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HOW-TO FOR STARTUP: SIMPLE WAYS TO MAKE THE BEST OUT OF CONVERTIBLE NOTES

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The scale of investors has risen in the golden age of financing, with hopes of landing the next success story such as Google, Facebook, or Amazon. In the following article, startup companies can learn more about the basics of convertible notes and about how their company can make the best out of the convertible bonds.

Without having an established credit history, startup companies aren’t qualified to get a conventional loan. Since a startup is in its initial stage of formation, it is unconvincing for a bank to trust the company. Hence, the startups turn to equity to fund their daily operations. Although, without any value or assets to the company’s name, defining the equity is also impossible. In such a situation, how does a startup get its seed funding? This is where convertible notes come into the picture.

 

What are Convertible Notes?

A convertible note is an instrument of debt that is used for a short period of time and can be converted into equity – a company’s portion that is under ownership. The initial funding for the startup is provided by angel investing, under which the company can grow further to create its own capital. The purpose of convertible notes is to help increase the pace by which the company earns its income. It is also a less expensive route than giving away the company’s equity while getting the same reliability as that of a conventional loan. In return for the funding received by the startup company, the convertible debt instrument typically converts into equity (stock ownership) in the future round of financing.

 

Convertible Notes – Are They Hybrid Securities?

What are hybrid securities? Hybrid securities are investment instruments that feature both the characteristics of equities and bonds. Even though convertible notes are originally structured as debt instruments, it also has a great probability of eventually converting into equity. Hybrid securities aren’t as high-risk as equities but more at risk than pure bonds.

 

Terms Related to Convertible Notes:

Here are some terms and terminologies that are related to convertible notes.

Interest on Convertible Bond

Investors that provide funding to a startup company, will possibly earn interest over their The purpose of having an interest rate on the convertible debt instrument is to assure that the investors can get a minimum rate of interest as their returns. If in case, the startup is unable to raise enough capital and the bonds don’t typically convert into equity, then the interest on the convertible bond will assure that the investors do get the minimum rate of interest with their funding amount. This will result in the investors making a low percentage of interest at maturity.

When it comes to building interest on the convertible debt instrument, the amount isn’t a series of transactions made in cash but rather an amount that is accumulated over a period of time.

 

Maturity Date

The convertible notes that are provided by the issuer come with a maturity date – which signifies when the convertible debt instrument bond is due or payable to the investor. If in case the notes have not already converted into equity by the maturity date, then the company will have to repay the investor along with the minimum interest amount. The future round of funding is also set by the maturity date of the convertible note.

 

Valuation Cap

The valuation cap is an added bonus for those investing in the startup at an early stage. A valuation cap can be defined as the value of the company that can help set a limit to the amount that the issuer will pay in case the debt converts into equity. The valuation cap is an ‘agreed upon’ amount or the maximum valuation that will set the price per share in the future round of funding. The price per share is decided by the division of the valuation cap. It makes it clear for the investor that the convertible note won’t be valued more than the agreed value cap. This allows the investors that have been issued convertible notes to cap the lower valuation price when converting the bonds into equity. Convertible bonds have a valuation cap which helps to adjust a limit price per share by division of value cap with a number of shares. In case if the company’s value is lesser than the cap value, a valuation cap isn’t used in such a situation.

 

How Can Startups Benefit from Convertible Notes? 

Startups can benefit from convertible notes in numerous ways. Angel investors, friends and family, venture capitalists are all sources that can help raise funding for a startup in its initial stage. Investors may also choose to invest using convertible notes which makes it easier for the startups to document. Based on legal processes, convertible notes prove to be more inexpensive than other investments and result in quicker financial rounds. Unlike equity bonds that require various documents for the closure of the financial round, convertible debt instruments take less time and expense to complete a round of funding.

Other than this, the valuation cap (company’s value at the time of investment) determines the rate of shares that are offered. The decision regarding the cap value is difficult for a startup to make as it is in its early stages. Convertible notes make this task easy for issuers as they need not guarantee a fixed valuation of the company until the next round of financing. This provides enough time for the startup ventures to develop appropriate standards that can help to decide a fair rate for the next round of funding.

Now that you’ve gained enough knowledge about convertible notes and startups, here’s your chance to develop your own idea into a successful business. If you’re looking to sharpen your strategy skills to build a great venture, this guide will surely help you out – 7 Business Strategies to Build a Successful Startup.

 

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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