By Anna Lykourina, EMEA Fraud Analytics Expert at SAS
In the past, the fight against fraud has been a bit hit-and-miss. It has relied on auditors to identify patterns of behaviour that just didn’t quite fit. They often only detected problems months after the event. And then organisations had to claw back stolen funds through legal processes.
In a world where transactions happen in under a second, however, this is no longer acceptable. We need to be able to detect fraud immediately, if not before it happens. Customers want safe and protected data that is not vulnerable to identity theft through company systems. But they still want to be able to pay online and in seconds. The stakes are high, but fortunately new tools and techniques in fraud analytics are enabling companies to stay ahead of fraud.
Trusting machines to do the work
Machines are much better than humans at processing large data sets. They are able to examine large numbers of transactions and recognise thousands of fraud patterns instead of the few captured by creating rules. On the other hand, fraudsters have become adept at finding loopholes. Whatever rules you set, it is likely that they will be able to get ahead of them. But what if your system was able to think for itself, at least to a certain extent?
New approaches to fraud prevention combine rules-based systems with machine learning and artificial intelligence-based fraud detection systems. These hybrid systems are able to detect and recognise thousands of fraud patterns and learn from the data. Automated analytical-based fraud detection systems can reveal novel fraud patterns and identify organised crime more consistently, efficiently and quickly. This makes them a good investment for businesses across a wide range of sectors, including public sector, insurance, banking, and even healthcare or telecommunications.
How, though, can you harness analytics as a tool in your fight against fraud?
Identifying needs and solutions
The first step is to identify which options you need. Probably the best way to do this is through a series of company-wide workshops with the fraud analytics experts to determine what analytics you need, which data to include and techniques to use, and what results to report. They can also identify the ideal combination of rules-based and AI/ML approaches to detect fraud as early as possible.
Companies looking towards advanced analytics for fraud detection will need to make a number of decisions. They will need to optimise existing scenario threshold tuning, explore big data, develop and interpret machine learning models for fraud, discover relevant information in text data, and prioritise and auto-route alerts. There may be industry-specific decisions to make, too, such as automating damage analysis through image recognition in the insurance sector. By automating these areas, companies can both significantly reduce human effort – reducing costs – and improve their fraud detection and prevention.
Benefits of an analytical approach to fraud detection and prevention
Companies that are already using an analytical approach for fraud prevention have reported several important benefits. First, the quality of referrals for further investigation is better. Investigators also have a much clearer idea of why the referral has been made, which improves the efficiency of investigation. Analytics also improves investigation efficiency by reducing the number of both false positives (that is, alerts that turn out not to be fraud) and false negatives (failure to spot actual frauds). This improves customer experience and reduces risk to the company.
Analytics makes it possible to uncover complex or organised fraud that rules-based systems would miss. Companies can group together customers and accounts with similar behaviors, and then set risk-based thresholds appropriate for each scenario.
There are several sector-specific benefits too. For example, insurance firms can identify fraudulent claims faster to prevent improper payments from going out. Claims investigation is likely to be more consistent because claims are scored through technology, algorithms and analytics, rather than by people. Finally, it becomes possible to shorten the claims process through automated damage analysis. It is no wonder that organizations across a wide range of sectors are placing analytics at the heart of their anti-fraud strategy.
TAPPING INTO THE RIGHT MINDS
David Holden-White, co-founder and managing director, techspert.io
The world is awash with information. Analyst house IDC estimated that more than 59 zettabytes of data would be created, captured, copied and consumed in 2020, and that the amount of data created over the next three years will be more than what was created in the past 30. The boom in consumer technology and the rapid improvement in mobile connectivity has meant that the 48% of the globe that owns a smartphone has near instant access to all the digitised, publicly available information in the world in their pocket.
A world overloaded by information
It’s no surprise that people talk of information overload, or how much it impacts productivity. It’s not new either. A 2012 study from McKinsey & Co highlighted that nearly a fifth of professionals’ time was spent searching for and gathering information, half of the time they spent undertaking role-specific tasks. This is only likely to have increased as we’ve become more dependent on digital tools and services.
On top of that is the realisation that, thanks to social media, we’re living in a time when anyone can be an influencer or thought leader if they shout loud enough. It doesn’t matter whether you’re pushing trainers or cloud computing, whether your audience is a broad spectrum of consumers or a niche group of B2B buyers; the tools and resources are pretty much freely available to build a profile and push your message out there.
The result is that it’s becoming increasingly hard to find the value amongst vast and accelerating volumes of online data and noise, and to use that data to make accurate, effective decisions.
This is something we need to be able to do. We’re all expected to work faster, to make better decisions more quickly. The pandemic showed that certain changes don’t need five committees, two working groups and a proof of concept to take place before decisions can be rubber stamped. At the same time, no matter what industry you work in, there will be competitors who are more agile, more flexible, and seem to be much better at making decisions and capitalising on opportunities.
Yet those decisions still need to be backed by evidence, by irrefutable knowledge. What’s more, there’s only so much data can give us. We need the insights stored in the minds of true experts, with lived experiences of the particular problems, markets and technologies in question. In accessing this, we can develop a decision-making edge in businesses that competitors don’t have, that can be used to drive entrance into new markets, or for winning investment decisions.
