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Factors for designing the perfect forecast



Factors for designing the perfect forecast

By Neville Isaac, Chief Customer Officer

Tips to improve competitiveness by designing a forecast adapted to your product

Neville Isaac

Neville Isaac

In a competitive market it is essential to find reliable formulas that allow us to establish a pattern that will simplify the task of achieving our revenue goals. Market competition and the emerging Big Data phenomenon provide the ideal scenario in which to understand what influences the consumer’s decision-making processes and how a business can use this to its advantage. This does not mean exerting some kind of control over the customer, but understanding the market better in order to optimize processes and adapt products to the potential needs of an increasingly demanding customer.

Developing a forecast is conceived as a way of “providing information” to the revenue management department to forecast the income and variables that influence an establishment’s growth. The procedure for making an estimate of expected income for future periods involves studying growth in previous years, analyzing current booking processes, as well as parameters and future availability so as to transfer to the accommodation what should be the most competitive market.

Price variables such as competitor set, demand, brand focus, target, sociopolitical changes, past events that have affected positively or negatively and future events related to the sector, directly influence the reservation processes of consumers and, therefore, affect competitiveness.

Importance of forecasting

By forecast, we mean the prediction of demand during a certain time period. As specialists in hospitality, we look at elaborating a correct forecast to anticipate future performance and reduce uncertainty about what will happen in the coming months. By analyzing historical and current results, as well as market trends and consumer and competitor behavior, the hotelier can make projections using the main result indicators: occupation, average price, revenue per room, income, etc.

The goal of forecasting is to better understand where the business is going and make the necessary strategic adjustments to obtain greater profitability. In Revenue Management, we can predict demand through forecasting, which will simplify the task of making strategic decisions to optimize inventory and pricing in the aim of maximizing income and operating profit.

A forecast, broken down by consumer market segments, will make it easier to identify the most profitable customers, so that we can take specific action aimed at providing the best possible customer experience, and thus win the loyalty of the most interesting target audience. By focusing on demand prediction, we can establish pricing customization strategies, working from the point of view of Customer-Centric Revenue Management.

Developing a forecast is a job for the Revenue Manager: many use basic spreadsheets to tackle this task. Bear in mind that an inadequate forecast can lead to poor decision-making, and thus impact negatively on revenue and profit margins, therefore investing in technology is highly profitable in optimizing the process of collecting data, synthesizing information and making decisions.

Since the forecast provides medium and long term insights, the work of the Revenue Manager can be challenging especially since it needs to be done on a daily or weekly basis, especially if broken down by segments or by booking channels, in which case gathering information can be an arduous task.

The importance of forecasting has a direct impact on the market intelligence that we can apply to the day-to-day running of an establishment.

Preparing an adequate forecast

Given the high expectations regarding the importance of forecasting, it is important for us to lay down the elements that affect this model. Both data collection, and the human and technological ability to interpret them will be decisive when developing the forecast. True business forecasts consider the following data:

Historical Data

Collecting and analyzing operational data is essential for a correct estimation of demand, our understanding of demand being the number of potential customers who are willing and able to acquire our product at a certain price.

When talking about data, we are not only referring to the historical results, for example in a hotel, we may consider occupation, average price and income, but also to many other indicators provide information about the behavior of our customers in the past, and any situation or event that has impacted demand.

Breaking down data by segments, markets and marketing channels, will allow us to access a much more accurate analysis, so that we can identify which are the most profitable customers that can provide a sustainable improvement in hotel profit.

Current Data

It is essential to relate results from past periods to what is happening now. In this respect, it is necessary at this point to collect data regarding previous indicators for reservations that we already have confirmed and compare it with the same time for the previous year. This allows us to check whether demand behavior is similar to that in the previous year, and to detect whether there have been variations in trends.

If we know the existing offer for a destination, this will inform us about alternative products, their level of quality and their price-performance ratio. By using this information, we can identify our competitive positioning and the reasons why clients choose our hotel against those of our competitors.

Future data

Forecasting tries to predict the demand in a future period of time. We must therefore consider all the demand generators that we can predict and those we know will have an impact on our bottomline, either because they have already occurred in the past, which we expect to be repeated with a similar impact, or because they are new events which we know will generate demand peaks.

