Account Takeover and New Account Fraud Spike in 2017 By Laura Bruck

Fraud Statistics

New fraud statistics from Javelin Strategy & Research’s Report, 2018 Identity Fraud: Fraud Enters a New Era of Complexity reveal growing consumer sentiment toward financial institutions as identity crimes hit another record-high. This comes despite industry best efforts to mitigate fraud.

In 2017, 16.7 million consumers were victims of identity fraud. This is an 8 percent uptick from the previous year’s record of 15.4 million victims.

These alarming rates were accompanied by $16.8 billion in collective losses — primarily bore by financial institutions.

Despite these back-to-back record-breakers, the industry recently had a few major wins on the fraud front. Many hoped to edge out identity thieves with wide-spread EMV adoption and strengthened data breach notification laws. Yet victimization continues to soar.

Shifting Fraud Trends in 2017

While EMV did successfully mitigate card-present fraud, criminals began shifting their approach to more complex fraud schemes. The latest fraud statistics show growth primarily in account takeover (ATO) and new account fraud (NAF).

Account Takeover Fraud

ATO tripled in 2017, which resulted in $5.1 billion in associated losses. It’s a costly crime that carries an average resolution time of 16 dedicated hours and $290 in out-of-pocket expenses.

Criminals favor ATO due to the wealth of account credentials leaked in major data breaches. Once criminals snatch login credentials, they’ll embark on widespread password testing on popular websites in hopes of scoring a match. Automated bots are often used to speed up the process.

Both financial and secondary accounts, such as mobile or email accounts, are targeted in the attacks. Secondary accounts provide criminals with validity in addition to helping them conceal the crime — as these accounts are often where password change alerts are sent.

New Account Fraud

In 2017, NAF rose an astonishing 70 percent. NAF, often known as identity theft, relies on highly-sensitive personally identifiable information (PII) to facilitate. With the surge in data breaches, this wasn’t hard for criminals to find.

For the first time ever, Social Security numbers were compromised more than credit card numbers in data breaches. This was largely a result of the Equifax data breach, which exposed nearly three in five Americans.

NAF has historically been one of the most difficult types of fraud to resolve. Victims must often work with creditors, financial institutions and government organizations to clear their good name.

It’s an uphill battle that leaves funds tied up for days, weeks or months — something few families can afford.

Effect of Fraud on Accountholder Loyalty

The barrage of data breaches and the rise in sophisticated fraud schemes has caused consumers to lose trust across-the-board. They hold little faith in the companies that collect and store their personal information and are growing equally restless with their financial institutions’ response to the growing incidents.

In a recent interview with NBC News, Javelin’s Al Pascual elaborated on the growing need for action from financial institutions:

“Consumers play a very central role in protecting their own identities,” Pascual said. “It seems like a lot of them feel pretty helpless and they’ve shifted the perceived responsibility for preventing fraud from themselves to other entities, such as their financial institution.”

In today’s competitive climate, this demand is one that few institutions can afford to leave unanswered.

The Fraud Action Plan: 3 Steps

You’ve seen the latest fraud statistics, but now what?

Banks and credit unions need to take a proactive approach to combatting fraud. This includes paying additional focus on ATO and NAF. Here are three key tactics for fortifying defenses and ultimately retaining accountholders:

  1. Invest in Verification
    With the accessibility of PII, identity-based verification through standard security questions is obsolete. Financial institutions must move beyond standard authentication protocols and develop tailored processes for specific environments. Often these will utilize two-factor and AI-based authentication.
  2. Educate and Empower
    Consumers need to be their own best advocates in today’s breach-prone world. Ensure they’re prepared by regularly offering consumer protection tips. Timely educational topics include:

    ● Password safety
    ● Social media privacy
    ● Scam awareness
    ● Account monitoring tips
    ● Data breach response plans

  3. Monitor the Online Black Market
    Become a protector in the eyes of your accountholders while mitigating your own fraud liability through internet monitoring services. These services scan known online black markets for signs an individual’s identity has been compromised. If detected, consumers receive an alert and restoration services are deployed.

These key prevention steps should be used in conjunction with value-added fraud protection offerings. Javelin Strategy & Research cites the need for greater cooperation between financial services and other industries for broader protection against evolving fraud schemes.

