Jason Robson is Head of Software Development at Equiniti Riskfactor
In essence, Artificial Intelligence attempts to mimic human intelligence or behaviours. Machine Learning attempts to analyse and associate patterns of behaviour in diverse data sets to support data-driven decision making based on new knowledge and understanding.
Traditional risk models have used statistical or expert-driven heuristics, but now the next generation of risk analytics is taking advantage of the work being done in this growing field of Data Science.
As fraud is thankfully a relatively a rare occurrence within an organisation, developing simulation tools is key to understanding the lifecycle of a fraud. Using real world examples, we are now able to model the patterns of behaviour surrounding a fraud in order to reproduce the event with diverse sets of changing dynamics. This allows us to represent and understand the fraud over a range of time periods and with utilising differing levels of funding.
Most of the work of a Data Scientist is at this (slightly unglamorous) end of the workflow – essentially the acquisition of test data and its transformation into more suitable forms for use in data analytics.
‘Data Munging’ is the delightful phrase that has been given to this activity.
Aside from a background in probability and statistics, the Data Scientist’s toolbox consists of technologies such as the programming languages Python and R, which can be tailored to accommodate statistical computing and graphics.
Cloud computing providers such as Microsoft Azure and Amazon also have services dedicated to Machine Learning problem domains.
Machine Learning algorithms allow the matching of patterns and connections that can’t be expressed easily, or even at all, by people. Imagine the field of speech recognition, where devices from Google, Amazon and Apple can not only identify what is being said, but which person in a household is saying it.
The unique patterns of speech can be recognised even though the reasons why could never be easily conveyed to its owner in words. Now swap the rises and falls in pitch and amplitude with time series metrics derived from a commercial finance facility, and you will immediately see the future possibilities we are exploring.
The abundance of data that surrounds us covers not only our work lives and business connections, but also information about our social interests and friends. This rich picture will play a hugely important role in fully understanding the events we wish to model.
The wealth of data in the world we inhabit today is moving the bar above mere fraud detection,towards future fraud prediction. And yes, if you are thinking ‘Minority Report’, Hollywood does seem to have got there first).
Sustainable technology must be prioritised over enhancement: Re-focusing a wasteful tech culture
By Jo Barnard, Founder of Morrama
The UN recently reported that as a global population we are throwing away £50bn worth of electrical waste every year, and with no sign of slowing up anytime soon. Alongside our collective environmental concerns of climate change, pollution, deforestation and biodiversity, our waste of electrical goods – or e-waste as it is becoming better known – is right up there as a real cause for concern.
As a society, we have found ourselves locked in a repetitive cycle of technology and device ownership. Televisions, tablets, and mobile phones – arguably the largest cause of technological waste – are consistently replaced and removed from our lives with little thought for where the remains of products go. Mobile devices specifically are a guilty party here. Round and round we go, disposing of our phones and replacing them with a new and equally expensive model – with the fate of our old devices often unknown. Until now it has been hard to imagine a world where we know any different. However, there are ways in which we can impact positive change onto our technology habits and move away from this wasteful tech culture. Is the latest iPhone really more important than the future of the planet?
One way to do this is by designing wasteful resources and materials out of the products themselves from the very first design stage. Waste by-products and pollution are a big issue for the technology industry and one of the key approaches for a more sustainable future is how to resolve and overcome this. The problem, though, is that the materials typically used in devices are currently high complexity and with few alternative options for such semi-precious materials.
How could we, for example, transform our traditional consumer technology products and business models into something that complements a sustainable vision for the future? We should consider how mobile phones could be made upgradeable rather than replaced using innovative design solutions. By creating a three-part design model for a phone, consisting of: A back; internal systems; and the screen; we could create a system where each element of the phone becomes individually upgradable and isn’t reliant on the device as a whole. You could seek to upgrade your operating system when there is a new update available, while improved cameras could be swapped in with a different case colour or design, and screen replacements available with ease. Combining this with entirely sustainable and eco-friendly materials could provide a new outlook for the modern consumer and mobile network – one in which we do not find ourselves stuck in a repetitive and often overly costly contractual agreement.
And what about Water? Approximately 2,000 gallons of ultrapure water is currently being used per 100 chips or semiconductors. This means we are wasting freshwater, which obviously could be put to much better use across the struggling societies of our world, and so instead we should look at reducing this amount and even recycling the water to avoid such unnecessary wastage of pure resources. Intel, which had previously reported it was using almost three billion gallons of water per year to create its products, are one company to have spearheaded such a necessary adjustment. Its $237million investment into water conservation projects since 1998 have moved the company back into a more positive light. They aim to return 100 percent of its water to communities for local use by 2025 – so it’s not beyond the realms of possibility to make this a standard across all technology sectors. It is broader business initiatives such as these that also work to improve brand reputation and recognition among customers, promoting these businesses as genuine leaders within sustainable innovation.
