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FOUR STEPS FOR BEATING PROCUREMENT FRAUD

By Laurent Colombant, Continuous Controls and Fraud Manager at SAS

Procurement fraud is always a difficult subject. No one wants to think their employees or suppliers are trying to defraud them, but it’s imperative that companies keep on top of anti-fraud efforts. When these activities are discovered too late, it isn’t just the targeted companies who suffer. The financial cost for victims can be severe and the reputational fallout for the business even worse.

If you think procurement fraud isn’t a problem in your organisation, you may be in for a rude awakening. New SAS research reveals that almost a third (31 per cent) of UK businesses have been victims of contract bid rigging between suppliers, and 43 per cent have received fraudulent duplicate invoices. According to PWC, procurement fraud has ranked as the world’s second most common economic crime since 2014.

Procurement fraud is a widespread issue and, in the worst cases, can become systemic in an organisation if left unchecked. However, it isn’t unbeatable. With the right tools and approach it’s possible to stop procurement fraud before it even begins.

To reclaim control over your procurement process, follow these steps:

  1. Recognise the risk

One reason procurement fraud is so prominent but so little talked about is that too many assume its impact is small. Indeed, a third of companies don’t consider it important enough to include it in their auditing processes. Yet, recognising the risk it poses may give a company the impetus it needs to nip procurement fraud in the bud.

It’s important to acknowledge that guarding against procurement fraud isn’t a fire-and-forget exercise. Fraud can occur at any stage of the procurement process or during any point of the supplier relationship. One general audit a year won’t be enough to stay on top of it.

Organisations should follow a regime of continuous end-to-end controls of the entire procure-to-pay chain, with constant detection processes and regular audits. However, delivering on this may well require technology and capabilities currently beyond their reach. We’ll come to these capabilities and how to implement them shortly.

Coming to grasp unknown unknowns is challenging at best. It’s easy to ignore what you neither want to see or address.

2.Consider your culture

Many companies struggle to tackle procurement fraud due to a lack of awareness and communication internally. Departments don’t collaborate, data is siloed and the processes aren’t in place for the easy sharing of data between them. Procurement fraud can arise in any part of an organisation, so it’s crucial for everyone to be involved. 

At the same time, however, there must be a designated fraud leader within the organisation. Even dedicated employees will miss signs and make mistakes, so there must be someone whose sole responsibility is to detect and resolve fraud. In 31 per cent of cases, the CFO or financial controller is handed this responsibility, but they aren’t always the best choice for the job. Ultimately, your fraud leader should have a detailed understanding of internal fraud detection capabilities, daily contact with any anti-fraud personnel, and the expertise to change strategy to meet ongoing and incoming threats.

3. Identify improvements

Another obstacle to beating procurement fraud is a lack of sufficient technology and in-house capabilities. Too many organisations rely on outdated technologies and methods of fraud detection, including rules-based software and manual controls. Once again, where humans are central to the process error is certain and the prospect of tampering can’t be ruled out. These methods can detect procurement fraud, but slowly and only after the fact once the damage has been done.

It’s up to businesses to regularly review and assess current detection mechanisms and allocate budget where improvements can be made. To do this, it’s also crucial to spot problems that could frustrate future tech adoption, whether it’s a lack of skills or disconnected data silos. Once the foundation has been laid, you can then start implementing the best tools for the job; analytics and artificial intelligence (AI).

4. Apply analytics

Continuous data-driven detection affords companies the best chance of holding procurement fraud at bay (and keep it that way). Advanced analytics and AI can process and analyse staggering amounts of data in seconds, picking up on patterns, outliers and anomalies otherwise missed and alerting investigators before the actual fraud takes place. While both take upfront investment to install, the costs are quickly covered and even surpassed in the money saved for the business.

However, it’s crucial that you pick the solution – and deployment – that’s right for your organisation. Solutions developed in-house are rarely the best approach and risk an investment of substantial time and money for capabilities already on the market. A scalable solution can take you up the analytics maturity curve from business rules to anomaly detection, predictive modelling and text analytics. Networks can then be deployed in the cloud, hosted or on-premise.

Companies of different sizes have different needs. Larger companies, for example, tend to have more complex supply chains and procurement processes where wrongdoing is more likely to go unnoticed; smaller ones need more packaged solution requiring less data science knowledge.  Best-of-breed solutions that identify the most fraud with the least false positives while preventing future cases are a must for all.

For more best practice on procurement integrity and how to tackle procurement fraud, read our report on “Unmasking the enemy within: how smart analytics can stop procurement fraud”.

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Business

CREATING A PEOPLE-CENTRIC WORKPLACE CENTERED ON FLEXIBILITY, EXPERIENCE AND WELLBEING

By Anne Marie Ginn, Head of Video Collaboration, Logitech EMEA

 

The light is appearing at the end of the long, dark tunnel that has been 2020. With vaccination schemes now underway, we can (albeit cautiously) dare to dream of a general return to relative normality. Yet in the wake of the pandemic, neither our personal lives nor our work lives will ever be quite the same.