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The Rise of AI: How Machine Deep Learning Will Change Businesses Forever By Derek Porter

Intelligent automation has grown exponentially over the last decade, with concepts that previously only existed in science fiction now becoming a reality. Automation software has been introduced in both the workplace and your personal lives, taking on rudimentary tasks and making our lives easier. There remains one aspect of intelligent automation that is still being slowly introduced to industry: the concept of machine deep learning.

What is Machine Deep Learning?

Computer software programs, which include automation software, are programmed to perform set tasks. When information comes into the software, the set perimeters will determine what happens next, which depends on what the software is for. But if an anomaly comes in or if there is a function requested outside of the settings, the automation software will not be able to perform perfectly. Machine deep learning fills in that void.

If a computer had the ability to reason like the human brain, it could process new information and utilize information that it already had to determine what to do next. The software can learn as it goes, remembering previous tasks and implementing new decisions as time goes on.

Impact of Machine Deep Learning on Businesses

Machine deep learning is going to benefit businesses greatly, as it becomes more widespread. Imagine having a robotic process automation that could field customer service chats, but take in more information than just scripted answers and redirecting to specific departments. If the automated software could learn as it went, customer service inquiries could be answered more quickly at any time of day to the benefit of both the business owner and the customer. Automation software can help businesses grow quicker without employee overhead.

Intelligent software is continually impacting how companies operate. For businesses that are just starting or that are aiming to grow and progress, automated software that contains machine deep learning can be the advancement in technology that businesses could have only for dreamed of, helping with tasks that human workers could not undertake while decreasing costs and improving effectiveness.

How Machine Deep Learning Works

When it comes to looking at robotic process automation, it is important to understand that not all automated software has the ability to learn. Deep learning software is specifically made to mimic brain activity, where humans can recognize patterns, sounds, and even images stored within our memory. The created “brain” in deep learning software follows mathematical formulas that mimic brain waves, creating a decision or process that a reasonable human would come up with.

Having a worker who reasons with consistent logic will help businesses across all fields. While having an emotional understanding is important in business as well, if automated software can take on the lesser roles that do not require feelings, a business would have the opportunity to build itself without having to train people. The support abilities can be extremely beneficial to customer who can get answers and help immediately, while helping keeping businesses going at any hour of the day without the need of keeping a constant staff.

Beyond the customer element, deep learning machines can sort through information and documents quicker and with more accuracy than a human worker, taking away the margin of error. This type of technology can look for trends and data with multiple criteria simultaneously, something that is not even conceivable for human workers to accomplish in any timely manner. Deep learning software is the future of business technology and will change how most businesses operate.

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To Protect Against Ransomware Attacks, Look Beyond Antivirus Solutions By Patrick Knight

February was a tough month for the Colorado Department of Transportation, which suffered two back-to-back ransomware infections. After security measures to protect against the first ransomware infection were put in place and recovery was underway, a new ransomware variant was able to penetrate defenses and re-infect systems in the environment.

This was just the latest among numerous reports of ransomware or other malware infections reported in recent months. These attacks affected hospitals, healthcare organizations, and state and local government agencies and resulted in enormous expenses to restore the data.

The Colorado Governor’s Office of Information Technology (OIT) is working with the FBI and other security agencies to identify how the ransomware entered the DOT network and has begun restoring systems from backup. OIT also indicated that no ransoms would be paid.

To pay or not to pay—that is a vexed question. While paying the ransom of a security breach is generally discouraged by security professionals and government agencies who respond and advise on cyber security, the decision can be a difficult one. It involves many factors including the time and cost to restore systems, as well as the state of the organization’s backup and the value of the data that stands to be lost.

Recent research by online security company McAfee reports that up to 30% of ransomware cases they examined used either “fake or nonexistent” contact information for infected victims to send ransom payments to. Other reports also suggest that recovery keys were not provided even after a ransom payment was made. This should cause everyone to wonder whether paying a ransom after a security breach does any good at all.

The number of public ransomware reports suggests that costly ransomware infections still penetrate defenses and that an effective response plan must include data backups of all critical systems.

Anti-virus solutions alone are insufficient to prevent costly infections

In a 2017 Ransomware Report by CyberSecurity Insiders, 74% of respondents relied on data backup and recovery as the most effective response to a ransomware infection. This, despite the fact that nearly the same majority (73%) reportedly relies on anti-virus or other types of endpoint security product to prevent a ransomware infection.

