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What is the Role of an Underwriter?

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Understanding the underwriting process

The term underwriting came from the practice of risk-takers writing their names under the total amount of risk they took. Before going into the details of the role of an underwriter, let’s get an idea about the underwriting process and what all it entails. In the most basics sense, underwriting can be explained as a process where big financial institutions provide their services to cover the financial risk for any liability arising out of an agreement.

It can also be understood on the grounds of leveraging the risk in case of an uncertain event. The financial institution guarantees monetary aid in case of any undesirable outcomes mentioned in the contract. Underwriting is one of the most important functions in the banking and finance segment.

The underwriting services can be provided by an individual or an organization wherein the risk related to a business or investment is undertaken by the service provider for a premium amount. The underwriters are primarily available in banking, insurance and stock markets to hedge the risks.

Now that we have a fair idea as to what the underwriting process entails, let’s delve deeper into the roles of an underwriter.

Role of an underwriter

So what exactly does an underwriter do? Well, an underwriter is responsible for hedging your risk in case of uncertain events. The roles and responsibilities along with the purview of an underwriter differ depending upon what type of underwriting services are offered by them.

Underwriters primarily function in three sectors; the banking sector, the insurance sector and the stock market. Let’s look at the role of an underwriter in these contexts to obtain a better understanding of the role.

  • Insurance underwriter: Insurance underwriters play a very crucial role in the insurance sector by hedging and dividing the risk associated with an insurance policy. Insurance underwriters are responsible for reviewing the application for risk coverage and conducting a thorough risk analysis.

    Based on their risk analysis they accept or reject the application. Insurance brokers and other parties submit the insurance applications on behalf of their clients which are reviewed by the underwriter who then decides whether to provide insurance coverage or not. In addition to this, the insurance underwriters also advise on risk management issues and determine the extent of coverage for various parties.

  • Mortgage underwriter: A mortgage loan underwriter is among the most common underwriters you’ll find. A lot goes into the mortgage application process. Thorough credit analysis is performed where the applicant’s income, credit records, cash flow, savings, etc. are assessed to determine the risk associated with loaning out the funds.

    Mortgage loan underwriters are responsible for overviewing the financial track record of the individual and based on their assessment, they approve or discard the loan for an applicant. They are also responsible for reviewing the value of collateral pledged for the loan so that the loan can be recovered in case of default.

  • Underwriting in the stock market: Underwriters in the stock market are responsible for determining the price of a security and identifying the risk associate with it. Let’s take an example to understand it. You must have heard about the IPOs where a company issues funds from the public by selling the shares of a company.

    In this case, investment banks provide their underwriting services to evaluate the correct price of a security by factoring in all risks and benefits. The insurance bank buys or underwrites the security issued by the business entity and then sells it to the public. Here, the insurance banks underwrite or hedges the risk associated with the sales of security.

A career as an underwriter can be very rewarding depending upon which segment or industry you are aiming for. You can opt for an underwriting course to obtain a comprehensive understanding as to how the market functions and learn the skills needed to get a job as an underwriter in a reputed organization.

Also Read: What is Underwriter Salary

What are the Objectives of Financial Statement Analysis?

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The main goals of financial statement analysis are to comprehend and analyse the data in financial statements in order to assess the firm’s profitability and financial stability, and to predict its future possibilities. The goal of the analysis is determined by the individual conducting it and his subject.

To highlight the significance of such analysis, the following purposes or objectives of financial statements analysis may be stated:
1) To evaluate the firm’s earning potential or profitability.

2) To evaluate managerial effectiveness and operational efficiency.

3)  To evaluate the firm’s long- and short-term solvency condition.

4) To pinpoint the causes of the company’s changing

Financial Statement Types

Companies give transparency to their stakeholders by using the balance sheet income statement and cash flow statement. The three statements are related to one another. They produce various interpretations of a company’s operations and performance.
Below is a summary of these four financial statements.

Balance Sheet

The balance sheet reveals the assets of a company’s value (per GAAP). Either equity or debt (liabilities) can be used to finance assets. As a result, the fundamental accounting formula of assets (A) = liabilities (L) + shareholders’ equity is created (E). In general, assets are ranked according to how quickly they can be turned.
In general, liabilities are listed according to when they are due.
The reader will be able to identify a company’s ability to meet short- and long-term financial obligations by understanding the leverage and liquidity of the balance sheet via the use of certain ratios that will be covered in this book. Understanding the balance sheet can also help the reader get a sense of the company’s capacity to raise money through equity or debt in order to purchase new assets or settle existing debt.

