By Matthew Attwell, Risk & Client Services Director at The ai Corporation (ai)
Fraud has reached the highest levels on record, affecting more organizations than ever. The scale of the problem was revealed in last year’s PWC Global Economic Crime and Fraud Survey. Where 49 percent of the 7,228 businesses across 123 territories, contacted by PwC, reported that they had experienced fraud and economic crime, over a two-year period.
Payment Fraud is also on the rise, alongside our awareness of it. In fraud professional circles, it is now recognised that adoption of a data-driven approach, which includes A.I, is required to effectively tackle fraud. A.I is now seen as the next evolutionary step for a data-driven approach, following from human data analysis, and I fully expect that blend of human input and A.I to deliver the performance required to counter the evolving payment and fraud landscape in the future.
Many organisations currently utilise A.I to augment, not replace, their human interactions. Viewing A.I as a productivity and scalability enabler. A.I is seen as an additional member of the ‘team’, which can operate at a similar level to the human equivalent and operates 24×7. As experience and confidence in A.I grows, organisations are becoming more willing to place increase emphasis on A.I to fulfil specific areas of the fraud management lifecycle.
It’s clear that certain elements of the fraud lifecycle will continue to be heavily supported by A.I in the future, for example, the automation of repetitive activity, advanced pattern detection and scalability of completing tasks in parallel. However, there are other aspects of fraud prevention, such as investigation and business strategy, that require ideation and innovation, and will remain best tackled by humans.
How A.I is helping to tackle fraud today
Traditional, manual, rules-based fraud detection systems require full time manual effort to keep up with changing fraud patterns. A.I supplements these systems, by keeping up with trends as they change, by analysing the data and understanding any underlying patterns, which experienced fraud analysts are unable to do due to the large volume.
A.I models remove the need to continually and manually manage a fraud system, by adding and removing rules, analysing data and other manual activities. Providing operational benefit on top of fraud risk benefit. By incorporating A.I tools into the fraud detection ecosystem, businesses experience efficiency gains throughout the entire risk management sector; as time costly, manual processes become automated and highly skilled individuals can be freed up to work on other revenue generating activities.
A.I is an excellent tool for automating the day to day upkeep of a risk system. Fraudsters are clever and are constantly developing new threats. A.I is the best defence against these fast-changing trends, with models constantly learning, and adapting to better understand cardholder spending, making it better at detecting anomalous activity. Models can adapt to new threats and traditionally ‘difficult to detect’ types of fraud, due to the statistical nature of A.I, which can uncover those patterns. A.I models, and processes, should be applied in big data environments, where it is expensive, not feasible, or impossible to manually analyse the data for fraud trend changes.
New data streams are also particularly well suited to application of A.I, since, in many cases, little is known about fraud by the fraud manager. A.I can extract fraud patterns in the data and use that intelligence to prevent further threats, while learning new fraud patterns, as they happen. This is done much faster than any manual process. Enabling a faster, safer ‘product to market’ process.
A.I is best utilised for monitoring activity on a temporal basis, where there is either historical or ongoing activity that needs to be assessed to identify a change in behaviour. Static fraud detection, such as application fraud, that has been traditionally tackled by scorecard approaches can also be tackled using A.I.
However, drilling down further, the capability to detect different types of fraudulent activity is less pronounced with AI. My team and I have conducted experiments, in conjunction with our clients, to identify whether A.I is better suited to certain types of card fraud – lost/stolen cards, skimming or collusion etc. We found that there was no significant difference in detection capability across different fraud types.
While traditionally A.I was seen to require a fully employed data scientist resource to develop, deploy and maintain AI solutions, that took months to realise benefit; there has been a paradigm shift towards self-service and increased speed of deployment. This has meant that business users (citizen data scientists) can manage A.I technologies and deploy solutions, within a matter of hours. This paradigm shift has resulted in a wave of interest and adoption by business, who were previously unable to commit resources towards the technology.
The future hold for its use in tackling fraud?
The use of A.I for fraud detection and prevention will become increasingly prevalent, as fraud managers look to increase efficiency and fraud detection rates; while craving out time to pursue other opportunities and business needs. A.I systems are continually improving on their performance and will adapt to add in other business-add features, such as marketing opportunity dashboards.
Over time, A.I systems will be increasingly relied upon, as they become more trusted to carry out effective fraud detection. This coupled with data, that is getting bigger, will be increasingly difficult to manually analyse for the same level of fraud performance, and we will see A.I utilised to a higher degree. Once A.I technology has developed to the point where the system performance can no longer be improved with manual input, A.I will become a necessity to maintain adequate system performance and business goals.
