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NAVIGATING UNCERTAINTY WITH ACCURATE MACHINE LEARNING

James Johnston, Regional VP at Cloudera

 

2020 will undoubtedly prove to be an unforgettable year. The pandemic has been unforgiving, plunging the UK into a recession, and many industries have faced closure and untold disruption. In the Financial Services sector in particular, 86% of profit warnings in the first seven months of 2020 cited Covid-19. But Covid-19 is not the only thing on the sector’s mind – another sizable challenge looms large on the horizon: Brexit. Individually both are highly disruptive events, together they create a double shock wave with a long tail of unknowns: how long the COVID-19 pandemic will last? What the fallout from Brexit will be? How resilient is the UK economy in the longer term? A key topic for discussion is therefore, how will we adapt to these seismic events and how can technology help?

 

Predicting the unpredictable

When it comes to planning, Machine Learning (ML) models have become an integral part of how most financial institutions operate, because of its ability to improve the financial performance for both businesses, and their consumers, through data. United Overseas Bank is a key example of a business that has used ML to make it’s customers’ banking experience simpler, safer and more reliable. Through analysing the thousands of files that are uploaded to the platform everyday, the ML models have a more comprehensive view of customer and transaction data to optimize their business processes, design distinctive customer experiences, and to improve detection of financial crimes.

However, in these circumstances of heightened uncertainty, the accuracy of ML models come into question. This is because the majority of ML models that are in use today have been built using large volumes and long histories of extremely granular data. With the world being as unpredictable as it is right now, it will take some time for ML models to catch up and adjust to this year’s events. The most recent example of such complications and abnormalities, at a global scale, was the impact on risk and forecasting models during the 2008 financial crisis. Re-adjusting these models is by no means a simple task and there are a number of questions to be taken into consideration when trying to navigate this uncertainty.

 

Adjusting to the ‘new normal’

The first step is to determine whether the disruption we are facing right now can be defined as a ‘Structural Change’ or a once in a blue moon ‘Tail Risk Event’. A structural change would represent a situation where the COVID-19 pandemic has had a seismic impact on how the world as a whole, and financial institutions in particular, operates. This would result in the world settling into a ‘new normal’, one that is fundamentally different from the pre-COVID-19 world. This shift would require institutions to develop entirely new ML models that rely on sufficient data to capture this new and evolving environment. On the other hand, if the COVID-19 pandemic is perceived to be a one-off ‘tail risk’ event, then as the world recovers and businesses, financial markets and the global economy return to some sort of normality, they should operate in a similar way to the pre-COVID-19 days. The challenge for ML models in this situation is to avoid becoming influenced and biased by a rare, and hopefully, once-in-a-lifetime event.

 

Readjust and reinvest

There’s no one size fits all solution for businesses, however there are some key steps financial institutions can take to them navigate today’s current climate:

  • Modify existing models: This is where all data science teams should start. Modifying models can range from using the latest data elements while creating scenario-based projections adjusted for various levels of model bias. There are a range of alternative ML-based approaches that can be used to revamp existing models.  One of the more innovative approaches to the lack of rich relevant data is a meta-learning approach. From a deep learning perspective, meta-learning is particularly exciting and adoptable for three reasons: the ability to learn from a handful of examples, learning or adapting to novel tasks quickly, and the capability to build more generalizable systems. These are also some of the reasons why meta-learning is successful in applications that require data-efficient approaches; for example, robots are tasked with learning new skills in the real world, and are often faced with new environments.
  • Stress testing: This is a fundamental step as it helps businesses gain a clearer understanding of their vulnerabilities before it’s too late. This isn’t just the job for one team, cross collaboration from finance leaders to Chief Risk Officers is required to set up multiple, dynamic stress testing scenarios. The learnings from these tests should then be implemented and then retested, to ensure businesses are in the best position possible.
  • Industrialisation of ML: If businesses haven’t already done so, now is the perfect time to invest in a platform that supports the entire ML lifecycle, from building and validating processes, to managing and monitoring all of their models across the entire enterprise. Nowadays, enterprises are faced with increasing amounts of data on their customers, entering the organisation from a range of different sources, from the customer service team to social media platforms. For ML models to work at their best, they need to take every stream of data into account, while being able to understand what the different data is saying, and quickly. This can only be achieved with a unified enterprise data cloud platform.
  • Prescriptive Analytics: This approach is complementary to ML and uses simulations for more accurate decision-making for different scenarios, brought on by shocks or market changes. One common approach is Agent-Based Modeling (ABM), a bottom-up simulation for modelling of complex and adaptive systems. ABMs help businesses project thousands of future scenarios without having to depend upon the limitations of historical data.