Many agencies won’t comment publicly about the security posture in their organizations or specify which antivirus solution they use to prevent malware threats such as ransomware. This is understandable from an operations security perspective. However, the number of public ransomware reports suggests that costly ransomware infections still penetrate defenses and that an effective response plan must include data backups of all critical systems.

After public reports of a malware infection in its environment, one organization in the healthcare industry, which initially declined to comment about their security defenses, did eventually respond, “Of course we were running antivirus.”

The Colorado Governor’s Office of Information Technology confirmed that antivirus technology was deployed in the CDOT environment as well. Clearly relying on antivirus alone is insufficient in today’s threat landscape to adequately protect against new ransomware and other malware variants. The CDOT and many other organizations utilize data backups along with other security technologies to protect against these threats. The best way to recover is with a data backup and restoration plan. Refusing to pay a costly ransom arguably deters future attacks.

What are you doing to protect your organization from ransomware attacks?

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Before Robots Can Outsmart Humans, the Tech Industry Has a Lot of Work to Do By Chad Steelberg

Technology that can cure cancer is a feasible and realistic outcome, but we’re not there yet. Here’s what needs to happen to move the needle on artificial intelligence.

Artificial intelligence is being touted as a panacea for everything from curing cancer to preventing crime. However, to achieve such goals, the A.I. industry must first focus on the smaller, more practical solutions before it takes on bigger challenges–a process that involves working through countless iterations.

The road to autonomous cars began with the invention of the wheel. It continued with the development of the horse-drawn carriage, the creation of the car, and hundreds of thousands of subsequent automotive advancements that spanned over a period of 133 years. It took this many years and many more models to create today’s connected cars with their sophisticated telematics and advanced driver assistance systems. Despite the promise of materializing in the foreseeable future, fully-autonomous cars will require many more iterations until they become a reality.

For A.I. innovation to be practical, companies have to first embrace the low-hanging fruit, which is, providing solutions that relieve people from mundane and repetitive tasks, while increasing productivity and return on investment. A.I. can be easily applied to tedious tasks such as sales-flow automation and advertising airchecks. It is for these reasons that change-management leaders like Deloitte are encouraging businesses to “start with the boring.”

These solutions are called “one-time” (1x) AI transformations. They represent pragmatic tools that satisfy immediate needs while promoting strategic objectives.

The importance of these transformations cannot be underestimated. Studies show that businesses that do not integrate such 1x AI into their operations will fall behind in productivity and overall competitiveness. In contrast, businesses that invest in such practical A.I. solutions are expected to boost employment by 10 percent and revenues by 38 percent during the next five years, according to a recent report from Accenture.

Two-fold (2x) AI transformations take things a step further by looking at the bigger picture.

In litigation, 2x AI can take terabytes of unstructured and structured data from hundreds of thousands of sources and quickly extract, review, and analyze it with great accuracy. Lawyers can gain insights, uncover trends, and better predict the outcome of litigation.

In broadcasting, networks can leverage 2x AI to evaluate how the content of their news segments compares to their competitors’. A network also can learn which topics, and even expressions, resonate best with its audience, enabling them to optimize programming accordingly.

The type of A.I. used in all these examples is called artificial narrow intelligence (ANI)–it pertains to solutions that target a specific task, executing it better than humans, thus, augmenting human tasks and capabilities.

But this is just the tip of the iceberg in terms of what A.I. can offer. Things get truly exciting when organizations engage in 10-times (10x) AI transformations.

The key to such transformations lies in two technologies: artificial general intelligence (AGI) and artificial superintelligence (ASI). AGI is defined as a machine that can perform any intellectual task as well as a human does. Artificial superintelligence goes beyond AGI by delivering machines with intellectual capabilities that are superior to humans’.

Attaining this level of A.I. not only requires patience but a re-thinking of the nature of the technology and a complete reshaping of business practices.

Using 10x AI, legal systems will be able to find case law to ensure the criminals are tried justly, reducing wrongful conviction errors to a minimum, if not eliminating them entirely. Lawyers will be able to know which specific evidence and precedence they should use to optimize their chances of prevailing in a lawsuit and bringing justice to light.