Income Statement

The income statement displays the revenues and costs that a business incurs over a specific time period). Typically, these line items are prepared in accordance with GAAP. Transactions are recorded at the point of sale. Accrual accounting uses the matching principle. In essence, accrual accounting computes receipts rather than actual cash.

Despite the fact that the money isn’t collected for a few weeks or months after it was sold, accrual accounting still considers the transaction to have occurred at the time of sale. Alternative accounting techniques include the cash basis.
This less popular method only computes transactions (revenues and expenses) when money is actually exchanged.

The revenues (or sales) produced from the sale of goods and/or services during typical business operations make up the top line of the income statement. The direct costs associated with making those sales are usually listed on the next line. When we analyse profitability ratios later, the net figure of these two reveals the gross profits and gross margins.

These non-cash expenditures are made in order to spread out the price of large, long-term assets throughout the time that they are utilised. The bottom line displays a company’s net income (or loss) after all expenses, including taxes and other non-operating income, have been deducted. Once more, a company’s net income (or loss) is not always the amount of money it brought in through sales; rather, it is the difference between that amount and that period’s expense receipts.

Statement of Shareholder’s Equity:

The income statement and balance sheet are related by this statement. The equity component of the balance sheet can then be reconciled with distributions, dividends, or capital infusions.
After distributions and dividends, any remaining net income is reinvested in the company, which raises the retained earnings account in the equity column of the balance sheet. The statement of shareholders’ equity contains a complete list.

The statement of shareholders’ equity also includes information about other equity sales and purchases, like stock repurchases, and reconciles. Many privately held businesses lack a declaration of shareholders’ equity and instead use simple equity accounts.
The company’s net income is often equal to the variation in retained earnings from one period to the next. If the amount is smaller, the difference usually represents the amount of distributions or dividends that were taken; nevertheless, the correctness of this information should be confirmed with the corporation.

The current balance sheet and prior period balance sheet dates should coincide with the income statement date range for these computations.

Statement of Cash Flows

A statement of cash flows can inform a reader whether or not a corporation generated cash from these receipts. The income statement can indicate if a company made a profit based on receipts. Sales receipts cannot pay creditors on their own. Actual cash received is of utmost importance to many readers of financial statements.

The accrual method data from the income statement is used to create the statement of cash flows. It is then adjusted up or down based on the changes in the balance sheet accounts.
The accountant creates the statement of cash flows using the direct approach. It includes factors like money received from clients, interest, money given to suppliers, etc. that have an impact on cash flow.

The reader is given a complete picture of the sources of cash and the uses of that cash in the statement of cash flows.
In this manner, an analyst may quickly analyse a company’s cash flows and be able to examine operating cash flows independently of the other operations. In the end, this statement makes up the discrepancy between the cash on hand at the start of a balance sheet period and the cash on hand balance at the end of the period.

The Skills You Need for Success

There are several methods available to you if you want to learn how to analyse financial statements.
You might choose a self-taught path, reading through publicly accessible financial statements to become accustomed to how financial data is normally presented.

Conclusion

Take your career to the next level. Find out how improving your knowledge can make your organisation more productive.

Financial statement analysis takes a holistic approach to evaluating and assessing the financial well-being of an organisation.

How do You Perform a Financial Analysis

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The finance and banking sector has evolved on an unprecedented scale over the last two decades. From a few internet transactions to a totally digitized banking experience that can be availed using the smartphones, the game in this segment has changed. Much of this change is owed to change in customer’s behaviour and preferences, for example, the growth in online shopping led people to use online payment methods. They gradually adopted digital banking services as it was more convenient for them to make a purchase.
This evolution in the banking and finance segment is naturally better for the customers and also for the institutions in most regards; it possesses a challenge in terms of complexity in business structure. This complexity has made it necessary for the companies to carry out financial analysis and evaluate the financial standing of the business. Financial analysis is not just limited to banks and other financial institutions but every organization must analyse their financial performance.

What is Financial Analysis

So, what exactly is financial analysis? Well, financial analysis can be explained as the process or method of evaluating and assessing a business, project, etc. that involves financial transactions and monetary gains. It is carried out to find out whether it will be feasible to take on a new project or invest in a business or for introspection. It helps to determine whether a business is financially stable and whether it will remain so in the near future. In short, financial analysis is a way to check the financial soundness of an organization.