Access to information sources

The goal of forecasting is to estimate future demand as precisely as possible. Thus, we must make a comprehensive analysis of data and information from both inside and outside the hotel. The technological challenge is to access reliable and precise data sources that we will need at all times. Technology helps in this respect, allowing us to use different tools that will enable us to collect data and organize information.

Types of forecast

In order to reduce costs associated with excess expenditure and opportunity costs, it is crucial to establish a cross-sectional revenue system for different departments. To ensure that the forecast is suited to our predictions, it is important for to know the two types of forecasts available:

  • Demand forecast. This is the most important of the two, especially for perishable products like a hotel room.  We estimate demand by the type of room, market and segment, regardless of hotel capacity. It may so happen that we end up with an estimate of demand that exceeds the number of rooms in the hotel, because a demand generator at the destination triggers booking requests around that date or period (for example, a football match, a concert, or public holidays). In this case we talk about unconstrained demand or unlimited demand.

It is essential to know dates which we can predict an excess of demand, as this will affect the strategic decisions to optimize the hotel’s results. The opposite case is that of demand deficit: dates on which the forecasts tell us that there is not enough demand to fill the hotel. Identifying these dates is of equal importance when making decisions.

  • Capacity Forecast. The capacity forecast will allow us to estimate future income. According to the demand forecast, we can predict what part of that demand we will be able to accommodate and what income bookings will generate. If we have total expenditure statistics by segment, such as breakfast catch rate or average expenditure on food and beverages, we can also estimate income in other departments, while also predicting how resources and raw material needs will be allocated.

The capacity offered by these types of forecasts will determine the ability to react to different market situations. An information flow that feeds into decision-making in each of our departments will improve performance and satisfaction in a hotel that is connected to the needs of your market.

Forecasting phases

Developing an adequate forecast means implementing a well-structured and well-informed methodology by which to make decisions. Traditionally, this process has been developed manually. Currently, technology provides tools that aids in the capture, generation, calculation and implementation of conclusions regarding forecasts.

We propose a forecasting process that is designed in five well-defined phases:.

  1. Collect historical data:

Data about what happened in the past will help us to understand market movements. Traditionally, these data have been the cornerstone of any forecasting process, although this is currently getting more complicated, as decision-making processes can be enriched with new sources through which we can interpret the market.

  1. Identify causes:

Patterns for more complex market situations will allow us to estimate what associated problems may arise and estimate what approximate percentage the accommodation should assume. These kinds of eventualities are more pronounced when the distribution is more decentralized from our own booking channels, and that is where we will have to control these booking specifications.

  1. Demand behavior:

Identifying and developing strategies in the main market segments will make it easier to work on the needs of each of them with policies and totally differentiated products. This type of approach will help to be more precise and consistent with the peculiarities and perceptions of the different types of accommodation traveler:

  1. Compare:

Once historical and current data regarding demand behavior and our establishment’s performance indicators have been identified and analyzed, we must compare current trends with those of the previous year. Thus, we will be able to identify variations in trends and look for the causes, in order to make decisions about how to deal with the variations.

  1. Adjust forecasts

Compare your policies with the rest of the market. What had traditionally been done first becomes the final part of a process of study and analysis. Determining a competitive set for both products and destinations based on our hotel’s objectives will be crucial to see the position that we occupy in the market.

Objective quality vs online reputation

Nowadays, travelers can use multiple information tools to reserve a room in a market saturated with accommodation and information. With this is mind, and in the interests of competitiveness, all hoteliers and accommodation managers must understand the numerous factors that condition the booking/purchase processes, analyzing everything from the qualities of the hotel and the configuration of available services, to overall experience throughout the booking process: a full set of factors that influence the price the customer is willing to pay at all times.

Online reputation, which is based on post-stay opinions, is a way of measuring the degree to which the customer’s expectations of quality during the purchase process has been met. Fulfillment of expectations will be measured according to the extent to which these conform to the reality of the hotel, that is to say, its objective quality and how it communicates this, and the way in which this provides the consumer with a realistic perception of this quality.

Objective quality and online reputation are two factors that should be constantly fed back into the organisation, and then to what extent that the hotel has understood the needs of the market, levels of satisfaction of the traveler will be improved.

Beonprice has created the Hotel Quality Index (HQI®): the only index of the hotel market that measures the integral quality of a hotel to know the competitive positioning and the price elasticity in the market, depending on each of the traveler segments.

