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NAVIGATING UNCERTAINTY WITH ACCURATE MACHINE LEARNING

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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

THE EVOLVING TECHNOLOGY NEEDS OF THE FINANCE DEPARTMENT

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Jennifer Sims, Senior Consultant at Xledger

 

The world of finance software is evolving quickly, but with many new software contenders entering the market it can be a mindfield for organisations. Many finance teams are already using multiple accounting apps and software packages for bookkeeping, payroll and invoicing to service individual needs. Whilst it may work fine for now, this segregated approach isn’t sustainable for long-term growth. The world is swiftly moving to agile, automated ways of working. As a result, there is a growing need to choose suppliers that can fulfil multiple functionalities within the one platform.

Financial software is evolving at such a pace that it can be difficult to keep up. Changing up a finance solution is a big step and ease of migration can be a substantial factor in determining which solution provider to go with. But how do you choose a solution that will grow with your business and still offer something innovative in five or ten years down the line? The fear is always that non-techie organisations will end up falling behind, but in such a highly concentrated industry, how do you decide which solution would work best for you?

 

Cloud-first: the term that makes all the difference 

You could find a ‘cloud-based’ service with an application that comes with automated audit trails to make it easier to meet compliance and record-keeping obligations, for example. But for a solution to offer all of the many future benefits promised by the cloud, it needs to have been built specifically for a cloud environemt from the outset – ie. not an on-premise built system that has been later adapted. Cloud-first services (true cloud) were always intended to leverage economies of scale, cope with live updates, be accessible from anywhere with an internet connection, and to scale rapidly, to name just a few of the many benefits.

When we talk about innovation in financial technology, we’re not just talking about software that makes it easier for the financial controller to create reports. If eliminating reliance on Excel spreadsheets is the only tangible benefit you have to really shout about, you are missing out on the real deal. With ‘true’ cloud finance software the sky is the limit.

Finance and accounting technology needs to directly meet the needs of the finance function and support the wider business needs.  When looking at accounting software platforms you’d be hard pressed to find one that doesn’t now promise ‘cloud-based’ enterprise resource planning (ERP) capabilities. The cloud is nothing new, but it’s the way that a solution harnesses this environment that makes a real difference. And here is where there is a need to read between the lines.

 

Automate more with true cloud 

Historically, repetitive and manual tasks are typical of the finance role – from invoice postings to expense claims handling – these can overwhelm the finance team. Research by Xledger[1] has found that an enormous 91% of CFOs and finance decision makers are carrying out at least one of these repetitive tasks as part of their job. What’s more, senior finance leads are averaging a whopping 25 hours per week carrying out repetitive and manual tasks, compared with 15 hours for other finance decision makers.

A modern, true cloud finance system can enable your business to automate repetitive tasks and provide one source of truth so that teams can make informed business decisions that will help to scale a business. Bank reconciliation, dashboard creation and reporting are just some of the tasks that can be handled automatically.These capabilities are aiding overtasked finance teams and saving hundreds or thousands of hours a year.

Whilst different companies are at different stages in their digital transformation what is clear is keeping up with the latest technology is fundamental to the future success of an organisation.

Xledger is a true cloud finance solution. The basics include invoicing, robust general ledger accounting, detailed slice and dice reporting, purchase orders, billing, VAT reporting, and cash and bank payments. It also adds process and structure to the enterprise with procurement and inventory, budgeting and forecasting, and project accounting. Users are always on the latest version of the software and with regulation more stringent than ever today, Xledger is ISO 27001 accredited.

Choosing the right provider for your financial ERP solution comes down to whether it has the fundamentals right. When hosting all of your vital data in the providers’ own servers, it should evidence a highly tested security process that comes with backup services as standard.

As our demand for technology capabilities grows and as ERP models progress, innovation will become the structure for growth – and there is no end to the possibilities.

 

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HOW RETURNS ABUSE AFFECTS RETAILERS

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By Aaron Begner, EMEA GM at Forter

 

Accompanying the significant growth in ecommerce over the past 12 months, is the need for retailers to manage the impact of a growing array of fraud and abuse challenges. One type of fraud that can easily fly under the radar is the abuse of a merchant’s returns policies.

Returns abuse can be difficult to detect and prevent for retailers, as often it is a challenge to identify fraudulent behaviour vs. a ‘usually-good’ consumer trying to bend – but not break – return policies. Therefore, it’s often a challenge to identify how returns abuse actually affects retailers. Here are three of the biggest ways that returns abuse negatively impacts business.

 

Lost Revenue

The most obvious effect that returns abuse has on a business is lost revenue, which can be significant. Research indicates that returns abuse may be costing retailers up to $15 billion per year. When fraudsters purchase items with the intent of abusing returns policies, the retailer makes no profit. Furthermore, it stops legitimate customers from purchasing the items they want, as fraudsters who don’t want the items are moving them around.

Various types of returns abuse can profoundly damage retailers’ bottom lines. Some tactics, such as shoplisting, where fraudsters try to obtain a refund for a list of products listed on a perfectly valid receipt, yet that they never purchased to begin with, can significantly impact retailers’ bottom line.

 

Increased Operational Costs

Returns abuse doesn’t only affect revenue pertaining to the products themselves. There are also operational costs to consider. An increase in returns abuse will often lead to more consideration being put into checking every return, for signs of abuse taking place. This can range from missing tags to damage or wear on the product. This process can be time-consuming, meaning more resources might be necessary to continue operating in an efficient manner. Handling and warehousing costs can also begin to increase, with returned items becoming significantly less valuable.

 

A Poor Customer Experience

As returns abuse continues to increase, many retailers will feel pressure to tighten their return policies. This could range from reducing the allotted time for eligible returns, to only issuing store credit instead of cashback. In some cases, more extreme measures such as requiring a restocking fee for more expensive merchandise will be taken.

While these are all effective ways to help diminish the effect of returns abuse on retailers, they can also have an adverse effect on a retailer’s customer experience. If loyal customers have become accustomed to a more flexible and forgiving return policy, they could be taken by surprise when it’s more difficult for them to return their items.

Ultimately, it can be tricky to balance the two. Returns abuse negatively affects retailer revenue and the overall business, but so does a poor customer experience.

 

The Negative Impact of Returns Abuse Cannot Be Understated

Returns abuse is often overlooked. It can be difficult to detect, but significantly impacts revenue and operations. Because stricter return policies may restrict loyal customers, the reputation of a retailer’s business can be affected. Poor customer experiences can lead to bad reviews and a loss of current and potential customers. Because of this, returns abuse prevention should be a top priority for all retailers.

With this information in hand, retailers can get a better understanding of how returns abuse affects their business and why they need to put a prevention plan in place, as soon as possible.

 

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