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The future of the biometric smart card: Five predictions for 2019

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By Stan Swearingen, CEO of IDEX Biometrics      

 

“Biometrics” has been the buzzword on everyone’s lips within the payments industry this year, and an influx of pilot schemes has confirmed that the biometric revolution is no longer a distant fantasy, but rather a pressing reality.

Following a number of successful trials using fingerprint sensor technology within smart cards across multiple markets, (including Bulgaria, the US, Mexico, Cyprus, Japan, the Middle East and South Africa) the biometric smart card is reaching its inflection point. Key players within the banking industry, including Visa and Mastercard, are already heavily invested in this new payment technology and anticipate that biometrics will play a key role in the revolution of the payments industry.

With mass market rollout on the horizon, here are five key predictions for the biometric payment industry in 2019.

 

2019: The year of dual interface

The first half of 2017 reported 937,518 cases of financial fraud, resulting in losses of an astonishing £366.4 million[1], a clear demonstration that the PIN is no longer fit for purpose. Recent research from IDEX Biometrics supports this claim and found that 29% of consumers surveyed felt concerned about the use of PINs to keep their money secure, and as many as 70% believed that contactless payment cards left them exposed to theft and fraud. As consumer concerns continue to grow around the security of payments, so too does the need for a personalised, secure and convenient payment solution.

Enter the biometric dual interface payment card. 2019 will see biometric fingerprint sensors integrated into cards with both a micro-processor and contactless interface, removing the need for PINs. This will provide consumers with the reassurance that their money is safe as any transactions will require their finger print to authenticate it. 2019 will be the year of the dual interface where biometric authentication will be available for both contact and contactless payments!

These advances in technology and those within the payments market have meant that the concept of biometric authenticated payments is no longer a novelty. In fact, according to forecasts by Goode Intelligence, nearly 579 million biometric payment cards will be used globally by 2023[2]. The integration of the biometric sensors in the payment card will be one of the next-generation transformative innovations to breathe new life into the payment industry next year and assist in the fight against payment fraud.

 

Remote enrolment will be the key to mass market adoption

For mass market deployment of biometric smart payment cards to be possible in 2019, banking infrastructures must look at the implementation of biometric technology and ensure that this method of enrolment is accessible and convenient to all. The elderly or those with physical health limitations may struggle leaving the house to enrol within bank branches and even those who work a 9-5 day can often find making it to the bank within opening hours a challenge.

The latest advancements in remote enrolment of biometric payment cards will mean that enrolment for biometric payment cards can take place in the comfort of your own home. Card users will be able to enrol straight onto the card by simply placing their finger on the sensor (with the aid of a small device that comes with the card) to upload their print to the card’s highly secure EMV chip. There is no need for an external computer, smartphone or internet connection. Once loaded, the fingerprint never leaves the card, thus eliminating multiple attack points.

 

Biometric payments will bridge the gap to financial inclusion

In 2019 advances in biometric fingerprint authentication will be a vital ingredient when bridging the gap to financial inclusionCurrently, 1.7 billion adults remain unbanked across the globe today[3]. This is for many reasons, from immigration issues, to illiteracy as well as mental health. Those living with dementia are also at risk of losing their financial independence as their short-term memories decline. A fingerprint sensor on the card can take the place of a PIN or even signature, meaning sufferers are able to stay financially independent for longer.

Currently those who lack access to financial services are missing out on the many benefits financial inclusion has to offer. Fingerprint authentication will remove the barriers that face those with literacy challenges, or face difficulty with memory, as card payments will no longer be about what you know, or what you can remember, but who you are.

Biometric authentication will be a simple, secure and convenient solution eradicating the need for passwords and PINs as a form of authentication. For this to work as a solution to financial inclusion, banking infrastructures and card manufacturers must work together to reach a price point that enables this technology to be available to all.

The possibilities for biometrics are endless…

While biometric authentication technology is already being used with smartphones and passport identification in the UK, 2019 and beyond will see endless possibilities for the use of biometric smart cards into payments and beyond. We can even expect to see biometrics branch into the Government issued identification and IoT enabled devices arenas.

In fact, a whole host of public services is set to benefit from this secure means of authentication. The use of biometric smart cards within the NHS, for example, could see access to sensitive patient records limited only to the patient themselves. Biometric social benefits cards could control how the money is spent and that it is spent by the right person. According to IDEX research, 38% of consumers surveyed would like to see biometric methods of authentication introduced to wider government identification including driving licenses, National Insurance numbers and even passports.

 

The future of the biometrics – 2019 and beyond!

In 2019, authentication will get even smarter, and further technological advances such as multi-modal or multi-factor authentication will further enhance security within the payments landscape. This refers to technology that combines a variety of different types of biometrics in order to add an additional layer of security, including persistent authentication. For example, instead of having one single authentication, smartphones could continuously scan features to ensure the correct person is using the device.

Whilst the biometric dual interface smart payment card is set to hit the mass market next year – this is just the beginning. The payment card of tomorrow will go beyond just transactions. Biometric smart cards will serve multiple purposes – a payment card, a form of ID for restricted goods and even a loyalty card!