The HQI® takes into account more than 350 objective parameters such as location, hotel services, catering, room size, etc., as well as online reputation. This index summarizes customer-booking behavior taking into account the quality expectation of the establishment before and after the reservation. The HQI®, as an innovator in the market, will continue its evolution on the calculation of the price elasticity that the market accepts for each accommodation. That is, the key information so that the recommendation is much more precise in order to increase the net RevPAR.

The HQI® takes into account the different perception of quality in each customer segment, which will enable a hotel to identify the most profitable segments, and work on a day-to-day basis from the standpoint of a customer-centric revenue management. Thus, what we seek is greater customization of price, so that we can achieve loyalty through optimizing prices and reducing acquisition costs in the future.

Probability of sale

Identifying competitive positioning allows the accommodation to obtain a new indicator that will directly impact the forecast: the probability of sale.

Given that we have information about the price the customer is willing to pay for a certain standard of quality, and we know our level of quality plus competitor quality and price details, we can determine the probability that a customer will decide to make a reservation. This increases precision when preparing our forecast.

Considering the probability of sale constitutes a new approach to forecasting that was, until now, impossible, since there was no access to technology that would allow this analysis. It is a new way of working that provides greater precision when calculating the demand forecast.

We believe that the key to success lies in using artificial intelligence to manage Big Data and know the behavior of demand. Having the perception of the integral quality of each establishment by each segment will allow the business to define its competitive positioning at all times.

This very precise analysis of demand will enable us to know what price the customer is willing to pay to meet their expectation in order to calculate a probability of sale. From here, businesses can establish strategies to optimize results, based on live data obtained.

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TCI: A time of critical importance



TCI: A time of critical importance 1

By Fabrice Desnos, head of Northern Europe Region, Euler Hermes, the world’s leading trade credit insurer, outlines the importance of less publicised measures for the journey ahead.

After months of lockdown, Europe is shifting towards rebuilding economies and resuming trade. Amongst the multibillion-euro stimulus packages provided by governments to businesses to help them resume their engines of growth, the cooperation between the state and private sector trade credit insurance underwriters has perhaps missed the headlines. However, this cooperation will be vital when navigating the uncertain road ahead.

Covid-19 has created a global economic crisis of unprecedented scale and speed. Consequently, we’re experiencing unprecedented levels of support from national governments. Far-reaching fiscal intervention, job retention and business interruption loan schemes are providing a lifeline for businesses that have suffered reductions in turnovers to support national lockdowns.

However, it’s becoming clear the worst is still to come. The unintended consequence of government support measures is delaying the inevitable fallout in trade and commerce. Euler Hermes is already seeing increase in claims for late payments and expects this trend to accelerate as government support measures are progressively removed.

The Covid-19 crisis will have long lasting and sometimes irreversible effects on a number of sectors. It has accelerated transformations that were already underway and had radically changed the landscape for a number of businesses. This means we are seeing a growing number of “zombie” companies, currently under life support, but whose business models are no longer adapted for the post-crisis world. All factors which add up to what is best described as a corporate insolvency “time bomb”.

The effects of the crisis are already visible. In the second quarter of 2020, 147 large companies (those with a turnover above €50 million) failed; up from 77 in the first quarter, and compared to 163 for the whole of the first half of 2019. Retail, services, energy and automotive were the most impacted sectors this year, with the hotspots in retail and services in Western Europe and North America, energy in North America, and automotive in Western Europe

We expect this trend to accelerate and predict a +35% rise in corporate insolvencies globally by the end of 2021. European economies will be among the hardest hit. For example, Spain (+41%) and Italy (+27%) will see the most significant increases – alongside the UK (+43%), which will also feel the impact of Brexit – compared to France (+25%) or Germany (+12%).

Companies are restarting trade, often providing open credit to their clients. However, there can be no credit if there is no confidence. It is increasingly difficult for companies to identify which of their clients will emerge from the crisis from those that won’t, and whether or when they will be paid. In the immediate post-lockdown period, without visibility and confidence, the risk was that inter-company credit could evaporate, placing an additional liquidity strain on the companies that depend on it. This, in turn, would significantly put at risk the speed and extent of the economic recovery.

In recent months, Euler Hermes has co-operated with government agencies, trade associations and private sector trade credit insurance underwriters to create state support for intercompany trade, notably in France, Germany, Belgium, Denmark, the Netherlands and the UK. All with the same goal: to allow companies to trade with each other in confidence.