In medicine, 2x image detection enables detection of cancerous growths at their very initial stages. 10x AI-based medicine will be truly preventative through cross-referencing many data sources ranging from personal data, such as DNA to external environmental factors, such as pollution.

In retail, 10x AI will enable brands to offer customized and optimized shopping experiences that will convert to the highest degree. Information, such as customers’ faces, moods, geo-location in the store or area, purchasing history up to the specific item and price point, the likelihood of purchasing certain items at specific moments, discount or loyalty programs that can be triggered, and more, will all be cross-referenced to achieve the most refined resolutions into customer behavior.

The list of ways that 10x transforms business processes goes on and on in every field imaginable.

True innovation happens when businesses re-think and re-frame needs, disrupt industries, and inspire. However, reaching the 10x summit requires a long and incremental climb. To get to the top of the A.I. mountain, technology providers must undergo innumerable iterations–from 1x to 10x-to address and solve business and technology challenges.

This article originally appeared on Inc.

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A Step Ahead: Connecting Brands and Individuals In The Digital Age By Justin Thomas-Copeland

Before the internet, companies built their brands through a top-down approach, with consumers operating as recipients rather than participants. Today’s digital age has reversed that paradigm. Consumers now hold the remote, and when brands ignore consumer needs or just shout louder, audiences flip the channel.

Take, for example, the demise of Nokia. Ten years ago, Nokia was a leader in the mobile phone space — an innovative company with a lot of brand love and a strong stock presence. But Nokia failed to anticipate consumer demands and adapt the way Apple and Samsung have, and by the time Nokia recognized this shortcoming and attempted to change, consumers had moved on.

While data-driven personalization through customer relationship management may have once been an optional bonus for brands, it’s now the marketplace norm. New technology, data, and connected analytics mean that consumers expect personalized advertising, content, and experiences across multiple channels. And the term customer managed relationship (CMR) itself means that customers hold the remote and navigate how and when they opt-in to these messages and experiences.

Still, changing marketing and investment models to maintain authentic engagement and relevancy isn’t easy. But marketers who attend to data and consumer feedback can help brands develop successful strategies and fortify their positions in the marketplace.

Why Digital Builds a Stronger Brand

To sustain relevance like successful CMR businesses Amazon, Google, and Airbnb, we have to treat consumers as individuals by recognizing previous interactions, sales, pain points, and moments of delight, and by inviting them to test new innovations. They derive value from the “me-conomy,” giving individuals a seat at the brand table.

For instance, to maintain relevance and share of the urban clothing marketplace, adidas took a unique approach to brand ambassadors. It gave up creative control in its partnership with Rita Ora. She designed the collection based on what she would wear — simple but authentic apparel in a way no other adidas design could duplicate. This allowed the company to reach a previously untapped consumer base and deepen its consumer engagement. To push the initiative as much as possible, adidas then ran online marketing campaigns and transformed Ora’s endorsement into a full brand experience using her voice. Audiences responded, seeing themselves reflected in these new designs and campaigns.

In today’s digital age, there is a real need for brands to reinvest in consumers, with the proliferation of technology, the ability to merge data sets in real time, and the ability to develop and serve content and experience while driving commerce. While no company can keep up with every technological advancement, brands should embrace a digital-first DNA to even begin to try.

How to Innovate With Digital DNA

How a brand behaves, where it activates, how it surrenders control to its consumers, how it fights to understand the individual, and how it redefines data points are all traits of a modern brand DNA that can help organizations reflect on their innovative goals and lead them to the next successful strategy.

For example, even though Under Armour was founded on its synthetic, quick-drying undershirt for athletes, the company didn’t stop innovation and development with that single product. It capitalized on its initial success and expanded by providing app-based services, investing in and acquiring digital companies like MyFitnessPal, and tracking and analyzing consumer behavior to grow its user base to 165 million people — a number that continues to climb.

Brands wanting to remain viable and innovative in this digital age, then, should likewise identify and build their platforms around their own digital DNA and the principles of CMR: data, analytics, changing behavior, and engagement over time, which gives birth to deeper connected engagement between brands and consumers.