The Process of Financial Analysis

The demand for financial analyst has grown over the period given its significance in the contemporary business landscape. A financial analysis course can help those who aspire to work in the capacity of a financial analyst in a reputed organization. Now that we have understood what financial analysis is, let’s explore how financial analysis is carried out by experts.
The first and foremost step in carrying out financial analysis is data collection; financial analysts are required to collect historical financial data of the company to conduct a thorough analysis. The data collection process includes collecting data for the last 3 to 5 years from various financial statements including balance sheet, cash flow statements, income statements, shareholder’s equity statement, etc. These statements can be obtained from the annual reports of the company.
Now, after successfully collecting all the relevant data that influences financial standpoint, financial analysts are required to go through the details and identify any large movements from year on year basis. A general analysis of the financial statements is carried out to pick out any abnormalities including any suspicious findings. Then based on these findings the analyst will need to research the business activities in the past to rule out any suspicions.

In addition to looking at the figures mentioned in the financial statements, analysts are required to review the financial notes mentioned to obtain valuable insights regarding the finances. An in-depth individual analysis of each financial statement is done. The balance sheet is analysed to identify any large changes in the assets or liabilities of the business. The income statements or profit & loss statements are analysed to identify any trends over time.

After this, the organization’s shareholder’s equity statement evaluated to find out the change is stock and retained earnings. It is done to answer questions such as whether the company has issued new shares or bought back any from the market. What’s the status of retained earnings? Has it grown or reduced over a given period? These findings are made only after analysing the shareholder’s equity.

In addition to all these, the cash flow statements are also analysed and financial ratios are calculated to evaluate the trends over time. Competitor’s research is also conducted to compare the company’s stats and find out where it’s lagging and what measures are needed to rectify the situation. After conducting this dynamic analysis, an analysis report or review report is created with all the problems and suggestions to overcome those.

Also Read: What Do You Mean By Financial Analysis

10 Best Books About Investment Banking!

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The Investment Banking Landscape

Investment banking has been one of the most essential industries in modern-day capitalist society. It has always been an important integration of the banking and finance sector given the role it plays. So what makes the investment banking industry so indispensable?

Well, the functions of an investment bank make it a crucial part of the economy. The primary function of the investment banks includes efficiently channeling funds in the economy by leveraging their large network and expertise.

The efficient channeling of funds is just a broader view of what9+ the investment banks do regularly. There are a whole lot of services that the investment banks offer, from underwriting to advisory, the purview of investment banks extends beyond imagination in the financial domain. The high-paying jobs in the investment banking industry make it a lucrative career prospect.

An investment banking course will surely help those aspiring for a job in this sector. However, people who are keen on learning the intricacies often also read books related to investment banking. Investment banking books provide a deeper insight into the functioning of the industry and gives a new perspective on things.

Here is a list of some of the most interesting investment banking books if you want to add some value to your investment banking knowledge base.

