When it comes to grappling with the future of quantum computing, enterprises are scrambling to figure just how seriously they should take this new computing architecture. Many executives are trapped between the anxiety of missing the next wave of innovation and the fear of being played for suckers by people overhyping quantum’s revolutionary potential.
That’s why the approach to quantum by pharmaceutical giant Merck offers a clear-eyed roadmap for other enterprises to follow. The company is taking a cautious but informed approach that includes setting up an internal working group and partnering with quantum startup Seeqc to monitor developments while keeping an open mind.
According to Philipp Harbach, a theoretical chemist who is head of Merck’s In Silico Research group, a big part of the challenge remains trying to keep expectations of executives reasonable even as startup funding to quantum soars and the hype continues to mount.
“We are not evangelists of quantum computers,” Harbach said. “But we are also not skeptics. We are just realistic. If you talk to academics, they tell you there is no commercial value. And if you talk to our management, they tell you in 3 years they want a product out of it. So, there are two worlds colliding that are not very compatible. I think that’s typical for every hype cycle.”
The quantum realm
Merck’s desire for the dream of quantum computing to become reality is understandable. The fundamental nature of its business — biology and chemistry — means the company has been building molecular or “quantum” level models for more than a century.
Part of the role of the In Silico Research group is to develop those models that can solve quantum problems using evolving technologies such as data analytics and AI and applying them to natural sciences to make experimental work less time-consuming.
But those models are always limited and imperfect because they are being calculated on non-quantum platforms that can’t fully mimic the complexity of interactions. If someone can build a fully fault-tolerant quantum computer that operates at sufficient scale and cost, Merck could unlock a new generation of efficiencies and scientific breakthroughs.
“The quantum computer will be another augmentation to a classical computer,” Harbach said. “It won’t be a replacement, but an augmentation which will tackle some of these problems in a way that we cannot imagine. Hopefully, it will speed them up in a way that the efficacy of the methods we are employing will be boosted.”
About 3 years ago, Merck decided it was time to start educating itself about the emerging quantum sector. The company’s venture capital arm, M Ventures, began looking within the company for experts who could help it with due diligence as it began to assess quantum startups. That included mapping out the players and the whole value chain of quantum computing, according to Harbach.
That led to the formal creation of the Quantum Computing Task Force, which has roughly 50 members who try to communicate with quantum players large and small as well as peers among Merck’s own competition.
“We are basically an interest group trying to understand this topic,” Harbach said. “That’s why we have a quite good overview and understanding on timelines, player possibilities, and applications.”
As part of that exploration, M Ventures eventually began investing in quantum-related startups. In April 2020, the venture fund announced a $5 million investment in Seeqc, a New York-based startup that bills itself as the “Digital Quantum Computing” company.
“We thought that it might be good to have partners in the hardware part and in the software part,” Harbach said. “Seeqc will partner with us within Merck to really work on problems basically as a hardware partner.”
Seeqc is developing a hybrid approach that it believes will make quantum computing useful sooner. The idea is to combine classical computing architectures with quantum computing. It does this through its system-on-a-chip design.
This technology was originally developed at Hypres, a semiconductor electronics developer which spun out Seeqc last year. The M Ventures funding for Seeqc followed a previous $6.8 million seed round. Seeqc raised a subsequent round of $22 million last September in a round led by EQT Ventures.
According to Seeqc CEO John Levy, the company’s technology allows it to address some of the fundamental challenges facing quantum systems. Despite rapid advancements in recent years, quantum computers remain too unstable to deliver the high-performance computing needed to justify their costs.
Part of the reason for that is that qubits, the unit of quantum computing power, need to be kept at near-freezing temperatures to process. Scaling then becomes costly and difficult because a system operating with thousands of qubits would be immensely complex to manage, in part because of the massive heating issue.