By providing additional reinsurance capacity to the trade credit insurers, governments help them continue to provide cover to their clients at pre-crisis levels.

The beneficiaries are the thousands of businesses – clients of credit insurers and their buyers – that depend upon intercompany trade as a source of financing. Over 70% of Euler Hermes policyholders are SMEs, which are the lifeblood of our economies and major providers of jobs. These agreements are not without costs or constraints for the insurers, but the industry has chosen to place the interests of its clients and of the economy ahead of other considerations, mindful of the important role credit insurance and inter-company trade will play in the recovery.

Taking the UK as an example, trade credit insurers provide cover for more than £171billion of intercompany transactions, covering 13,000 suppliers and 650,000 buyers. The government has put in place a temporary scheme of £10billion to enable trade credit insurers, including Euler Hermes, to continue supporting businesses at risk due to the impact of coronavirus. This landmark agreement represents an important alliance between the public and private sectors to support trade and prevent the domino effect that payment defaults can create within critical supply chains.

But, as with all of the other government support measures, these schemes will not exist in the long term. It is already time for credit insurers and their clients to plan ahead, and prepare for a new normal in which the level and cost of credit risk will be heightened and where identifying the right counterparts, diversifying and insuring credit risk will be of paramount importance for businesses.

Trade credit insurance plays an understated role in the economy but is critical to its health. In normal circumstances, it tends to go unnoticed because it is doing its job. Government support schemes helped maintain confidence between companies and their customers in the immediate aftermath of the crisis.

However, as government support measures are progressively removed, this crisis will have a lasting impact. Accelerating transformations, leading to an increasing number of company restructurings and, in all likelihood, increasing the level of credit risk. To succeed in the post-crisis environment, bbusinesses have to move fast from resilience to adaptation. They have to adopt bold measures to protect their businesses against future crises (or another wave of this pandemic), minimize risk, and drive future growth. By maintaining trust to trade, with or without government support, credit insurance will have an increasing role to play in this.

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What Does the FinCEN File Leak Tell Us?



What Does the FinCEN File Leak Tell Us? 2

By Ted Sausen, Subject Matter Expert, NICE Actimize

On September 20, 2020, just four days after the Financial Crimes Enforcement Network (FinCEN) issued a much-anticipated Advance Notice of Proposed Rulemaking, the financial industry was shaken and their stock prices saw significant declines when the markets opened on Monday. So what caused this? Buzzfeed News in cooperation with the International Consortium of Investigative Journalists (ICIJ) released what is now being tagged the FinCEN files. These files and summarized reports describe over 200,000 transactions with a total over $2 trillion USD that has been reported to FinCEN as being suspicious in nature from the time periods 1999 to 2017. Buzzfeed obtained over 2,100 Suspicious Activity Reports (SARs) and over 2,600 confidential documents financial institutions had filed with FinCEN over that span of time.

Similar such leaks have occurred previously, such as the Panama Papers in 2016 where over 11 million documents containing personal financial information on over 200,000 entities that belonged to a Panamanian law firm. This was followed up a year and a half later by the Paradise Papers in 2017. This leak contained even more documents and contained the names of more than 120,000 persons and entities. There are three factors that make the FinCEN Files leak significantly different than those mentioned. First, they are highly confidential documents leaked from a government agency. Secondly, they weren’t leaked from a single source. The leaked documents came from nearly 90 financial institutions facilitating financial transactions in more than 150 countries. Lastly, some high-profile names were released in this leak; however, the focus of this leak centered more around the transactions themselves and the financial institutions involved, not necessarily the names of individuals involved.

FinCEN Files and the Impact

What does this mean for the financial institutions? As mentioned above, many experienced a negative impact to their stocks. The next biggest impact is their reputation. Leaders of the highlighted institutions do not enjoy having potential shortcomings in their operations be exposed, nor do customers of those institutions appreciate seeing the institution managing their funds being published adversely in the media.

Where did the financial institutions go wrong? Based on the information, it is actually hard to say where they went wrong, or even ‘if’ they went wrong. Financial institutions are obligated to monitor transactional activity, both inbound and outbound, for suspicious or unusual behavior, especially those that could appear to be illicit activities related to money laundering. If such behavior is identified, the financial institution is required to complete a Suspicious Activity Report, or a SAR, and file it with FinCEN. The SAR contains all relevant information such as the parties involved, transaction(s), account(s), and details describing why the activity is deemed to be suspicious. In some cases, financial institutions will file a SAR if there is no direct suspicion; however, there also was not a logical explanation found either.