  1. One Up on Wall Street
    One Up on Wall Street is considered an evergreen book for investment banking. It is written by Peter Lynch, an investor, mutual fund manager, and philanthropist. He successfully managed the Magellan Fund at Fidelity Investments between the years 1977 to 1990. It gives a very plain and simple approach to investing and removes all the unnecessary complexities in the process.
  2. Investment Banking: Valuation, Leveraged Buyouts, and Mergers & Acquisitions
    The second in this list is a book by Joshua Rosenbaum & Joshua Pearl. If you are looking to find easy explanations for the complex technical jargon of the investment banking industry, this book will surely help you in every aspect. It takes a holistic approach to explain the investment banking industry.
  3. The Business of Venture Capital
    A lot of investment banking is all about raising funds for companies from venture capitalists. This exemplary book by Mahendra Ramsinghani throws light on the essentials of raising funds, structuring deals, value addition, and exit strategies. It’s highly recommended if you want to obtain deeper insights into Venture Capital.
  4. Investment Banking for Dummies
    Matthew Krantz and Robert Johnson have meticulously put together the concepts of investment banking in their masterpiece. It explains the fundamentals of banking concepts and their real-world applications, it is one of the best books if you are just starting with your investment banking journey.
  5. Investment Banking: Institutions, Politics & Law
    This book is written by Alan Morrison and William Wilhelm Jr. It traces the history of investment banking and gives a fresh perspective for those who want to learn about the fundamentals of investment banking and its origin.
  6. The Best Book On Investment Banking Careers
    If you want to cut the chase and know about careers in the investment banking domain then this book will surely clear the air. It is beautifully put together by Donna Khalife. The author sheds light on the basics of investment banking and the job roles in this domain.
  7. The Accidental Investment Banker
    The Accidental Investment Banker is authored by Jonathan A Knee. It gives an insider’s perspective on the investment banking industry from the 90s when the dot com bust had disrupted the investment banking sector. It creates an interesting story about an investment banking episode.
  8. The Business of Investment Banking
    K. Thomas Liaw is the author of this insightful book on investment banking. It takes a fresh perspective on the global investment banking industry and covers everything from underwriting to M&A and other functions of investment banking in the modern-day capitalist society.
  9. Financial Modelling and Valuation
    This book provides deeper insights into the basics of financial modeling and valuation techniques. It is written by Paul Pignataro who guides on making accurate stock valuations with the help of financial modeling techniques.
  10. Investment Banking Explained
    This masterpiece gives deep insights into how Investment banking works and an insider’s perspective on this industry. It is authored by Michael Fleuriet.

Why Data Scientists Should Follow Software Development Standards?

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Introduction

Technology has become the flagbearer of changes to which we are subjected to daily. Therefore, it impacts us in every possible way. How technology comes to us should mostly positively affect us. Therefore, it becomes important for the people driving this change to adhere to some pre-defined standards for improved quality of work and standardization of the same.

Data Science has come a long way. It has become one of the most popular subjects giving people the best in class in jobs and putting them in a position of the drivers of change. A Data Science course in Chennai would help you in becoming employment ready.

Data Science has enabled handling the bulk of data with ease. With Data Science you can drive different conclusions from the same set of data. You just need to change the algorithm.

Who is a data scientist?

Your Data Science career can bring a lot to the table. Initially, the word ‘Data Scientist’ was used for people who used to organize and analyze a huge amount of data. However, the role of a data scientist has drastically evolved in its due time course.

Today, data scientists develop algorithms that make sorting, compiling, and analyzing the sets of data a cakewalk. Effective data scientists have standardized the processes and have developed a standard procedure to work things out. These data scientists are technically well-equipped and can build complex algorithms which can be repeatedly used to make a task easy.

They have a strong quantitative background and are usually result oriented. Also, they have extensive knowledge of different programming languages like R, Python, Tableau, SQL, etc. As the demand for automatization is increasing, data scientists can access more and more jobs.

The need for data scientists to follow Software Development Standards

Standardization is important everywhere irrespective of the field. Therefore, these data scientists need to adhere to a specific set of software development standards that are already in place.

In the times where cybersecurity is a major issue, it is really important to have some software development standards in place. This would ensure that the new software is being designed keeping in mind these standards which will consider the safety and security of data and information of the end-users of that particular person.

Development standards have been also designed to keep uniformity across the organization. These standards ensure that the work output is generated at a certain level. Also, with software development standards, a set of consistent rules are laid down which makes the job of a data scientist quite easy.

With Software Development standards, you can use the same algorithm for different purposes with slight modifications. Also, it ensures that the program written by a data scientist is clear and understandable and adheres to the statistical principals. With standardization, codes will be written in a language that is understood by all.

Having simple rules is important. Software development standards follow a structured approach when it comes to writing a code or designing software. It bridges the gap between your research and the final product which you want to build.

These standards are up to date and are formulated keeping in mind different quality assurance standards. This would ensure that a quality product in the form of codes is delivered. With the implementation of these practices, it would be really easy for the data scientists to meet the requirements of their customers and deliver quality results.

Conclusion

Following a set of standard procedures can make the work of data scientists’ error-free to a great extent. Also, it enables easy quality checks ensuring good delivery of an end product.

 

Big Data Influences Online Trading in 3 Primary Ways!

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Organizations and corporates are using analytics and data to get insights into the market trends to make decisions that will have a better impact on their business. The organization involved in healthcare, financial services, technology, and marketing are now increasingly using big data for a lot of their key projects.