One, very avoidable, area where we could easily cut back on waste lies in the launch phase of a product. So often we hear of faulty devices being recalled by companies due to flaws in their system design – the mass recalling of the Samsung Note 10 comes to mind – which ultimately leads to a colossal waste of resources as a result of the devices being returned. Also, there is the unnecessary pollution caused by transporting useless products back to suppliers. More attention therefore needs to be paid to carrying out proper testing before products are – often carelessly – sent out into the world only for 100,000s of these devices to be rendered totally useless and obsolete.
It is important, however, to consider more than just the contribution of modern, handheld and shorter lifespan devices to e-waste. Smaller technology such as phones and tablets actually only account for 9% of the world’s total e-waste, with home technology such as irons, kettles and toasters (37%); fridges and freezers (22%); and televisions (14%) making up the larger portion of electrical waste. Their longer lifespan naturally makes them less harmful and damaging to the environment proportionally to the likes of computerised technology, but there is still a case to be made for how design can provide solutions to make our existing utilities upgradable rather than replaceable, similarly to smaller devices.
That being said, it is important to consider the contributions technological waste makes towards damaging the planet as a collective, not just focus on the smaller scale. It is an issue not previously held as high in importance as the more common issues for our environment, but is one that should not be underestimated. Promoting sustainable design, production and removal of our devices and large-scale technology and appliances is something that should be prioritised ahead of any new technological enhancement. Controlling our wasteful tech consumption must come first.
AI reduces procurement fraud, error and abuse
By Hans Bonde, Senior Industry Consultant, SAS
In recent years, there has been an increasing focus on financial crime in both public and private entities. The press coverage of money laundering, tax fraud and employee embezzlement of public funds is greater than it has been in the past several years. This increased awareness leads to an increasing expectation that we must detect and punish financial crime.
It is a wonder why big data and analytics are not mentioned more often in the debate. Experts recognise analytics as one of the most important tools in the fight to identify and resolve financial crime.
How big is the problem?
An estimate from PwC indicates that procurement is one of the areas most prone to fraud. International organisations, including the Association of Certified Fraud Examiners and the English CROWE, indicate that the number of errors, fraud and abuse is on average 5% of public/private entities’ procurement. In other words, if your organisation purchases £100 million, there will most likely be a high risk of £5 million in fraud. In addition, international institutions point out that:
- The process of identifying fraud is complicated. Typically 18-24 months pass before an organisation identifies and acknowledges that it has been subjected to fraud.
- Typical perpetrators have been in the organisation for a long time (over 10 years), and only 4% have been previously convicted of fraud.
- Fraud is committed at all levels of an organisation. And people at the highest management level commit almost 20% of it.
Fraud does not start out as fraud
One of the reasons why fraud is both widespread and difficult to uncover is that there are many different types of fraud. And most often it occurs as errors, which if not detected, will be tried again and again, becoming more systematic.
This means that fraud may not start with the perpetrator intending to profit from it, but that those involved gradually read the possibilities, and then develop and refine the fraud, possibly other people, internally or externally. This makes it even more complicated to uncover what is happening.
Some of the typical types of fraud are double invoicing, splitting orders/invoices in order to keep the value below a limit for extra processing, agreed action between two or more individuals (can be both internal and external) and payment for items that are never delivered. Furthermore, employees often have financial interests in companies that serve as suppliers.
Generally, all types of fraud bypass internal processes. Organisations design their procurement processes to balance rigidity and flexibility. The process must be rigid enough to be difficult to circumvent, but flexible enough that procurement can be carried out efficiently, without undue costs in the form of checks or disruptions to internal operations.
Problems with manual monitoring
This means that it is not enough for a company that wants to systematically fight fraud and ensure good control to tighten up procurement and payment processes by adding extra checks of the individual processes. For example, there may be an extra check of payments over £1000. If you want to safeguard a company effectively against fraud, there must be a more systematic follow-up and monitoring of process compliance.
An internal or external auditing function typically performs such monitoring, whose task is to dive into the volume of data on procurement and payments in order to identify and verify fraud. This is often a time-consuming, manual – thus costly – process that often uses random samples or checks on leads from whistleblowers. In some cases, auditors find collusion between the perpetrator of the fraud and the person who should monitor for it.
Moreover, it takes a lot of experience, and possibly great ingenuity, to manually identify new patterns in the enormous amount of procurement-related data.
The solution is to use analytical tools to constantly monitor compliance with processes and rules. This is called continuous monitoring.
Continuous monitoring automates a number of the functions that are currently manual. First, the solution extracts millions of data records from the relevant systems, including purchase orders, invoices, payments, HR data, etc. Afterwards, the data is cleaned and prepared for the actual analyses of anomalies, patterns and events that can identify possible fraud, error and misuse.
Continuous monitoring with the support of analytical tools is far more effective than the more traditional audit. The main differences are:
- Analysis of ALL transactions. Full analysis not only proportionally increases the chance of finding fraud, but is crucial for effective identification of anomalies and patterns.