So what deems certain activities to be suspicious and how do financial institutions detect them? Most financial institutions have sophisticated solutions in place that monitor transactions over a period of time, and determine typical behavioral patterns for that client, and that client compared to their peers. If any activity falls disproportionately beyond those norms, the financial institution is notified, and an investigation is conducted. Because of the nature of this detection, incorporating multiple transactions, and comparing it to historical “norms”, it is very difficult to stop a transaction related to money laundering real-time. It is not uncommon for a transaction or series of transactions to occur and later be identified as suspicious, and a SAR is filed after the transaction has been completed.

FinCEN Files: Who’s at Fault?

Going back to my original question, was there any wrong doing? In this case, they were doing exactly what they were required to do. When suspicion was identified, SARs were filed. There are two things that are important to note. Suspicion does not equate to guilt, and individual financial institutions have a very limited view as to the overall flow of funds. They have visibility of where funds are coming from, or where they are going to; however, they don’t have an overall picture of the original source, or the final destination. The area where financial institutions may have fault is if multiple suspicions or probable guilt is found, but they fail to take appropriate action. According to Buzzfeed News, instances of transactions to or from sanctioned parties occurred, and known suspicious activity was allowed to continue after it was discovered.

Moving Forward

How do we do better? First and foremost, FinCEN needs to identify the source of the leak and fix it immediately. This is very sensitive data. Even within a financial institution, this information is only exposed to individuals with a high-level clearance on a need-to-know basis. This leak may result in relationship strains with some of the banks’ customers. Some people already have a fear of being watched or tracked, and releasing publicly that all these reports are being filed from financial institutions to the federal government won’t make that any better – especially if their financial institution was highlighted as one of those filing the most reports. Next, there has been more discussion around real-time AML. Many experts are still working on defining what that truly means, especially when some activities deal with multiple transactions over a period of time; however, there is definitely a place for certain money laundering transactions to be held in real time.

Lastly, the ability to share information between financial institutions more easily will go a long way in fighting financial crime overall. For those of you who are AML professionals, you may be thinking we already have such a mechanism in place with 314b. However, the feedback I have received is that it does not do an adequate job. It’s voluntary and getting responses to requests can be a challenge. Financial institutions need a consortium to effectively communicate with each other, while being able to exchange critical data needed for financial institutions to see the complete picture of financial transactions and all associated activities. That, combined with some type of feedback loop from law enforcement indicating which SARs are “useful” versus which are either “inadequate” or “unnecessary” will allow institutions to focus on those where criminal activity is really occurring.

We will continue to post updates as we learn more.

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How can financial services firms keep pace with escalating requirements?



How can financial services firms keep pace with escalating requirements? 3

By Tim FitzGerald, UK Banking & Financial Services Sales Manager, InterSystems

Financial services firms are currently coming up against a number of critical challenges, ranging from market volatility, most recently influenced by COVID-19, to the introduction of regulations, such as the Payment Services Directive (PSD2) and Fundamental Review of the Trading Book (FRTB). However, these issues are being compounded as many financial institutions find it increasingly difficult to get a handle on the vast volumes of data that they have at their disposal. This is no surprise given that IDC has projected that by 2025, the global “datasphere” will have grown to a staggering 175 zettabytes of data – more than five times the amount of data generated in 2018. As an industry that has typically only invested in new technology when regulations deem it necessary, many traditional banks are now operating using legacy systems and applications that haven’t been designed or built to interoperate. Consequently, banks are struggling to leverage data to achieve business goals and to gain a clear picture of their organisation and processes in order to comply with regulatory requirements. These challenges have been more prevalent during the pandemic as financial services firms were forced to adapt their operations to radical changes in customer behaviour and increased demand for digital services – all while working largely remotely themselves.

As more stringent regulations come in to play and financial services firms look to keep pace with escalating requirements from regulators, consumer demand for more online services, and the ever-evolving nature of the industry and world at large, it’s vital they do two things. Firstly, they must begin to invest in the technology and processes that will allow them to more easily manage the data that traditional banks have been collecting and storing for upwards of 50 years. Secondly, they must innovate. For many, the COVID-19 pandemic will have been a catalyst for both actions. However, the hard work has only just begun.