Big Data influences online trading in 3 primary ways:

1. Levels the playing field to stabilize online trade

Big Data analytics

Algorithmic trading is the current trend in the financial world and machine learning helps computers to analyze at rapid speed.

The real-time picture that big data analytics provides gives the potential to improve investment opportunities for individuals and trading firms.

 

 

2. Estimation of outcomes and returns

Big Data AnalyticsAccess to big data helps to mitigate probable risks on online trading and making precise predictions.

Financial analytics helps to tie up principles that affect trends, pricing, and price behavior.

3. Improves machine learning and deliver accurate predictions

Big data analytics training can be used in combination with machine learning and this helps in making a decision based on logic than estimates and guesses.

Big Data Analytics

The data can be reviewed and applications can be developed to update information on a regular basis for making accurate predictions.

 

The Art of Machine Learning!

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Machine learning is the application of Artificial Intelligence (AI) that enables systems to learn automatically and improve from experience without being programmed directly. Its main focus is on the development of programs that access data and use the same for self-improvement.

The machine learning process starts with observing data to look for similar patterns in any form and make better decisions in the future based on these trends. The main aim is to enable the computers to learn automatically and adjust actions accordingly without human intervention or assistance.

Data mining and predictive modeling involve similar processes as machine learning. Both these methods involve searching through data to look for patterns and then adjusting the program actions according to those patterns.

A common example of machine learning for people is shopping on the internet and being served ads related to it. This happens because online ad delivery is personalized almost in real-time by recommendation engines using machine learning.

Along with personalized marketing; detection of fraud, spam filtering, network security threat detection, predictive maintenance, and building news feeds are other common machine learning use cases.

Some machine learning methods
Machine learning algorithms are often categorized as supervised or unsupervised.

  • Supervised machine learning algorithms can apply past learnings to new data with the use of labeled examples to predict future events. It starts with the analysis of a known training dataset based on which the learning algorithm produces an inferred function to make predictions about the output values.Targets are provided by the system for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
  • Unsupervised machine learning algorithms – These types of algorithms are used when the information used to train is neither classified nor labeled. Unsupervised machine learning enables you to understand how systems can infer a function to describe a hidden structure from unlabeled data. The output given by the system is not right, but it explores the data and can draw conclusions from datasets to describe hidden structures from unlabeled data.
  • Semi-supervised machine learning algorithms have qualities of both supervised and unsupervised learning since they use both labeled and unlabeled data for training. Mostly, these algorithms use a small amount of labeled data and a large amount of unlabeled data. Learning accuracy is considerably improves in systems using this method.When the acquired labeled data requires skilled and relevant resources in order to train it or learn from it, semi-supervised machine learning is used. Otherwise, additional resources are generally not required for acquiring unlabeled data.
  • Reinforcement machine learning algorithms is a learning method that collaborated with its environment by delivering actions and finds errors or rewards. The most pertinent characteristics of reinforcement learning are trial and error search and delayed reward.Machines and software agents can automatically determine the ideal behavior within a particular context in order to maximize their performance using this method of machine learning. Simple reward feedback is required for the software specialist to learn which action is best and is known as the reinforcement signal.

Large quantities of data can be analyzed using machine learning. It identifies profitable opportunities or dangerous risks by delivering faster, more accurate results; however, it may also require additional time and resources to train it properly.

Large volumes of information can be processed more effectively if machine learning is combined with AI and cognitive technologies.

For example, Facebook’s News Feed customizes each user’s feed with the help of machine learning. If a user frequently likes or shows any activity on a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the user’s feed.

At the backend, the software is simply using statistical analysis and predictive analytics to identify patterns in the user’s data and use those patterns to populate his/her News Feed. If the user no longer shows interest to read, like, or comment on the friend’s posts, that new data will be included in the dataset and the News Feed will update accordingly.

Is Working from Home Fintech’s ‘New Normal’?

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Many organizations had not even imagined about running a business with their employees working remotely from the convenience of their homes. Work from home was considered as a privilege to selected profiles at unavoidable circumstances.

Yet, it was looked down as a less productive arrangement as the employers presumed many household distractions that could hamper the productivity of the employee. However, there is a tectonic shift in these concepts after the COVID-19 hit the world.

Many weeks into the remote working arrangement, companies have started to realize that they can maintain productivity with their employees working from home. The perception that employees would relax at home while working from home rather than concentrating on their work has completely changed. This could bring a major shift in the structure and design of fintech courses in the future.