A wholesale change to working practices, and the nature of how and where we work, is set to be one of the big lasting legacies of 2020. Cal Henderson, co-founder of Slack, recently came forward to say he thinks that the age of the office is coming to an end. In a less extreme view, AWS’ CEO Andy Jassy predicts we’ll see the rise of ‘hot offices’, where employees will mostly work remotely, only coming into the office when they need to work on specific projects. And Microsoft founder Bill Gates predicts the age of business travel is over, with only 50% of business trips set to resume.

As the office evolves it’s clear employers will have to adapt their spaces in line with new, post-pandemic wellbeing and workplace trends, and create an office centred around “super experiences” that makes it a destination in itself.

So, in what ways will working practices change, and how do we see the physical workspace evolving?

 

Re-focussing on the employee

Ultimately, the pandemic has re-focussed the discussion on how employees can best work, and how teams are spending their time. It has also given employers the opportunity to ensure they’re in a better position to help people find a good work life balance.

Yet even after Coronavirus, it’s clear we won’t be working from home forever. The UK government says work from home orders may stay in place until April 2021 and with this in mind a flexible, and hybrid, way of working is set to stay. Employees feel that way too – a recent Simply Communicate survey found only 2% want to go back to the full week in the office.

With the digital tools available and the experience gained over the past 10 months, the idea of everyone being in the office everyday seems old fashioned and unnecessary. People don’t want to travel into an office to then just be sat at their desk for eight hours. What they want is to connect with colleagues, to learn, to be inspired and to share with others.

Whilst getting your head down to work is important, social time and collaboration is equally valued, and central to general wellbeing. For many employees, their work is central to their sense of self, their meaning and purpose, and after a long period of being at home alone, they’ll be yearning for those in-person, face-to-face experiences. This should be placed at the forefront of modern office culture and design.

 

An office designed for the people working in it

Offices will become destinations unto themselves – for collaboration, innovation and strengthening team relationships – and less about desk-based or task-based work. The space should also be vibrant and different.

These offices should offer a mixture of meeting rooms and open operational space, which will promote gathering for teamwork, collaboration and companywide networking events. At the same time, smaller collaborative working areas, enabled by video, will facilitate break away group work for those both physically present and working remotely. Banks of individual cubicles will disappear, and instead we’ll see occasional, dedicated concentration pods for when employees need to get their heads down between meetings. And how about relaxation pods should employees want a quick break and recharge?

Beyond work, offices also need to become social destinations in themselves. A recent JLL study found that nearly half of employees hope their office will prioritise social spaces, such as coffee areas, lounges or outdoor terraces and gardens. Common areas play a central role in nurturing informal work relationships, which improve development opportunities and help career outlook – especially crucial for people early in their work life. These spaces allow employees to maintain the inspiration, energy and social connection that comes with belonging to a physical team and environment – something which many found a real challenge to maintain virtually during the pandemic.

Flexible schedules and shared spaces will also lead to a “rightsizing” of office space, where organisations will rethink their real estate, in what will undoubtedly save costs. Some are even predicting that we’ll see the creation of an office ‘ecosystem’, which will comprise of employees working from offices, houses, and third places such as cafes, coworking spaces, and libraries.

 

Tech and video as the glue for hybrid working

While all of the above will support flexibility, functionality and employee wellbeing, for it to all work it needs high-end peripherals, such as Logitech’s MX Series of high-performance mice and keyboards, and collaboration software to pull it together. This tech needs to help us and not take us away from people, helping our collective mental health in environments that could be potentially isolating.

This human centred approach to work collaboration requires non-intrusive, seamless video conferencing and productivity tools. Through each space in the office, from large town hall style areas, through to smaller huddle rooms, personal workspaces and even satellite offices in the suburbs, these video solutions and smart productivity technologies can help to bring together a team as one.

Fortunately, there are a wide variety of high-quality video tools available that can fit the needs of the modern worker within each individual environment. From large 4K cameras with the ability to pan, tilt and zoom to focus on an individual speaking within a large room, to wide angled huddle room cameras for smaller groups, and webcams with integrated high-quality microphones and optics to make sure remote workers are seen and heard just as clearly as if they were physically in the office.

 

The hybrid opportunity

The hybrid office presents itself with an opportunity to make work better for employees, while creating a more committed and motivated workforce. There’s also potential to save money through reduced office related overheads.

Tied together by smart technologies such as video, this hybrid office has the potential to make employees happier, more motivated and equipped to do their best work. Video will pivot from being the technology we used to survive during the pandemic to the one we use to thrive.

 

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Business

NAVIGATING UNCERTAINTY WITH ACCURATE MACHINE LEARNING

Richard Harmon, Managing Director, Financial Services 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.

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