Levy said Seeqc can address that problem by placing classic microchips over a qubit array to stabilize the environment at cryogenic temperatures while maintaining speed and reducing latency. The company uses a single-flux quantum technology that it has developed and that replaces the microwave pulses being used in other quantum systems. As a result, the company says its platform enables quantum computing at about 1/400 of the cost of current systems in development.
“We have taken much of the complexity that you’ve seen in a quantum computer and we’ve removed almost all of that by building a set of chips that we’ve designed,” Levy said.
Just as important is a philosophical approach Seeqc is taking. It’s not building a general-purpose quantum computer. Instead, it plans to build application-specific ones that are tailored specifically to the problems a client is trying to solve. Because Seeqc has its own chip foundry, it can customize its chips to the needs of application developers as they create different algorithms, Levy said.
In that spirit, Merck’s Quantum Computing Task Force is working closely with Seeqc to create viable quantum computers that can be used by its various businesses.
“Their technology is a key technology to scale a quantum computer, which is actually much more important because it will make quantum computers bigger and cheaper,” Harbach said. “And this is, of course, essential for the whole market.”
Merck’s quantum future
For all this activity, Harbach’s view of quantum’s potential remains sober. He sees nothing on the market that will have any commercial impact, certainly not for Merck. At this point, many of the company’s questions remain academic.
“What we are basically interested in is how — or will — the quantum computer hardware ever be scalable to a level that it can tackle problems of realistic size to us,” Harbach said. “And the same question also goes to the software side. Will there ever be algorithms that can basically mimic these problems on a quantum computer efficiently so that they don’t run into noise problems? We are not interested in simulating a molecule right now on a quantum computer. Everything we try to understand is about the timelines: What will be possible and when will it possible.”
Harbach has watched the rise in quantum startup funding and various milestone announcements but remains dubious of many of these claims.
“They are creating a new market where there’s not even the technology ready for it,” Harbach said. “You have to stay realistic. There’s a lot of money at the moment from governments and VCs. There’s a lot of boost from consultancies because they try to sell the consultancy. And if you talk to experts, it’s the other way around. They tell you not before 15 years.”
The questions Merck asks internally are split into 2 fundamental categories: When will there be a quantum computer that can be more efficient at processing its current quantum models? And when will there be a quantum computer that is so powerful that it opens up new problems and new solutions that the company cannot even imagine today?
“Quantum will be a thing, definitely,” Harbach said. “The only question is when, and I’m really, really sure it won’t be in the next two years. I wouldn’t even say three years. There will be a quantum winter. Winter is coming.”
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Predictive Maintenance is a Killer AI App
By John P. Desmond, AI Trends Editor
Predictive maintenance (PdM) has emerged as a killer AI app.
In the past five years, predictive maintenance has moved from a niche use case to a fast-growing, high return on investment (ROI) application that is delivering true value to users. These developments are an indication of the power of the Internet of Things (IoT) and AI together, a market considered in its infancy today.
These observations are from research conducted by IoT Analytics, consultants who supply market intelligence, which recently estimated that the $6.9 billion predictive maintenance market will reach $28.2 billion by 2026.
The company began its research coverage of the IoT-driven predictive maintenance market in 2016, at an industry maintenance conference in Dortmund, Germany. Not much was happening. “We were bitterly disappointed,” stated Knud Lasse Lueth, CEO at IoT Analytics, in an account in IoT Business News. “Not a single exhibitor was talking about predictive maintenance.”
Things have changed. IoT Analytics analyst Fernando Alberto Brügge stated, “Our research in 2021 shows that predictive maintenance has clearly evolved from the rather static condition-monitoring approach. It has become a viable IoT application that is delivering overwhelmingly positive ROI.”
Technical developments that have contributed to the market expansion include: a simplified process for connecting IoT assets, major advances in cloud services, and improvements in the accessibility of machine learning/data science frameworks, the analysts state.