 

Businesses have had to cope with a lot this year and those that have survived have faced a steep learning curve. When faced with such a crisis, they need to look inwards, towards the technology they have invested in, review whether it’s working in the new circumstances, and whether crucial tools such as ML models are being deployed in the best way possible. Financial institutions shouldn’t look at the issue as a one-off, but instead as a chance to implement longer-term strategies that enable them to prepare and tackle the next crisis head on. Businesses that invest the time now to re-evaluate their ML models are the ones that will set themselves up for success, now and into the future.

Business

BOUNCING BACK IN 2021: DIGITAL TRANSFORMATION IS NO LONGER A CHOICE AS DEPENDENCE ON 5G, IOT AND DATA INCREASES IN SOCIETY AND BUSINESS

Ivan Ericsson, Head of Quality Management, Expleo Group Limited

 

The global pandemic has put enormous strain on businesses and brought into sharp focus the importance of being agile, adaptable and able to increase the pace of innovation and change at short notice – catapulting technology right to the top of the agenda for many organisations.

As the economy works to get back on its feet, technology is only going to play a bigger role in our lives. At Expleo, as experts in digital transformation and the reliable implementation of technological innovations, we’ve outlined the biggest tech-driven trends that we expect to see in 2021 and beyond.

 

1)     “Digital transformation” no longer a choice

If the COVID-19 pandemic has taught businesses anything, it’s that they need to be poised to respond to abrupt market disruption at any moment, making digital transformation mandatory overnight.

With no room for delay, hugely complex corporations – that have historically been slow to adopt technology – have had to accelerate their reliance on technology just to keep afloat in recent months. Digital change, at speed, has become the norm.

Even last year, the idea of an unscheduled video conference call might put people on edge – now most of us wouldn’t think twice about calling a colleague over Teams or Zoom even for a 2-minute conversation. At the same time, social infrastructure has moved with the needs of its users, with telecoms giants strengthening and opening up networks so we can keep communicating despite social distancing.

There are now very few excuses left for operating in a non-digital way. All businesses need to be intelligent businesses that can change direction nimbly, with speed, confidence and composure. As we see more businesses putting this into practice, it’ll likely result in an increased number embracing and normalising some of the behaviours of tech-savvy giants like Apple and Amazon, who have no doubt thrived during this period.

Their success can largely be attributed to normalising an agile approach. By ensuring all applications have testing facilities built in – a “quality shadow” if you will – it allows for continuous improvements, and the ability to change direction quickly and confidently, when needed. This is particularly valuable today as the world becomes more fast-paced and increasingly unpredictable.

 

2)     Big data/AI/predictive analytics 

We’re moving into a space where big data can be extracted from the most seemingly innocuous places. In a hyper-connected world, a move as simple as a dog walk could offer huge swathes of data to the right companies. Many businesses already realise the benefits of capturing and utilising big data, but not all have taken advantage of it. The businesses that move quickest are most likely to reap the rewards in a more impactful way than their ‘data shy’ competitors. Where data used to be a side effect of business operation, it is now the driving force.

As businesses begin to rely more heavily on data to make critical decisions, independent assurance becomes increasingly important to get those decisions right. Forward-thinking, data-driven organisations must therefore assure that the data is correct in the first place, to avoid giving businesses false confidence and risk them moving in the wrong direction – something that is rarely affordable in today’s competitive and fast-paced environment. If businesses are not 100% confident in assuring the quality and accuracy of their own data, they should look to a third party for support.

A key data trend we expect to see moving further into 2021 is the increased use of predictive analytics. At the moment, businesses will often use data analytics to give us insights into our past activities, or to tell us where we are right now. However, the real value lies in knowing where we are going and how we are going to get there. Data analytics will help to identify the optional levels that can be pulled to drive change and realise business benefit.