In summary, A.I will become more central in tackling fraud, as institutions struggle to scale their operations, while keeping pace with increasing volumes of data, transactions and channels. Recent data from UK Finance reported that payments in the UK in 2017 totalled 38.8 billion, split amongst nine main payment types. This is predicted to rise 5% to 40.9 billion by 2027, during which cash usage, as a percentage of all payments, will reduce from 34% to 16%. This rapid increase, combined with initiatives that speed up customer interaction (expedited provision of goods, faster payments etc.) will mean that there are more fraud assessment decisions that need to be made in real-time. The only way to achieve this, and maintain profitably, is to use A.I to support the human workforce.
To take the nation’s financial pulse, we must go digital
By Pete Bulley, Director of Product, Aire
The last six months have brought the precarious financial situation of many millions across the world into sharper focus than ever before. But while the figures may be unprecedented, the underlying problem is not a new one – and it requires serious attention as well as action from lenders to solve it.
Research commissioned by Aire in February found that eight out of ten adults in the UK would be unable to cover essential monthly spending should their income drop by 20%. Since then, Covid-19 has increased the number without employment by 730,000 people between July and March, and saw 9.6 million furloughed as part of the job retention scheme.
The figures change daily but here are a few of the most significant: one in six mortgage holders had opted to take a payment holiday by June. Lenders had granted almost a million credit card payment deferrals, provided 686,500 payment holidays on personal loans, and offered 27 million interest-free overdrafts.
The pressure is growing for lenders and with no clear return to normal in sight, we are unfortunately likely to see levels of financial distress increase exponentially as we head into winter. Recent changes to the job retention scheme are signalling the start of the withdrawal of government support.
The challenge for lenders
Lenders have been embracing digital channels for years. However, we see it usually prioritised at acquisition, with customer management neglected in favour of getting new customers through the door. Once inside, even the most established of lenders are likely to fall back on manual processes when it comes to managing existing customers.
It’s different for fintechs. Unburdened by legacy systems, they’ve been able to begin with digital to offer a new generation of consumers better, more intuitive service. Most often this is digitised, mobile and seamless, and it’s spreading across sectors. While established banks and service providers are catching up — offering mobile payments and on-the-go access to accounts — this part of their service is still lagging. Nowhere is this felt harder than in customer management.
Time for a digital solution in customer management
With digital moving higher up the agenda for lenders as a result of the pandemic, many still haven’t got their customer support properly in place to meet demand. Manual outreach is still relied upon which is both heavy on resource and on time.
Lenders are also grappling with regulation. While many recognise the moral responsibility they have for their customers, they are still blind to the new tools available to help them act effectively and at scale.
In 2015, the FCA released its Fair Treatment of Customers regulations requiring that ‘consumers are provided with clear information and are kept appropriately informed before, during and after the point of sale’.
But when the individual financial situation of customers is changing daily, never has this sentiment been more important (or more difficult) for lenders to adhere to. The problem is simple: the traditional credit scoring methods relied upon by lenders are no longer dynamic enough to spot sudden financial change.
The answer lies in better, and more scalable, personalised support. But to do this, lenders need rich, real-time insight so that lenders can act effectively, as the regulator demands. It needs to be done at scale and it needs to be done with the consumer experience in mind, with convenience and trust high on the agenda.
Placing the consumer at the heart of the response
To better understand a customer, inviting them into a branch or arranging a phone call may seem the most obvious solution. However, health concerns mean few people want to see their providers face-to-face, and fewer staff are in branches, not to mention the cost and time outlay by lenders this would require.
Call centres are not the answer either. Lack of trained capacity, cost and the perceived intrusiveness of calls are all barriers. We know from our own consumer research at Aire that customers are less likely to engage directly with their lenders on the phone when they feel payment demands will be made of them.
If lenders want reliable, actionable insight that serves both their needs (and their customers) they need to look to digital.
Asking the person who knows best – the borrower
So if the opportunity lies in gathering information directly from the consumer – the solution rests with first-party data. The reasons we pioneer this approach at Aire are clear: firstly, it provides a truly holistic view of each customer to the lender, a richer picture that covers areas that traditional credit scoring often misses, including employment status and savings levels. Secondly, it offers consumers the opportunity to engage directly in the process, finally shifting the balance in credit scoring into the hands of the individual.
With the right product behind it, this can be achieved seamlessly and at scale by lenders. Pulse from Aire provides a link delivered by SMS or email to customers, encouraging them to engage with Aire’s Interactive Virtual Interview (IVI). The information gathered from the consumer is then validated by Aire to provide the genuinely holistic view of a consumer that lenders require, delivering insights that include risk of financial difficulty, validated disposable income and a measure of engagement.
No lengthy or intrusive phone calls. No manual outreach or large call centre requirements. And best of all, lenders can get started in just days and they save up to £60 a customer.
Too good to be true?
This still leaves questions. How can you trust data provided directly from consumers? What about AI bias – are the results fair? And can lenders and customers alike trust it?