Along with the technical developments, the predictive maintenance market has seen a steady increase in the number of software and service providers offering solutions. IoT Analytics identified about 100 companies in the space in 2016; today the company identifies 280 related solution providers worldwide. Many of them are startups who recently entered the field. Established providers including GE, PTC, Cisco, ABB, and Siemens, have entered the market in the past five years, many through acquisitions.
The market still has room; the analysts predict 500 companies will be in the business in the next five years.
In 2016, the ROI from predictive analytics was unclear. In 2021, a survey of about 100 senior IT executives from the industrial sector found that predictive maintenance projects have delivered a positive ROI in 83% of the cases. Some 45% of those reported amortizing their investments in less than a year. “This data demonstrated how attractive the investment has become in recent years,” the analysts stated.
More IoT Sensors Means More Precision
Implemented projects that the analysts studied in 2016 relied on a limited number of data sources, typically one sensor value, such as vibration or temperature. Projects described in the 2021 report described 11 classes of data sources, such as data from existing sensors or data from the controllers. As more sources are tapped, the precision of the predictions increase, the analysts state.
Many projects today are using hybrid modeling approaches that rely on domain expertise, virtual sensors and augmented data. AspenTech and PARC are two suppliers identified in the report as embracing hybrid modeling approaches. AspenTech has worked with over 60 companies to develop and test hybrid models that combine physics with ML/data science knowledge, enhancing prediction accuracy.
The move to edge computing is expected to further benefit predictive modeling projects, by enabling algorithms to run at the point where data is collected, reducing response latency. The supplier STMicroelectronics recently introduced some smart sensor nodes that can gather data and do some analytic processing.
More predictive maintenance apps are being integrated with enterprise software systems, such as enterprise resource planning (ERP) or computerized maintenance management systems (CMMS). Litmus Automation offers an integration service to link to any industrial asset, such as a programmable logic controller, a distributed control system, or a supervisory control and data acquisition system.
Reduced Downtime Results in Savings
Gains come from preventing downtime. “Predictive maintenance is the result of monitoring operational equipment and taking action to prevent potential downtime or an unexpected or negative outcome,” stated Mike Leone, an analyst at IT strategy firm Enterprise Strategy Group, in an account from TechTarget.
Advances that have made predictive maintenance more practical today include sensor technology becoming more widespread, and the ability to monitor industrial machines in real time, stated Felipe Parages, senior data scientist at Valkyrie, data sense consultants. With more sensors, the volume of data has grown exponentially, and data analytics via cloud services has become available.
It used to be that an expert had to perform an analysis to determine if a machine was not operating in an optimal way. “Nowadays, with the amount of data you can leverage and the new techniques based on machine learning and AI, it is possible to find patterns in all that data, things that are very subtle and would have escaped notice by a human being,” stated Parages.
As a result, one person can now monitor hundreds of machines, and companies are accumulating historical data, which enables deeper trend analysis. “Predictive maintenance “is a very powerful weapon,” he stated.
In an example project, Italy’s primary rail operator, Trenitalia, adopted predictive maintenance for its high-speed trains. The system is expected to save eight to 10% of an annual maintenance budget of 1.3 billion Euros, stated Paul Miller, an analyst with research firm Forrester, which recently issued a report on the project.
“They can eliminate unplanned failures which often provide direct savings in maintenance but just as importantly, by taking a train out of service before it breaks—that means better customer service and happier customers,” Miller stated. He recommended organizations start out with predictive maintenance by fielding a pilot project.
In an example of the types of cooperation predictive maintenance projects are expected to engender, the CEOs of several European auto and electronics firms recently announced plans to join forces to form the “Software Republique,” a new ecosystem for innovation in intelligent mobility. Atos, Dassault Systèmes, Groupe Renault, and STMicroelectronics and Thales announced their decision to pool their expertise to accelerate the market.
Luca de Meo, Chief Executive Officer of Groupe Renault, stated in a press release from STMicroelectronics, “In the new mobility value chain, on-board intelligence systems are the new driving force, where all research and investment are now concentrated. Faced with this technological challenge, we are choosing to play collectively and openly. There will be no center of gravity, the value of each will be multiplied by others. The combined expertise in cybersecurity, microelectronics, energy and data management will enable us to develop unique, cutting-edge solutions for low-carbon, shared, and responsible mobility, made in Europe.”