Limiting risk in investment decisions
As we all know, investments are inherently risk-related, so, anyone making such a decision will do all they can to minimise their risk exposure, especially in volatile post-covid markets.
To do that requires being able to identify, consume and process information quickly. Investment opportunities, particularly in industries with significant growth capacity, come around quickly and get snapped up fast.
Those decisions will incorporate analysing and drawing insights from raw data, using publicly available and analyst-produced information. But there is also an opportunity to draw on human insights, from leading experts in relevant fields, to get a sense of the story that 0s and 1s can’t properly tell yet. Tapping into the right minds is essential to informing investment decision-making in 2021.
In an ever-growing haystack of information, the challenge is finding them quickly. Plus, once they are found, there’s a tendency to keep using them, or to use them as a gateway to others in their network. While there’s nothing inherently wrong with this approach, it leaves investors exposed to a lack of diversity in thought that makes getting to an unbiased view of the world impossible. At the same time, casting their net wide and finding lots of experts is resource and time-intensive, at a point when time is one commodity in short supply.
So, what’s the solution? Ironically, given that the challenge is bringing the right human insight into the process, the answer could lie in technology, specifically artificial intelligence (AI). AI-powered platforms can take a request for expertise and run searches through all available published and credible material to recommend the most appropriate experts for the project in question.
It’s true that there are already services that recommend experts, but they are heavily manual and therefore slow and imprecise. It’s also true, there are also both negative and positive connotations being attached to AI. No technology is without its flaws, and if investors were relying on the AI platform itself to provide expertise then there would be cause for concern. Services that provide access to the experts themselves, however, are providing a fast way through the noise and data – it’s a car to the destination, not the destination itself. Once investors and experts are connected, the former has access to the relevant insight the latter holds in their heads. What AI has done is rapidly scan through millions of people of talent to highlight the relevant knowledge holders with pin-point accuracy.
Using technology to highlight the best human knowledge
Using an AI technology platform to find the most relevant human is a way of taking a resource-consuming process and finding what’s needed in a thousandth of the time. In that way, investors can get fast access to the human insight they need to make the best decisions, allowing them to capitalise on opportunities and not miss the next big growth opportunity.
A NEW VISION FOR GRANT MANAGEMENT REQUIRES FAMILIAR IT
Jack Perschke, Partner at Netcompany
At its very heart, the business of government is mostly about either taking in money or giving it away. Of course, that’s a simplified view but grant management is a large, important and complex area of government. Its raison d’être is to support policy objectives across government from education, health, rural affairs, innovation and research as well as abroad through international aid for public good.
It requires both process flexibility and mandatory security and monitoring to ensure safe case management and the payment of grants. And while the funding mechanisms, rules and scopes may differ, grant management is something nearly every department of every government does.
The challenges are familiar across governments too – with the need to improve the efficiency of grants administration, the effectiveness of the grant funding and reduction in losses from fraud driving innovation.
Grant management following exit from the EU
While the Grants Management Function in the Cabinet Office is continuing with its ambitions to make grant management more effective efficient and safe, the UK’s exit from the EU is ensuring that grant management is rising up the agenda across many other departments as they re-think how grant funding can better support policy.
For farming in particular, the end of EU farm subsidies represents one of the biggest changes to farming policy in half a century. With the end of the Common Agricultural Policy (CAP), Defra will assume responsibility for designing, implementing and managing its own domestic agricultural policies and schemes. And is now starting its 7-year transition towards a system that pays farmers to improve the environment, improve animal health and welfare, and reduce carbon emissions.
Essentially, they are moving away from decades-old practices of funding based on land size, to instead reward farmers for work that only they can do – whether that’s ensuring the survival of threatened species or locking up carbon on their land. Work that benefits everyone in society.
This move will have to be both driven by long-term policy, and reactionary to need. Yet, the UK grant management function is underpinned by platforms and processes formed in the 1950s. Platforms that are clunky and rigid, and not realistically up to the job of delivering what government and society wants.
Innovate around proven solutions
If government needs to underpin a reimagined grants management function with a new platform to ensure it meets its ambitions, how should it do that? It could, in the established way, start from scratch and build something that might work. Yet we know projects often fail to deliver on time, exceed the budget, and do not provide the value promised. The alternative is to look at what others are doing. As we said before, the grant management function remains relatively the same from government to government, as do the challenges and aspirations to streamline processes and improve transparency.
With this in mind, wouldn’t it be better to adopt proven solutions that create agile, future-proof systems, based on open components that ensures full flexibility and the opportunity for ongoing innovation? Take this approach and government can spend 20 per cent of the effort getting 80 per cent of the way to the digital national scale grants solution they need. Why should Defra and the like build their own when suppliers have already delivered these proven solutions to other governments?
With little effort government can create a cutting-edge grant management system, own it and be responsible for it, leaving more space to innovate around the edges creating the impetus, through data-led funding and subsidies strategies, to create behaviour changes that will benefit society.
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