Legacy technology

Traditionally, due to tight budgets and no overarching regulatory imperative to change, financial institutions haven’t done enough to address their overreliance on disconnected legacy systems. Even when faced with the new wave of regulation that was implemented in the wake of the 2008 banking crash, financial services organisations generally only had to invest in different applications on an ad hoc basis to meet each individual regulation. However, as new regulations require the analysis of larger data sets within smaller processing windows, breaking down any and all data siloes is essential and this will require financial institutions that are still reliant on legacy systems to implement new technologies to meet the regulatory stipulations.

With this in mind, solutions which offer high-quality data analytics and enhanced integration will be key to the success of financial institutions and crucial to eliminate data silos. This will enable organisations to achieve a faster and more accurate analysis of real-time and historical data no matter where they are accessing the data from within smaller processing windows to keep pace with regulatory requirements, while also benefiting from low infrastructure costs.

This technology will also play a huge part in helping financial institutions scale their online operations to meet demand from customers for digital services. According to PNC Bank, during the pandemic, it saw online sales jump from 25% to 75%. Therefore, having data platforms that are able to handle surges in online activity is becoming increasingly important.

Real-time analysis of data

Tim FitzGerald

Tim FitzGerald

While the precise solution financial services institutions need will differ based on the organisation, broadly speaking, the more data they are storing on legacy solutions, the more they are going to require an updated data platform that can handle real-time analytics. Even organisations that have fewer legacy systems are still likely to require solutions that deliver enhanced interoperability to help provide a real-time view across the business and enable them to meet the pressing regulatory requirements they face. Let’s also not lose sight of the fact that moving transactional data to a data warehouse, data lake, or any other silo will never deliver real-time analytics, therefore, businesses making risk decisions based on this and thinking it is real-time is completely inappropriate.

As such, financial services firms require a data platform that can ingest real-time transactional data, as well as from a variety of other sources of historical and reference data, normalise it, and make sense of it. The ability to process transactions at scale in real-time and simultaneously run analytics using transactional real-time data and large sets of non-real-time data, such as reference data, is a crucial capability for various business requirements. For example, powering mission-critical trading platforms that cannot slow down or drop trades, even as volumes spike.

Not only will having access to real-time data enable financial institutions to meet evolving regulatory requirements, but it will also allow them to make faster and more accurate decisions for their organisation andcustomers. With many financial services firms operating on a global basis, this is vital to help them keep up not only with evolving regulations but also changing circumstances in different markets in light of the pandemic. This data can also help them understand how to become more agile, help their employees become productive while working remotely, and how to build up operational resilience. These insights will also be vital as financial institutions need to consider the likelihood of subsequent waves of the virus, allowing them to gain a better understanding of what has and hasn’t worked for their business so far. 


The financial services sector is fast-paced and ever-changing. With the launch of more digital-only banks, traditional institutions need to innovate to avoid being left behind, with COVID-19 only highlighting this further. With more than a third (35%) of customers increasing their use of online banking during this period, it is those banks and financial services firms with a solid online offering that have been best placed to answer this demand. As financial institutions cater to changing customer requirements, both now and in the future, implementing new technology that provides access to data in real-time will help them to uncover the fresh insights needed to develop new and transformative products and services for their customers. In turn, this will enable them to realise new revenue streams and potentially capture a bigger slice of the market. For instance, access to data will help banks better understand the needs of their customers during periods of upheaval, as well as under normal circumstance, which will allow them to target them with the specific services they may need during each of these periods to not only help their customers through difficult times but also to ensure the growth of their business. As financial institutions not only look to keep pace with but also gain an advantage over their competitors, using data to fuel excellent customer experiences will be essential to success.  

With the current economic uncertainty and market volatility, it’s critical that financial services are able to meet the changing requirements coming from all angles. With COVID-19 likely to be the biggest catalyst for financial institutions to digitally transform, they will be better able to cater to rapidly evolving landscapes and prepare for continued periods of remote working. As they look to achieve this, replacing legacy systems with innovative and agile technology solutions will be crucial to ensure they can gain the accurate and complete view of their enterprise data they need to comply with new and changing regulations, and better meet the needs of consumers in an increasingly digital landscape, whether they are located in an office or working remotely.

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