The Software République will be based in Guyancourt, a commune in north-central France at the Renault Technocentre in a building called Odyssée, a 12,000 square meter space which is eco-responsible. For example, its interior and exterior structure is 100 percent wood, and the building is covered with photovoltaic panels.
Post Office Looks to Gain an Edge With Edge Computing
By AI Trends Editor John P. Desmond
NVIDIA on May 6 detailed a partnership with the US Postal Service underway for over a year to speed up mail service using AI, with a goal of reducing current processing time tasks that take days to hours.
The project fields edge servers at 195 Post Services sites across the nation, which review 20 terabytes of images a day from 1,000 mail processing machines, according to a post on the NVIDIA blog.
“The federal government has been for the last several years talking about the importance of artificial intelligence as a strategic imperative to our nation, and as an important funding priority. It’s been talked about in the White House, on Capitol Hill, in the Pentagon. It’s been funded by billions of dollars, and it’s full of proof of concepts and pilots,” stated Anthony Robbins, Vice President of Federal for NVIDIA, in an interview with Nextgov. “And this is one of the few enterprise–wide examples of an artificial intelligence deployment that I think can serve to inspire the whole of the federal government.”
The project started with USPS AI architect at the time Ryan Simpson, who had the idea to try to expand an image analysis system a postal team was developing, into something much bigger, according to the blog post. (Simpson worked for USPS for over 12 years, and moved to NVIDIA as a senior data scientist eight months ago.) He believed that a system could analyze billions of images each center generated, and gain insights expressed in a few data points that could be shared quickly over the network.
In a three-week sprint, Simpson worked with half a dozen architects at NVIDIA and others to design the needed deep-learning models. The work was done within the Edge Computing Infrastructure Program (ECIP), a distributed edge AI system up and running on Nvidia’s EGX platform at USPS. The EGX platform enables existing and modern, data-intensive applications to be accelerated and secure on the same infrastructure, from data center to edge.
“It used to take eight or 10 people several days to track down items, now it takes one or two people a couple of hours,” stated Todd Schimmel, Manager, Letter Mail Technology, USPS. He oversees USPS systems including ECIP, which uses NVIDIA-Certified edge servers from Hewlett-Packard Enterprise.
In another analysis, a computer vision task that would have required two weeks on a network of servers with 800 CPUs can now get done in 20 minutes on the four NVIDIA V100 Tensor Core GPUs in one of the HPE Apollo 6500 servers.
Contract Awarded in 2019 for System Using OCR
USPS had put out a request for proposals for a system using optical character recognition (OCR) to streamline its imaging workflow. “In the past, we would have bought new hardware, software—a whole infrastructure for OCR; or if we used a public cloud service, we’d have to get images to the cloud, which takes a lot of bandwidth and has significant costs when you’re talking about approximately a billion images,” stated Schimmel.
Today, the new OCR application will rely on a deep learning model in a container on ECIP managed by Kubernetes, the open source container orchestration system, and served by NVIDIA Triton, the company’s open-source inference-serving software. Triton allows teams to deploy trained AI models from any framework, such as TensorFlow or PyTorch.
The deployment was very streamlined,” Schimmel stated. “We awarded the contract in September 2019, started deploying systems in February 2020 and finished most of the hardware by August—the USPS was very happy with that,” he added
Multiple models need to communicate to the USPS OCR application to work. The app that checks for mail items alone requires coordinating the work of more than a half dozen deep-learning models, each checking for specific features. And operators expect to enhance the app with more models enabling more features in the future.
“The models we have deployed so far help manage the mail and the Postal Service—they help us maintain our mission,” Schimmel stated.