- Automated data integration, cleaning and preparation. A good data basis is essential for finding fraud. Experience shows that 80% of the time spent on auditing goes to locating, compiling and cleaning data. Automation of these processes will free up time for the more important task of monitoring for fraud.
- Using artificial intelligence. You can look at AI as the automation of cognitive processes. This means that the problems that traditionally require human logic to solve can be handled efficiently and often more precisely by computer. Today, AI can improve all stages of the process, from data cleaning to reporting.
- Known vs. unknown scenarios. In our experience, companies will be able to automate simple rule-based controls that capture known scenarios of error, fraud or misuse. In the worst-case scenario, such controls increase the possibility of fraud, as new scenarios go under the radar.
The value of AI for detecting errors, fraud and abuse
Artificial intelligence is an essential element in building an effective procurement monitoring process. The Association of Certified Fraud Examiners points out that the use of data and analysis is among the most effective elements in reducing loss and resolution time. Using AI to complement more traditional analytics only reinforces this trend. You can apply artificial intelligence to many of the key tasks of the overall analytical process:
- Cleaning and compiling data.
- Preparing data.
- Generating and analysing networks.
- Identifying anomalies.
- Scoring the potential for fraud, error and abuse.
Artificial intelligence, therefore, provides fraud investigators with the best opportunities for efficiently identifying possible fraud, prioritising scenarios and documenting their investigation. Thus, in addition to reducing the organisation’s financial losses, this strong management tool also reduces the risk of bad publicity. And it conveys that you are working purposefully to avoid fraud because it is unacceptable.
81% of Business Managers in the Manufacturing Industry Agree that a Modern IT infrastructure Accelerates Innovation, Creativity, and Productivity
- 83% of business decision makers are convinced that slow running networks and applications are inhibiting these three success factors
78% of IT decision makers believe that innovation, creativity, and productivity of the employees is being limited by the technology in their companies, and 94% within this say it is costing their company money
86% of business decision makers in the manufacturing industry believe that digital performance is critical for business growth
LONDON, 22nd September, 2020 – Riverbed® today launched its expanded ‘Rethink Possible: Visibility and Network Performance – The Pillars of Business Success’ Study, focused on the manufacturing industry, a critical sector for Germany. The study revealed that 81% of business decision makers in manufacturing companies are convinced that IT infrastructure plays a crucial role in enabling their organisations to be innovative, creative, and productive. And, when limited, IT infrastructure inhibits these three success factors enormously (83%).
The Study – which lays out the indisputable link that business and IT decision makers see between strong IT infrastructure and the manufacturing industry – further revealed that 40% of business decision makers in the manufacturing sector consider IT investment to be the most important business objective at present. And that a further 35% of business decision makers in the manufacturing industry prioritise digital transformation. This means that IT expansion in manufacturing is currently more important than traditional corporate operations such as financial rationalisation. Almost two-fifths of the business decision makers (39%) stated that they had pushed digitalisation in their companies to the greatest possible extent, whilst 52% are still in the process of implementing it.
The vast majority IT decision makers in the manufacturing industry (93%) say that a well-functioning infrastructure plays an even greater role in creativity, innovation and productivity than it does for business decision makers. At the same time, 94% of IT managers say that limitations of these three factors costs organisations a lot of money.
Other key findings from the Rethink Possible: Visibility and Network Performance Study include:
IT and business leaders agree that digital performance is crucial for business growth (86%) and staff retention (80%)
88% of IT decision makers reveal that employee satisfaction falls considerably when systems are slow
More than two-thirds of IT decision makers (68%) consider network transparency in their companies to be sufficient
The majority of IT managers (82%) would, however, like to see more investment in network transparency
And finally, at least six in every ten IT decision makers (63%) acknowledge that there’s a lot of catching up to do in preparing management for the challenges of digitisation.
“The Study shows that the majority of the manufacturing industry recognises the importance of having an efficient IT infrastructure. In the coming months and years, as manufacturing returns to a ‘new normal’ way of working, companies will have to invest even more in technology to fully implement digital transformation,” comments Colette Kitterhing, Senior Director UK&I at Riverbed Technology. With remote working expected to continue and an overall shift towards a more distributed workforce as a result of the pandemic, the performance of corporate networks is also becoming increasingly important for those sectors where the focus has so far been on the automation of equipment rather than their IT infrastructure. However, the convergence of manufacturing plants and IT is creating new challenges, and the opportunities offered by digitalisation are being fully exploited. To ensure that networks and applications deliver the necessary performance and work efficiently, the IT team needs complete transparency. This is the only way for employees to be truly innovative, creative and productive in times of digitalisation.”
Rethink Possible: Evolving the Digital Experience
With 86% of IT and business decision makers in the manufacturing industry believe that digital performance is critical for business growth, technology is the enabler in this process. Riverbed’s portfolio of next-generation solutions is giving customers across the globe the visibility, acceleration, optimization and connectivity that maximizes performance and visibility for networks and applications.
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