-party data;
  2. Highly optimised and validated AI/ML algorithms that detect fraud and minimise false positives and false negatives;
  3. A real-time data platform capable of running these AI/ML algorithms across enormous data sets in sub-millisecond response times to provide customers with the fast customer experience that they expect.

 

 

 

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