The early days of biometrics where it was felt to be invasive and a privacy concern are long gone. In fact, according to recent research from IDEX, 56% of consumers surveyed state they would trust the use of their fingerprint to authenticate payments more than the traditional PIN. Further to this, 52% would feel more confident if their fingerprint biometric data was stored on their payment card, rather than a bank’s central database.

Consumers are ready for the use of biometric fingerprint methods of authentication for card payments and 66% expect their roll out to authenticate in-store transactions in 2019. We predict that by 2019 biometric smart payment card adoption will go into many millions!

 

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Business

Ransomware chokes COBRA: How AI-powered data analysis can support financial services’ plight

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By Toby Butler, Financial Crime Solutions Manager at Ripjar

 

Ransomware attacks are on the increase in the United Kingdom. Most of the British Government’s COBRA meetings have been convened in response to ransomware attacks, showing how cybersecurity breaches are as pressing as national emergencies and crises. The National Cyber Security Centre’s (NCSC) annual review found this year that the country was hit by 17 ransomware incidents that were so impactful they “require a nationally coordinated response”. That extends to the financial services sector, which saw an increase of ransomware attacks with 55% of organisations hit in 2021.

Where does this leave the sector and how can artificial intelligence and machine learning be instrumental in understanding the risks companies face against future ransomware attacks?

Toby Butler

Company information is being stolen and sold to different threat groups, who prey on the individuals in that organisation who are more likely to pay them. The UK is one of the most cyber-attacked countries in the world and the Government has been criticised for being “ill-equipped” to deal with this exponential rise of fraud cases.

 

Ransomware-as-a-Service

Ransomware is one of the most common forms of cybercrime. Fighting it has become one of the biggest problems that organisations today face during their everyday operations. For instance, Malware (malicious software) encrypts the files of a single computer, then works its way through an entire network to reach the server and inflict maximum damage. Company information is being stolen and sold to different threat groups, who prey on the individuals in that organisation who are more likely to pay them.

When these attacks occur the victims, more often businesses, are left with minimal options. If they have substantial backup solutions already in place, they can attempt to restore the encrypted data to their servers. But if that data isn’t already secured elsewhere, they may need to pay a ransom to the criminals behind the attack. Thereby allowing the business to function once again and restoring their reputation. The cost of paying the ransom will feel considerably smaller compared to starting a business again from scratch. Sophos’ State of Ransomware in Financial Services 2022 report found that 52% of financial services organisations paid the ransom to restore their data, the average remediation cost in financial services was US$1.59M.

Cybersecurity Ventures estimates that ransomware is set to cost global businesses more than $256 billion by the end of 2031. By that token, organisations need to be extremely mindful of the potential threats they may face. Businesses need to understand the methodologies these hackers use, to address the weaknesses within their domain and take measures to isolate and prevent further ransomware attacks from happening again.

 

The rise of WAMs

According to a recent report by security firm CyberSixgill, 19% of the 3,612 cyberattacks that took place in 2021 were traced back to Wholesale Access Markets – or WAMs for short. WAMs are, in essence, underground internet flea markets. These markets are where aspiring attackers come to purchase network access from threat actors – the individual or entity involved in carrying out the cyber-attack. Types of threat actors include insiders, cybercriminals, rival organisations, or even nation states stealing data.

WAMs sell access to multiple compromised endpoints (or pathways) for around 10-20 dollars. Researchers found that WAMs listed access to approximately 4.3 million compromised endpoints in 2021, which include access to both provider and enterprise software (for example, an organisation’s Slack channel) up to 180 days before the attack itself took place. This shows how long these compromised endpoints remain undetected without proper internal analysis.

 

How can Financial Services stay ahead of the curve?

The use of Artificial Intelligence (AI) and machine learning is undisputed across modern businesses and sectors, and continues to revolutionise processes across the board. AI is a significant player in the financial services industry, building the ‘cyber-wall’ against nefarious users. It gives organisations optimal insights into reducing the likelihood of a ransomware attack in the future.

Namely, AI and machine learning collects and analyses vast amounts of messy (structured and unstructured) data from disparate sources. The challenge for the sector is to understand the volume and variety of the raw data collected from any source to build better protection in the future.

Structured information could be best understood as the clear data we see in a table. For example, the following attendees made a business meeting: first name – Joan, surname – Smith, age – 46. But unstructured information is information presented in a complex manner. For example, ‘there were five people who attended the business meeting, one of whom was forty-six and called Joan Smith’. Naturally, due to the complex nature of the prose, it would be more difficult for a machine to process that data into a digestible format for further risk analysis. This is where AI continues to prove invaluable.

AI uses natural language processing to understand the information provided on the web. As the software continues to evolve, natural language processing reads the information in a way a human would to extract the key information from the text. By incorporating AI and machine learning within an organisation’s IT infrastructure, companies operating within financial services can be better equipped to handle cybercrime.