One model, for example, automatically checks to see if a package carries the right postage for its size, weight, and destination. Another one that will automatically decipher a damaged barcode could be online this summer.
“We’re at the very beginning of our journey with edge AI. Every day, people in our organization are thinking of new ways to apply machine learning to new facets of robotics, data processing and image handling,” he stated.
Accenture Federal Services, Dell Technologies, and Hewlett-Packard Enterprise contributed to the USPS OCR system incorporating AI, Robbins of NVIDIA stated. Specialized computing cabinets—or nodes—that contain hardware and software specifically tuned for creating and training ML models, were installed at two data centers.
“The AI work that has to happen across the federal government is a giant team sport,” Robbins stated to Nextgov. “And the Postal Service’s deployment of AI across their enterprise exhibited just that.”
The new solutions could help the Postal Service improve delivery standards, which have fallen over the past year. In mid-December, during the last holiday season, the agency delivered as little as 62% of first-class mail on time—the lowest level in years, according to an account in VentureBeat . The rate rebounded to 84% by the week of March 6 but remained below the agency’s target of about 96%.
The Postal Service has blamed the pandemic and record peak periods for much of the poor service performance.
Here Come the AI Regulations
By AI Trends Staff
New laws will soon shape how companies use AI.
The five largest federal financial regulators in the US recently released a request for information how banks use AI, signaling that new guidance is coming for the finance business. Soon after that, the US Federal Trade Commission released a set of guidelines on “truth, fairness and equity” in AI, defining the illegal use of AI as any act that “causes more harm than good,” according to a recent account in Harvard Business Review.
And on April 21, the European Commission issued its own proposal for the regulation of AI (See AI Trends, April 22, 2021)
While we don’t know what these regulation will allow, “Three central trends unite nearly all current and proposed laws on AI, which means that there are concrete actions companies can undertake right now to ensure their systems don’t run afoul of any existing and future laws and regulations,” stated article author Andrew Burt, the managing partner of bnh.ai, a boutique law firm focused on AI and analytics.
First, conduct assessments of AI risks. As part of the effort, document how the risks have been minimized or resolved. Regulatory frameworks that refer to these “algorithmic impact assessments,” or “IA for AI,” are available.
For example, Virginia’s recently-passed Consumer Data Protection Act, requires assessments for certain types of high-risk algorithms.
The EU’s new proposal requires an eight-part technical document to be completed for high-risk AI systems that outlines “the foreseeable unintended outcomes and sources of risks” of each AI system, Burt states. The EU proposal is similar to the Algorithmic Accountability Act filed in the US Congress in 2019. The bill did not go anywhere but is expected to be reintroduced.
Second, accountability and independence. This suggestion is that the data scientists, lawyers and others evaluating the AI system have different incentives than those of the frontline data scientists. This could mean that the AI is tested and validated by different technical personnel than those who originally developed it, or organizations may choose to hire outside experts to assess the AI system.
“Ensuring that clear processes create independence between the developers and those evaluating the systems for risk is a central component of nearly all new regulatory frameworks on AI,” Burt states.
Third, continuous review. AI systems are “brittle and subject to high rates of failure,” with risks that grow and change over time, making it difficult to mitigate risk at a single point in time. “Lawmakers and regulators alike are sending the message that risk management is a continual process,” Burt stated.
Approaches in US, Europe and China Differ
The approaches between the US, Europe and China toward AI regulation differ in their approach, according to a recent account in The Verdict, based on analysis by Global Data, the data analytics and consulting company based in London.
“Europe appears more optimistic about the benefits of regulation, while the US has warned of the dangers of over regulation,”’ the account states. Meanwhile, “China continues to follow a government-first approach” and has been widely criticized for the use of AI technology to monitor citizens. The account noted examples in the rollout by Tencent last year of an AI-based credit scoring system to determine the “trust value” of people, and the installation of surveillance cameras outside people’s homes to monitor the quarantine imposed after the breakout of COVID-19.