To look at first-party misbehaviour or ‘gaming’, sophisticated machine-learning algorithms are used to validate responses for accuracy. Essentially, they measure responses against existing contextual data and check its plausibility.
Aire also looks at how the IVI process is completed. By looking at how people complete the interview, not just what they say, we can spot with a high degree of accuracy if people are trying to game the system.
AI bias – the system creating unfair outcomes – is tackled through governance and culture. In working towards our vision of a world where finance is truly free from bias or prejudice, we invest heavily in constructing the best model governance systems we can at Aire to ensure our models are analysed systematically before being put into use.
This process has undergone rigorous improvements to ensure our outputs are compliant by regulatory standards and also align with our own company principles on data and ethics.
That leaves the issue of encouraging consumers to be confident when speaking to financial institutions online. Part of the solution is developing a better customer experience. If the purpose of this digital engagement is to gather more information on a particular borrower, the route the borrower takes should be personal and reactive to the information they submit. The outcome and potential gain should be clear.
The right technology at the right time?
What is clear is that in Covid-19, and the resulting financial shockwaves, lenders face an unprecedented challenge in customer management. In innovative new data in the form of first-party data, harnessed ethically, they may just have an unprecedented solution.
The Future of Software Supply Chain Security: A focus on open source management
By Emile Monette, Director of Value Chain Security at Synopsys
Software Supply Chain Security: change is needed
Attacks on the Software Supply Chain (SSC) have increased exponentially, fueled at least in part by the widespread adoption of open source software, as well as organisations’ insufficient knowledge of their software content and resultant limited ability to conduct robust risk management. As a result, the SSC remains an inviting target for would-be attackers. It has become clear that changes in how we collectively secure our supply chains are required to raise the cost, and lower the impact, of attacks on the SSC.
A report by Atlantic Council found that “115 instances, going back a decade, of publicly reported attacks on the SSC or disclosure of high-impact vulnerabilities likely to be exploited” in cyber-attacks were implemented by affecting aspects of the SSC. The report highlights a number of alarming trends in the security of the SSC, including a rise in the hijacking of software updates, attacks by state actors, and open source compromises.
This article explores the use of open source software – a primary foundation of almost all modern software – due to its growing prominence, and more importantly, its associated security risks. Poorly managed open source software exposes the user to a number of security risks as it provides affordable vectors to potential attackers allowing them to launch attacks on a variety of entities—including governments, multinational corporations, and even the small to medium-sized companies that comprise the global technology supply chain, individual consumers, and every other user of technology.
The risks of open source software for supply chain security
The 2020 Open Source Security and Risk Analysis (OSSRA) report states that “If your organisation builds or simply uses software, you can assume that software will contain open source. Whether you are a member of an IT, development, operations, or security team, if you don’t have policies in place for identifying and patching known issues with the open source components you’re using, you’re not doing your job.”
Open source code now creates the basic infrastructure of most commercial software which supports enterprise systems and networks, thus providing the foundation of almost every software application used across all industries worldwide. Therefore, the need to identify, track and manage open source code components and libraries has risen tremendously.
License identification, patching vulnerabilities and introducing policies addressing outdated open source packages are now all crucial for responsible open source use. However, the use of open source software itself is not the issue. Because many software engineers ‘reuse’ code components when they are creating software (this is in fact a widely acknowledged best practice for software engineering), the risk of those components becoming out of date has grown. It is the use of unpatched and otherwise poorly managed open source software that is really what is putting organizations at risk.
The 2020 OSSRA report also reveals a variety of worrying statistics regarding SSC security. For example, according to the report, it takes organisations an unacceptably long time to mitigate known vulnerabilities, with 2020 being the first year that the Heartbleed vulnerability was not found in any commercial software analyzed for the OSSRA report. This is six years after the first public disclosure of Heartbleed – plenty of time for even the least sophisticated attackers to take advantage of the known and publicly reported vulnerability.
The report also found that 91% of the investigated codebases contained components that were over four years out of date or had no developments made in the last two years, putting these components at a higher risk of vulnerabilities. Additionally, vulnerabilities found in the audited codebases had an average age of almost 4 ½ years, with 19% of vulnerabilities being over 10 years old, and the oldest vulnerability being a whopping 22 years old. Therefore, it is clear that open source users are not adequately defending themselves against open source enabled cyberattacks. This is especially concerning as 99% of the codebases analyzed in the OSSRA report contained open source software, with 75% of these containing at least one vulnerability, and 49% containing high-risk vulnerabilities.
Mitigating open source security risks
In order to mitigate security risks when using open source components, one must know what software you’re using, and which exploits impact its vulnerabilities. One way to do this is to obtain a comprehensive bill of materials from your suppliers (also known as a “build list” or a “software bill of materials” or “SBOM”). Ideally, the SBOM should contain all the open source components, as well as the versions used, the download locations for all projects and dependencies, the libraries which the code calls to, and the libraries that those dependencies link to.