Secondly, as intuitive technology advances and becomes more accessible, we expect over the next 12 months to see companies of all sizes begin to adopt artificial intelligence (AI) to drive intelligent analytics. In this context, AI refers to various technologies that allow machines to learn, sifting through ‘messy’ big data in order to find and unlock valuable predictive insights into future events. This allows businesses to better adapt their strategy to likely future outcomes and get a head start in the market.

However, with this ever-increasing emphasis on data and data protection, ethical AI will have a more prominent role to play in 2021 and beyond. Protected, usable Data is a by-product of good data security and privacy measures; however, the public remain wary of how their data is being used, particularly after the fallout from Cambridge Analytica’s use of data to influence an election[1]. Businesses, therefore, must give their customers confidence that their data is secure and protected.

 

3)     Moral relevance/corporate altruism

Research shows that young people are increasingly researching and considering the ethics of brands they’re purchasing from. And it won’t be long before this attitude starts seeping into every other aspect of their lives, with more and more people wanting to work for what they consider to be “purpose-driven” businesses.

Talent is the lifeblood of any company, so for big corporations, many of whom were born to create profit, this could put them in a tricky position. They might already be influencing society in a positive way – but this is unlikely to have ever been their main goal.

Moving forward, however, all organisations will have to start thinking about the “Triple Bottom Line”. That means considering the environmental and social impact of your business, alongside your commercial imperative.

We’ll soon see a mindset switch across businesses, from ‘competing’ to ‘advancing’. Instead of wanting to be the “best,” the question will be, how can I better serve the world around me?

In line with this, businesses will have to start thinking more about how to use tech for good, as we’ve seen with the likes of Microsoft Teams connecting tens of millions of people every day, during this very dark time[2].

2021 is likely to bring even more inroads when it comes to using technology to improve society, whether it’s developing bespoke problem-solving technologies or using IT to ‘eco-proof’ existing sectors, the goal for businesses is to rise to this challenge and build a better future for people and the planet through the use of technology. But all organisations will continue to need to be able to justify technology use and prove that they’re using it ethically, and in a secure manner.

 

4)     5G new networks – just about all big trends are driven by/reliant upon faster networks – particularly relevant for a more distributed workforce 

Greater access and utilisation of 5G networks across the country will underpin and accelerate all of the key trends discussed. Everything we do on our smart devices we can expect to do at higher speed, greater capacity and with lower lag times.

As our digital footprints extend beyond simple web browsing and into our daily lives through smart technology, we are creating huge amounts of data every minute. This vast flow of data is increasingly dependent on new high bandwidth networks to facilitate it. Therefore, the merging of technology and engineering will become critical in ensuring big data is carried successfully to drive analytics and drive business.

The fact we have managed to successfully work from home during COVID is a glowing recommendation for the quality of the networks as they exist today, and they will only get better.

The telecoms industry is already working overtime to ensure that people all over the country get reliable access to the internet – and the fact that there is still inequality in this area proves just how challenging this is. But, in line with this trend toward hyper automation, which will make data extraction and analysis a part of everyday life for businesses, the consolidation of tech and engineering will be ever more important.

Forward-thinking companies will look to incorporate 5G networks into their business strategy. This could be from an internal perspective to enhance the abilities of their remote workforce. Alternatively, this could relate to their own products or offerings – developing an internet of things (IoT) strategy, improve user experience, or bring products to market faster by analysing big data and adapting quicker. Either way, with increasingly improved networks, businesses are expected to take advantage of the huge increase in accessible and usable data.

 

Concluding comments:

For businesses to truly reap the benefits of these new technologies, they must be developed and adopted in the right way.

Quality assurance, trust and security are three key requirements that the technology of the future depends on to succeed. Having these requirements at the heart of any digital transformation will ensure that systems perform reliably, having been tested and assured.

By prioritising a seamless customer experience combined with an ability to create, test, and scale digital solutions and operationalise at pace, businesses will be in the best possible position to take advantage of the potential being unlocked by these new technologies.

 

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THE FUTURE OF SAVVY TECH PURCHASES IS KNOWING WHEN TO BUY

There’s no mistaking the impact technology has had in our lives. Once a novelty, technology has now infiltrated every aspect of modern society, and technological advances continue to bring new dimensions to modern living. Yet for all the good that technology brings us – and despite the costs associated with production falling year-on-year – purchasing electronics is often a significant investment.