“Whether the US’ tech industry-led efforts, China’s government-first approach, or Europe’s privacy and regulation-driven approach is the best way forward remains to be seen,” the account stated.
In the US, many companies are aware of the risk of new AI regulation that could stifle innovation and their ability to grow in the digital economy, suggested a recent report from pwc, the multinational professional services firm.
“It’s in a company’s interests to tackle risks related to data, governance, outputs, reporting, machine learning and AI models, ahead of regulation,” the pwc analysts state. They recommended business leaders assemble people from across the organization to oversee accountability and governance of technology, with oversight from a diverse team that includes members with business, IT and specialized AI skills.
Critics of European AI Act Cite Too Much Gray Area
While some argue that the European Commission’s proposed AI Act leaves too much gray area, the hope of the European Commission is that their proposed AI Act will provide guidance for businesses wanting to pursue AI, as well as a degree of legal certainty.
“Trust… we think is vitally important to allow the development we want of artificial intelligence,” stated Thierry Breton, European Commissioner for the Internal Market, in an account in TechCrunch. AI applications “need to be trustworthy, safe, non-discriminatory — that is absolutely crucial — but of course we also need to be able to understand how exactly these applications will work.”
“What we need is to have guidance. Especially in a new technology… We are, we will be, the first continent where we will give guidelines—we’ll say ‘hey, this is green, this is dark green, this is maybe a little bit orange and this is forbidden’. So now if you want to use artificial intelligence applications, go to Europe! You will know what to do, you will know how to do it, you will have partners who understand pretty well and, by the way, you will come also to the continent where you will have the largest amount of industrial data created on the planet for the next ten years.”
“So come here—because artificial intelligence is about data—we’ll give you the guidelines. We will also have the tools to do it and the infrastructure,” Breton suggested.
Another reaction was that the Commission’s proposal has overly broad exemptions, such as for law enforcement to use remote biometric surveillance including facial recognition technology, and it does not go far enough to address the risk of discrimination.
Reactions to the Commission’s proposal included plenty of criticism of overly broad exemptions for law enforcement’s use of remote biometric surveillance (such as facial recognition tech) as well as concerns that measures in the regulation to address the risk of AI systems discriminating don’t go nearly far enough.
“The legislation lacks any safeguards against discrimination, while the wide-ranging exemption for ‘safeguarding public security’ completely undercuts what little safeguards there are in relation to criminal justice,” stated Griff Ferris, legal and policy officer for Fair Trials, the global criminal justice watchdog based in London. “The framework must include rigorous safeguards and restrictions to prevent discrimination and protect the right to a fair trial. This should include restricting the use of systems that attempt to profile people and predict the risk of criminality.”
To accomplish this, he suggested, “The EU’s proposals need radical changes to prevent the hard-wiring of discrimination in criminal justice outcomes, protect the presumption of innocence and ensure meaningful accountability for AI in criminal justice.”
Pandemic Spurred Identity Fraud; AI and Biometrics Are Responding
By AI Trends Staff
Cyberattacks and identity fraud losses increased dramatically in 2020 as the pandemic made remote work the norm, setting the stage for AI and biometrics to combine in efforts to attain a higher level of protection.
One study found banks worldwide saw a 238% jump in cyberattacks between February and April 2020; a study from Javelin Strategy & Research found that identity fraud losses grew to $56 billion last year as fraudsters used stolen personal information to create synthetic identities, according to a recent account from Pymnts.com. In addition, automated bot attacks shot upward by 100 million between July and December, targeting companies in a range of industries.
Companies striving for better protection risk making life more difficult for their customers; another study found that 40% of financial institutions frequently mistake the online actions of legitimate customers to those of fraudsters.
“As we look toward the post-pandemic—or, more accurately, inter-pandemic—era, we see just how good fraudsters were at using synthetic identities to defeat manual and semi-manual onboarding processes,” stated Caleb Callahan, Vice President of Fraud at Stash Financial of New York, offering a personal finance app, in an interview with Pymnts.