Benefits of Working Remotely

The most noticeable benefit is that the remote working arrangement is that companies can access talents from different geographical regions. Companies have started thinking that they don’t want to restrict their talent pool to any specific geographical area. This could be highly motivating to those who are planning to enrol in a fintech course.

Many companies have felt that their employees are working dedicatedly even while working remotely. The fact that even many weeks into the lockdown, there isn’t any drop in productivity. This has led to many companies now willing to test this model going forward, even after the lockdown. This arrangement has shown to improve the morale and motivation of the employees.

Tools Required for Remote Working

Companies have started using digital tools to create a virtual work environment. The teams are staying connected with tools like Zoom and Slack for video meetings and communications respectively. Many companies have enabled SSL VPN to ensure a secure network. Other popular tools are Hangout, Microsoft Teams, Trello and Google Docs.

Challenges of Remote Working

The data protection is the biggest challenges faced by organizations. They are not encouraging employees to store data locally. Small fintech might find it still feasible to provide work from home facilities to their employees; big organizations with hundreds of employees or more are still working out their ways to cope with this new arrangement. They are looking at cloud-based solutions to ensure a seamless transition of office model to work from home arrangement. If remote working becomes a new normal, this could be an area of focus for fintech courses.

Dealing with cyber fraud and malicious acts remain a serious concern for many fintech and banks. This has led to many fintech companies considering investing in reliable cybersecurity tools.

Many companies have introduced working with calendar system to ensure transparency and to know how their employees are spending their days and what are they working on at a given point in time. This will also help companies to monitor how long an employee takes to finish an assigned task. This has helped increase productivity. Daily, weekly, and monthly updates are also helpful to monitor the team output.

In some ways, remote working is benefiting the fintech. Many banks have now come forward to join hands with fintech companies to partner with them at different stages of the customer journey. They are actively looking to shift their non-branch-based functions to remote workplaces.

Along with employees, fintech companies must take care of the needs and convenience of their customers as well. Many fintech companies have created lighter versions of their tech services so that their customers can use them on lower bandwidth.

Conclusion

Fintech companies can implement seamless working from home arrangements with effective communication and with collaboration tools. Many companies have already found a workaround to facilitate remote working for a longer period. Many have said that they would consider moving a higher percentage of their workforce to remote working post-lockdown. However, it is too early to comment if work from home will be the new norm. However, many companies are planning to keep this as a possibility, even though it greatly depends upon the government rules.

The Role of AI in Minimising Physical Contact in Public Spaces!

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The novel coronavirus pandemic has forced a majority of countries around the world to enforce lockdowns. Although met with initial resistance, a large chunk of the global population has stuck to social distancing and shelter-in-place norms, allowing the curve to be flattened.

As countries now begin to emerge out of lockdowns in phases, the focus will turn to maintain high standards of sanitation and hygiene. This is to avoid undoing the work that has been done over the past few months as well as set new norms for effective mitigation and disease controls. Amongst these, processes to minimize the frequent touching of common surfaces in public spaces will certainly feature.

So far, however, all efforts have been wholly dependent on manual efforts and individual dedication to social distancing and mitigation. AI can be pivotal in the efforts to curb the touching of surfaces in public areas without banking on individuals entirely.

Here’s how:

  • Contactless Access Systems

Tech titans are currently exploring the use of technologies for facial recognition to monitor the social distance between staff members. These can also be taken one step further to be combined with thermal scanning; when paired, this system can regulate who enters and exits the front doors in just a few seconds.

Machine Learning

This system also negates the need for touch-and-go biometric scanners or ID scanners which often become a collection point for employee throughout the day. Artificial Intelligence can be used to virtually cordon off some parts of the office as well as maintain control over how many times a person touches their face in a day (which is one of the quickest methods of COVID19 transmission).

  • Leveraging Voice Commands

Voice functionality has penetrated many aspects of human lives– and it’s only set to increase. Voice commands can be used to operate systems in public spaces such as bathrooms, elevators, entryways and cubicles to minimize the risk of contact. It can also be implemented at the water cooler, in the printing room and in office pantries, which are often places that see the highest footfall in large-scale organisations. Voice functionality can be implemented by integrated voice assistants and or smartphone apps. Aside from voice commands, gestures can also be used to minimizing the frequency of touching high-risk surfaces such as flushes, taps, door handles and elevator buttons.