According to new research by Carphone Warehouse, the average price of electronics in the last year is just shy of £500 at £497.70. Consumers looking to make savvy savings on big tech purchases must both understand what the industry average is for the product category they’re interested in, and learn when prices will be lowest so they can make the purchase at the right time. The electronics retailer outlined the following tips for consumers looking to take stock of technology prices.

 

  1. Know what the average price is, so you can spot the biggest savings

The first step to being able to haggle a better price is knowing what benchmarks to look out for. While you can choose to go under or over the average price – high-end models are often priced significantly higher than average, after all – it can still be useful to know what to compare prices against. The average smartphone currently retails at £527.60, laptops at £680, TVs at £712.31 and digital cameras at £782.60.

As mentioned, there are significant differences between the price of budget and top-end items. For example, while you could get a budget phone such as the Nokia 1.3 for just £65, an Apple iPhone 11 Pro Max could set you back £1,249. Similarly, while a low-end camera like the Nikon Coolpix B500 costs around £205, the camera behemoth that is the Canon EOS 5D Mark IV clocks in at a whopping £1,700.

 

  1. Before you buy, consider any upcoming sales

Once you’ve figured out what you want and the price you’re willing to pay, it can be tempting to make your purchase immediately – for fear of missing out, if nothing else. However, using the sales to your advantage could result in some nifty savings, while keeping abreast of the consumer retail industry could also help you buy at just the right time. Below is a short rundown of some key dates to keep in mind:

January: The start of the year usually sees retailers clearing their Christmas stock, with smartphones, cameras and TVs offered at sale prices. January is also when the Consumer Electronics Show (CES) is held; an event in which new cameras are often announced. If you spot a gem in the CES crowd, wait a couple of months and you could see last-gen models fall in price.

February: As well as Valentine’s Day sales, February sees the Mobile World Congress (MWC) and the announcement of new Sony releases. This month is also a great time to buy the Google Pixel and other smartphones – again due to their release cycle – as well as nab yourself a bargain camera, TV or laptop.

March-April: Look out for a ton of new releases in the spring months, with Huawei, Samsung and Sony releasing their new smartphones, and LG, Samsung, Sony and Panasonic announcing their new TV ranges.

July, August, September: The summer months bring back-to-school sales that typically see student essentials at lower prices. You may be able to enjoy a wide range of deals on TVs, laptops, computers and more during this time. Keep in mind that July is also when Intel and AMD announce their new releases. Responsible for many of the processors that make up the backbone of much of our electronic products, Intel and AMD inspire many brands to lower their prices in anticipation of incorporating their new, advanced processors into their product lines.

November-December: Black Friday and Cyber Monday are undoubtedly the biggest sale events in the consumer electronics space, with deals, discounts and flash sales offered across the entire gamut of the tech world. It’s the optimum time to invest in premium brands, including Apple, Samsung and Google.

 

  1. Embrace the pre-order period for additional bonuses

If you’re an early adopter and only the latest innovations will do, you may not be keen on last-gen products. But keeping up to date with the latest gadgets needn’t mean your bank balance has to take a hefty hit. While costs will naturally be higher for new releases, ordering during the pre-order period could mean you’re able to take advantage of bundle deals or other freebie items provided by the retailer to entice uptake. Sign up to notifications on the products you’re interested in, so you’re forewarned and ready for the pre-order period.

 

  1. Learn some insider tips to beat retailers at the price game

As a final point, when it comes to saving on tech-related purchases, it can be handy to understand retailer behaviour. For example, if you’re looking to upgrade your TV at a cutthroat price, aim for the 55” models. As it’s the most popular size, retailers tend to drop prices on these first in their holiday sales.

If you’re buying tablets, waiting until a new model has been released is usually the best time to get the best prices; last-gen iPads are often discounted a couple of months after a new release. Similarly, if Android tablets are your preference, all you have to do is wait a few months for the newest release to start seeing price drops. And if Kindles are your go-to tablet, it’s probably no surprise that Amazon is the best place for bargains, and particularly so on Amazon Prime Day.

There’s vast potential in technology – and consumer electronics are seeing more and more innovations every year. But, as long as retailer behaviour stays fairly consistent, the information above can help you comfortably get the most for your money.

 

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