What Are Earnings Per Share And What You Need To Know

THE STOCK DOR | April 20, 2018

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Earnings per share are the amount of money earned for each share of stock outstanding. This is a profitability measure for companies. The calculation is as follows: EPS = (net income – dividends on preferred stock)/outstanding shares. Furthermore, EPS requires both the income statement and the balance sheet for calculations. Net income comes from the income statement. Conversely, the balance sheet reveals total outstanding shares and dividends. Intelligent analysts use a weighted average of the outstanding shares as the number changes over time.

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BEHAVIOURAL ANALYTICS WILL FUEL CUSTOMER INTERACTION IN FINANCIAL SERVICES

Article | June 1, 2021

The axing of third-party cookies by Google and the other major browser companies will require a major readjustment by financial services organisations. The decision, coming fully into force next year, will effectively choke off the data that has enabled personalisation, optimised website interactions and driven much internet advertising. It is no comfort that the browser companies have acted because of fears about infringement of privacy and data protection legislation such as the EU’s GDPR (General Data Protection Regulation) and the CCPA (California Consumer Privacy Act) in California. The move will affect how UK financial services organisations interact with millions of people. More than three-quarters of Britons now use online banking and 14 million use digital-only banks, expecting a slick, light-touch interaction. So it appears that just as many people go digital, financial services organisations will no longer have access to information they need for personalisation, being unable to track where customers go on the internet after they have visited a bank’s website. All that data about individuals’ habits and preferences will be unavailable. It seems catastrophic, but in reality, it is not. Financial organisations have a new opportunity to radically improve how they interact with web visitors and customers. AI-powered behavioural analytics offer far superior, real-time capabilities, using the data from the first-party cookies on their own website domains and where available, data from customers’ transaction histories. The result is a solution that is faster, more accurate and responsive than conventional technology relying on cookie data owned and stored by third-party organisations. Instead of relying on such data for relatively rigid profiling and personalisation, behavioural analytics enables real-time interactions based on a more dynamic picture of how an individual’s requirements are changing. The technology analyses all the browsing characteristics including time on site, speed of movement and page views, as well as more obvious features such as interest in specific products. Historical data added to the analysis includes what customers did on previous visits and the interval between those visits, establishing patterns where possible. The flexible advantages of behavioural analytics hubs in financial services Segmentation allows a bank to identify customers as soon as they arrive on its site, according to whether they are a new or existing customer. Their behaviour then indicates what they want. Knowing what customers are interested in is important. Customers visit financial services websites for a host of reasons – from seeking information, to opening accounts, exploring loans and mortgage offers, making or setting up new payments. They may also want advice about investments and savings, pensions or small business finance. Almost all of these requirements involve quite complex mental processes which financial organisations can influence while consumers are on their sites. Collecting the data is not difficult – the skill is in making it actionable in an effective way, replicating the ability of a perceptive employee to read a customer’s state of mind. Banks can do this by setting up a behavioural analytics hub to understand what a customer’s behaviour means and how it can be optimised. Using customised parameters, the hub will, for instance, trigger a screen notification that prompts the web visitor to fill in a form requesting an appointment. In the case of existing customers, the technology can correlate health insurance offers with spending on fitness, and, in general, savings and investment recommendations can be tailored to the client’s concerns or goals as revealed by their navigation of a bank’s website or mobile app. Banks can set up analytics to see when consumers are behaving in a way that indicates they about to leave the website, allowing them to intervene with a notification that could include an offer. This provides a positive outcome and avoids the blanket use of offers that undermines profitability. It is a more sophisticated and personalised approach that avoids annoying pop-ups or recommendations that fail to match individual preferences. As part of a single AI-powered segmentation platform, the technology enables banks to personalise marketing content in SMS messages and emails sent to consumers (who consent), which deliver far better results through precise targeting. Solutions for last-mile interaction in the open banking era The single platform approach also has another major advantage. It is much easier to implement and far more efficient and streamlined compared with separate solutions for different parts of the customer journey. The benefits of using AI-powered segmentation solutions should be part of the financial sector’s broader strategy to transform its systems for the open banking era as we approach the end of third-party cookies. For established banks, the reality for some time has been that complexity of systems has undermined their ability to deliver a high-quality last mile. This they can now address without huge disruption or investment. The alternative is for financial services organisations to become lost on an ocean of data, losing track of customers. Behavioural analytics will bring banks new insights into customers that surpass third-party cookie data, being actionable and accurate and in real time. To provide a streamlined and profitable experience for themselves and their millions of customers, banks must now employ the latest advances in AI-powered behavioural analytics.

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The Importance of Financial Literacy During Uncertain Economic Times

Article | April 7, 2020

What does it mean to be financially literate? Is it more important to be able to balance a checkbook or to understand the power of compound interest? Does a financially literate person pay down student debt or consumer debt first? And does a truly financially literate person even take on debt in the first place? A growing number of fintechs – many of them Finovate alums you’ll meet below – have devised innovative ways to help young people in particular, become better earners, savers, spenders, and investors.

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Machine Learning in Finance – Present and Future Applications

Article | March 4, 2020

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google’s Tensorflow).

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The Covid-19 impact on fintech: now is the time to boost investment

Article | April 20, 2020

While the coronavirus pandemic sweeps the entire world, governments’ response to the international health crisis has led the global economy to a standstill. Extensive lockdowns and social distancing measures are leading to massive unemployment spikes and numerous businesses are already seeing dramatic loss of revenue and struggling to maintain their operations.

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Domestic company providing advisory services, research and risk analysis for customers throughout the United States. Specializing in commercial real estate from the property level to the bond level.

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