  • Smart Handles and Locks

Doorknobs and handles are high-priority areas for sanitation teams given that we subconsciously handle them every day. AI can be implemented to reduce the need to physically touch handles to open doors. Technology can be used to kick into motion self-locking or gesture-controlled mechanisms. In a case where physical touch is absolutely required, AI can also be used to trigger the dispensing of antibacterial coatings or single-use sanitary sleeves. Newer inventions that use these technologies are able to be retrofitted onto existing doorknobs and handles, making them a quick fix to the sanitation problem in this aspect.

  • Location and Distance Tracking

Although some industries are slowly opening up, others have seen an influx of workers considered essential. However, that doesn’t reduce the need for strict social distancing measures, which is where AI comes into the picture. Artificial Intelligence can be used to account for the location of every employee in the facility and alert them if they have crossed social distancing boundaries.

Additionally, AI can also be used to demarcate spaces in queues and cubicles to maintain distance between employees. This system can be implemented through smartphone apps or wearable devices such as smartwatches.

Conclusion

Even after the pandemic loosens its hold, social distancing is slated to become the new norm. Businesses looking to leverage AI to maintain these rules without manual labour can consider upskilling their IT team through an artificial intelligence course or Machine learning training to ensure they’re achieving their potential.

What Impact is Covid-19 Having on Global Fintech?

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The world has almost come to a standstill due to the COVID-19 outbreak. Several economies got hit, resulting in the worst economic shock in world history. However, the survival of the fittest is the norm. Digital and tech companies are preparing for a big paradigm shift in terms of their organizational structure and working models.

The business must continue, and many organizations have switched to a remote working model. Fintech is one among the worst-hit business and must exhibit a substantial resilience to cope with this change. After some minimal initial hitches, employers and employees are slowly getting comfortable with the new arrangement. As face-to-face interactions and cash transactions are witnessing a sharp decline, many fintech companies are experiencing a surge in the demand for their services.

What are the Main Challenges to Fintech?

Fintech deals with extremely confidential data, and cybersecurity is one of their biggest concerns. With most of the transactions and activities happening online, hackers would be trying to break into these networks to gain access the sensitive data. Any such incidence that blows away the customer trust could be damaging to the industry. This has led to the fintech firms scaling up their cybersecurity measures with more investments in that front.

Similarly, storage of information is another challenge. The fact that fintech employees deal with a huge amount of confidential data emphasises the need for cloud storage. Many fintech companies have strictly banned their employees from using local storage for keeping information.

These companies are relying on cloud-based storage to ensure data protection. Similarly, they have enabled VPN to ensure secure office network.

Moving to a Remote Working Plan

This could be the last thing any fintech would have ever preferred. However, the coronavirus outbreak has forced to rethink the way work has to be done. Though the situations compelled the companies to switch to the new working arrangement,

Market giants such as Google, Facebook, Twitter etc. have instructed both the employees and the companies are getting comfortable with passing time. fintech in the middle east like the UAE and Bahrain have announced their plans to move towards working from home to ensure the safety of employees while still supporting the resident’s needs.

Similarly, countries across the globe have asked the companies to make necessary arrangements for their employees to work from home.

Fintech companies are known for their fast adaptation to the changing environment. One major reason behind this is that they are driven by newest technology. They use cloud-based systems and the latest software, and all these supports remote working.

However, this unprecedented situation has proven that working from home doesn’t mean less productivity. It has been several weeks into this new arrangement and the companies have not seen any considerable dip in productivity.

On the traditional business front, on the other hand, customers are demanding for fast adaptation of technology to facilitate online business and transactions, and this has increased the demand for fintech services, Banks, for example, are actively consulting fintech companies to help them move towards technology-driven business.

Wealth management companies are also looking to move towards the online business model. Compared to 2019, there is a considerable increase in their online users and many financial advisors have started to offer their services online.

COVID-19 and the consequential lockdown have called for a quick adaptation to a new business and work model with extreme urgency. Although the global economic crisis and recession have posed fresh challenges to the fintech companies, they are adapting to the new arrangement without much problem.

However, the coming days are challenging for fintech companies because of the massive change happening in the global economic landscape. Success and survival of fintech companies largely depend upon how fast and efficiently they are coping up with the challenges, and how fast they could come up with innovative business solutions to deal with this crisis.

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