SIM Sway Can Create a Synthetic Identity
One technique for achieving a synthetic identity is a SIM swap, in which someone contacts your wireless carrier and is able to convince the call center employee that they are you, using personal data that may have been exposed in hacks, data breaches or information publicly shared on social networks, according to an account on CNET.
Once your phone number is assigned to a new card, all of your incoming calls and text messages will be routed to whatever phone the new SIM card is in.
Identity theft losses were $712.4 billion-plus in 2020, up 42% from 2019, Callahan stated. “To be frank, our defenses are fragmented and too dependent on technologies such as SMS [texting] that were never designed to provide secure services. Banks and all businesses should be looking at how to unify data signals and layer checkpoints in order to keep up with today’s sophisticated fraudsters,” he stated.
Asked what tools and technologies would help differentiate between fraudsters and legitimate customers, Callahan stated, “in an ideal world, we would have a digital identity infrastructure that banks and others could depend on, but I think that we are some ways away from that right now.”
Going forward, “The needs of the travel and hospitality, health, education and other sectors might accelerate the evolution of infrastructure for safety and security,” Callahan foresees.
AI and Biometrics Seen as Offering Security Advantages
AI can be employed to protect digital identity fraud, such as by offering greater accuracy and speed when it comes to verifying a person’s identity, or by incorporating biometric data so that a cybercriminal would not be able to gain access to information by only providing credentials, according to an account in Forbes.
“AI has the power to save the world from digital identity fraud,” stated Deepak Gupta, author of the Forbes article and cofounder and CTO of LoginRadius, a cloud-based consumer identity platform. “In the fight against ID theft, it is already a strong weapon. AI systems are entirely likely to end the reign of the individual hacker.”
While he sees AI authentication as being in an early phase, Gupta recommended that companies examine the following: the use of intelligent adaptive authentication, such as local and device fingerprint; biometric authentication, based on the face or fingerprints; and smart data filters. “A well-developed AI protection system will have the ability to respond in nanoseconds to close a leak,” he stated.
Pandemic Altered Consumer Financial Behavior, Spurred Identity Fraud
The global pandemic has had a dramatic impact on consumer financial behavior. Consumers spent more time at home in 2020, transacted less than in previous years, and relied heavily on streaming services, digital commerce, and payments. They also corresponded more via email and text, for both work and personal life.
“The pandemic inspired a major shift in how criminals approach fraud,” stated John Buzzard, Lead Analyst, Fraud & Security, with Javelin Strategy & Research in a press release. “Identity fraud has evolved and now reflects the lengths criminals will take to directly target consumers in order to steal their personally identifiable information.”
Companies made quick adjustments to their business models, such as by increasing remote interactions with borrowers for loan originations and closings, and criminals pounced on new vulnerabilities they discovered. Nearly one-third of identity fraud victims say their financial services providers did not satisfactorily resolve their problems, and 38% of victims closed their accounts because of lack of resolution, the Javelin researchers found.
“It is clear that financial institutions must continue to proactively and transparently manage fraud as a means to deepen their customer relationships,” stated Eric Kraus, Vice President and General Manager of Fraud, Risk and Compliance, FIS. The company offers technology solutions for merchants, banks, and capital markets firms globally. “Through our continuing business relationships with financial institutions, we know firsthand that consumers are looking to their banks to resolve instances of fraud, regardless of how the fraud occurred,” he added.
This push from consumers who are becoming increasingly savvy online will lay a foundation for safer digital transactions.
“Static forms of consumer authentication must be replaced with a modern, standards-based approach that utilizes biometrics,” stated David Henstock, Vice President of Identity Products at Visa, the world’s leader in digital payments. “Businesses benefit from reduced customer friction, lower abandonment rates and fewer chargebacks, while consumers benefit from better fraud prevention and faster payment during checkout.”
The 2021 Identity Fraud Study from Javelin is now in its 18th year.
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