These tools are flexible and adaptable, they can be configured to analyse different types of data from different sources to curate key insights. This collated information provides a better analysis of the organisation’s exposure, allowing them the opportunity to get upstream in preventing future attacks. This kind of approach is essential to processing listings on WAMs.

The power to analyse data to identify weakness is vital in the battle against cybercrime. It gives organisations a better understanding into what they could expect to see in the future. Hosting the correct data, and with the analytical skills, financial organisations can gain a better understanding of the methodologies and weaknesses in-house that attackers use and exploit to hold them to ransom. Organisations can then use this as a reference to pinpoint compromised endpoints, giving them a chance to reduce access before this route can be exploited and ruin their business.

With cybercrime and ransomware continuing to remain prevalent, it’s vital that financial services companies understand how they can get ahead of the curve and build a robust security platform within their IT infrastructure that can withstand an attack. In 2022, a ransomware attack occurred every 40 seconds. The mindset for the sector needs to be one of when, not if.

Organisations need to be thinking about an attack now – before it’s happened. Pre-planning and preparing for the worst possible outcome from future threats and adversaries. The introduction of AI and machine learning in the fight against cybercrime is a must, and the sooner the industry gets behind in implementing AI, the safer it will be through the next decade.

 

 

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Banking

How Banks Can Boost App Innovation, Speed and Compliance

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By

Steve Barrett, Senior Vice President of International Operations, Delphix 

As new finance and banking applications disrupt the market each day, and customer expectations around speed, privacy and quality continue to grow, financial organization CIOs and DevOps teams have to innovate quickly to bring new apps and updates to market, while remaining strictly compliant to a myriad of regulations. DevOps innovation in financial services requires fast access to accurate, compliant test data, and as anyone who touches the industry knows, data privacy is a highly complex, critical process woven into the everyday world of finance.

Banks and financial services organizations collect vast amounts of data, but using that data for innovation can be challenging due to the vast size and complexity of test data. These challenges can inhibit the adoption of new and transformative technologies and hinder innovation if they are not addressed head on. To address these challenges, many organizations are integrating the use of highly innovative test data management (TDM) tools within their DevOps ecosystems. DevOps TDM provides access and delivery of lightweight, compliant data for DevOps initiatives including digital transformation, software upgrades, cloud migration, artificial intelligence and machine learning (AI/ML), and analytics.

Data – the last automation frontier

Historically, application teams manufactured data for development and testing in a siloed, unstructured fashion. Over time, large IT organizations began consolidating TDM functions to take advantage of innovative tools to create test data. With the rise of modern development methodologies like DevOps and CI/CD that demand fast, iterative release cycles and end-to-end API-driven automation, legacy TDM approaches are often no longer sufficient.

Reliance on a traditionally manual, ticket-driven, request-fulfill model creates time drains during test cycles and slows the pace of application delivery. Consider the payments industry, in which agile technology companies using optimized DevOps processes can release new code hundreds of times per month. In contrast, traditional banks with slow IT ticketing systems may take months to release new features. These manual, legacy TDM approaches exist in contradiction with modern DevOps practices and CI/CD processes that depend on automation and fast feedback to development teams.

TDM for the DevOps Era

DevOps teams rely on TDM to evaluate the performance, functionality and security of applications. However, while processes including storage, compute, and code have all been automated, data has eluded the reach of most DevOps toolchains.

Now, DevOps TDM can help accelerate app releases and increase compliance.by automating the delivery, provisioning, and compliance of data. These practices provide both development and testing teams with data APIs, including the ability to refresh, rewind, bookmark, group, tag, branch, and share test data, to accelerate DevOps productivity and improve application quality. DevOps TDM also includes copying production data, and the masking (anonymization) and virtualization of data through the DevOps pipeline, which helps accelerate app releases and increase compliance.

And as the pace of application development quickens, so does the pace of privacy regulations and efficiently ensuring compliance in DevOps has become a significant challenge for enterprises. Non-production data used for testing software applications, reporting, and analytics can contain up to 80% of an enterprise’s sensitive data. To solve this, DevOps TDM provides integrated data masking to de-identify personally identifiable information (PII) and other sensitive data in non-production environments, eliminating the risk of sensitive data exposure.

The World Quality Report 2022-2023[1] by Capgemini stressed the importance of an enterprise wide approach to test data provisioning (a core component of TDM). The report states, “Over the years, with stringent regulatory and security requirements around data, organizations have increased their focus on provisioning test data safely and securely.”

The report shows that secure test data provisioning remains a challenge, with only 20% of respondents having a fully-implemented enterprise test data provisioning strategy in place to address security and compliance requirements.

Data is the catalyst to innovation

Automation is fueling myriad digital transformations within the financial services sector, but without the right data, these application innovations cannot succeed. DevOps TDM can help further accelerate DevOps initiatives by automatically delivering fresh, complete, and secure test data wherever and whenever it is needed, in minutes. With DevOps TDM, banks and financial institutions can innovate faster, reduce time-to-market for updating legacy applications, and accelerate development and testing of disruptive fintech.

 

[1] Source: https://www.capgemini.com/insights/research-library/world-quality-report-wqr-2022/

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