Creating and communicating policies
Modern applications contain an abundance of open source components with possible security, code quality and licensing issues. Over time, even the best of these open source components will age (and newly discovered vulnerabilities will be identified in the codebase), which will result in them at best losing intended functionality, and at worst exposing the user to cyber exploitation.
Organizations should ensure their policies address updating, licensing, vulnerability management and other risks that the use of open source can create. Clear policies outlining introduction and documentation of new open source components can improve the control of what enters the codebase and that it complies with the policies.
Prioritizing open source security efforts
Organisations should prioritise open source vulnerability mitigation efforts in relation to CVSS (Common Vulnerability Scoring System) scores and CWE (Common Weakness Enumeration) information, along with information about the availability of exploits, paying careful attention to the full life cycle of the open source component, instead of only focusing on what happens on “day zero.” Patch priorities should also be in-line with the business importance of the asset patched, the risk of exploitation and the criticality of the asset. Similarly, organizations must consider using sources outside of the CVSS and CWE information, many of which provide early notification of vulnerabilities, and in particular, choosing one that delivers technical details, upgrade and patch guidance, as well as security insights. Lastly, it is important for organisations to monitor for new threats for the entire time their applications remain in service.
On the Frontlines of Fraud: Tactics for Merchants to Protect Their Businesses
By Nicole Jass, Senior Vice President of Small Business and Fraud Products at FIS
Fraud isn’t new, but the new realities brought by COVID-19 for merchants, and the rising tide of attacks have changed the way we need to approach the fight. Even before the pandemic broke out earlier this year, the transition to digital payments was well underway, which means fighting fraud needs a multilayered, multi-channel approach. Not only do you want to increase approval rates, you want to protect your revenue and stop fraud before it happens.
A great place to start is working with your payment partners to refresh your company’s fraud strategies with emerging top three best practices:
- AI-based machine learning fraud solutions helps your business stay ahead of fraud trends. Leveraging data profiles to model both “good” and “bad” behavior helps find and reduce fraud. AI-based machine learning will be increasingly essential to stay ahead of the explosive and sophisticated eCommerce fraud.
- Increasing capabilities around device fingerprinting and behavioral data are essential to detect fraud before it happens. While much of the user-input values can be easily manipulated to look more authentic, device fingerprinting and behavioral data are captured in the background to derive unique details from the user’s device and behavior. Bringing in more unique elements into decisioning, can help authenticate the users and determine the validity of the transactions.
- Prioritize user authentication. User authentication is a vital linchpin in any fraud defense and should receive even greater priority today. Setting strong password requirements and implementing multi-factor authentication helps curb fraud attacks from account takeover.
As well as working with your payment partners it’s more critical than ever to protect online transactions while not jeopardizing legitimate purchases. Fortunately, there are a few things you can do right now to address these concerns:
- Monitor warning signs
Payment verification is an important part of protecting your business. There are a variety of strategies to employ including implementing technology utilizing artificial intelligence and machine learning to help catch certain patterns. In addition to technology, here are a few other tips that may serve as warning signs. These are not a guarantee fraud is occurring, but they are flags to investigate.
o The shipping address and billing address differ
o Multiple orders of the same item
o Unusually large orders
o Multiple orders to the same address with different cards
o Unexpected international orders
- Require identity verification
Finding a balance between protection and ease of purchase will ultimately help you protect your customers and your business. The following tactics can make it more difficult for fraudsters to be successful:
o For customers that have a login, require a minimum of eight characters as well as the use of special characters in your customers’ passwords
o Set up Two-Factor Authentication that requires a One-time Passcode (OTP) via SMS or email
o Use biometric authentication for mobile purchases or logins
- Monitor chargebacks
Keeping good records is essential for eCommerce. If a customer initiates a dispute, your only available recourse is to provide proof that the order was fulfilled. Be prepared to provide all the supporting information about a disputed transaction. Worldpay’s Disputes solutions can connect to your CRM and provide you dual-layer protection against friendly fraud, first deflecting them before they arise and then fully managing chargeback defenses on your behalf.
- Monitor declines
Credit card issuers mitigate fraud by automatically declining payments that look suspicious, based on unusual card activity such as drastic changes in spending patterns or uncommon geolocations of spending. You can check your own declined payment history to help spot a potential problem. When volumes increase, the help of a payments fraud management partner is beneficial.
- Protect your own wallet
While you take the steps to protect your business, it’s also important to be mindful of your own protection—it’s incumbent on all responsible consumers to be vigilant about their data. Whether it’s simple awareness of how the fraudsters are operating today, sticking to trusted brands when shopping online, and thinking twice about what data you share and who you share it with, you’ll soon see how often you are sharing personal information about yourself.
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