To deliver on this, marketers and leaders can follow these steps:

1. Unlearn the art of marketing to meet higher expectations. Stepping outside of your industry to become a student of business and marketing across categories can help you examine how other brands are campaigning, activating, and reimagining themselves to sell core and adjacent products and services. This outside-in examination also helps you recognize the control consumers have (and exert) over their buying options.

Consumers expect the same level of service at a car dealership as they would with Apple, wanting personalization, simple choices and ease of use. Once you understand this, you can stop making excuses about how your brand hasn’t caught up to consumer standards and instead become the market leader by using your digital DNA to establish a larger consumer base.

2. Partner smarter and then put your people first. Many brands can’t build custom digital systems and can’t afford to hire a digital team, so consider partnering with another company that specializes in marketing technologies. Hundreds of new and innovative companies are eager to help clients win the digital war.

With your partner onboard, let your employees flourish by encouraging them to use and explore these platforms and technologies, even if it means they procrastinate on their daily operations or, ultimately, fail in their attempts. Giving your people room to experiment and think can stimulate creativity while reassuring them that they are still essential to the organization.

3. Act quickly in the marketplace, but listen closely to consumers. While it may be tempting to test every technology platform or digital approach before deciding on the best course of action for your brand, avoid that propensity. It can stall any momentum you have. Instead, act quickly but moderately. Launch your new products, partnerships, and services in small doses while listening to consumers and allowing their feedback to help guide your decisions.

What are people saying about your brand? About your competitors? About the market in general? Stay informed about what’s happening in your industry and beyond to identify where new technologies are disrupting the status quo and to prepare to meet new expectations. Empowering customers to provide feedback — especially negative feedback — directly to you or through open and public feedback channels can help you measure, learn, and iterate on new solutions that allow your brand to scale as it grows.

As companies seem to rise and fall faster than ever, brands can fortify themselves in the marketplace by honing their digital DNA and following a sequence of studying, implementing, and executing. The digital age is upon us. While today’s solution may be tomorrow’s problem, the only brands that will be able to keep up with dynamic consumer trends are those that learn to embrace (and then re-embrace) this connected world.

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The Biggest Factor Accelerating the Human Innovation Curve Right Now By Chad Steelberg

For the first time in history, the invention process is not entirely dependent on human intellect.

Throughout the ages, human innovation has been accelerating at a mind-boggling rate. Consider that 1 million years elapsed between the control of fire and the invention of the wheel, but just 5,400 more years until the creation of the Gutenberg press–and a mere 455 additional years before the development of the light bulb.

When plotted on a chart to illustrate the human innovation curve, it is clear that there is only one word that can describe the increase in the pace of progress: exponential. However, the innovation curve now is shifting into even higher gear with the proliferation of artificial intelligence (AI).

AI grew exponentially in 2017, with no signs of stopping in 2018. AI fundamentally changes the equation of innovation, adding a new variable that dramatically accelerates the rate of advancement. For the first time in history, the invention process is not entirely dependent on human intellect. Machines are now augmenting and will eventually supplant human brainpower.

Although AI is still in its early stages, the arrival of a new approach –called “conducted learning”–will expedite the rate of overcoming current limitations and is set to affect the speed of both AI and the human innovation curve.

Narrow-minded AI

AI algorithms now are reaching, and even exceeding, human capabilities in areas such as strategic game playing and image classification. However, these algorithms fall under the category of artificial narrow intelligence (ANI) since they are limited to excelling in narrowly defined tasks.

We can train an AI algorithm to recognize the shape of a gun, for example, and it will be able to detect the image faster and better than humans. However, due to this narrowness limitation, in a real-world application, such as in a TSA scanning, this effective scanning method will be restricted only to the specific gun models on which the algorithm was trained.

Consequently, we still have a way to go until we reach artificial general intelligence (AGI), which will be more akin to humans and present capabilities similar to what we see in sci-fi movies.

The maestro of AI

Conducted Learning presents a promising solution towards achieving AGI by leveraging the combined power of separate ANI engines. Conducted learning enables running several cognitive engines in concert, picking the best engine or engines to perform the task, similarly to an orchestral performance. This results in a more accurate outcome than what can be obtained from any single network, while cutting down on computational costs and speed.

Like other deep learning models, conducted learning initially formats data, preprocesses it, and generates input. The magic happens during the next stage, when the technology acts like the orchestra’s conductor, instructing each cognitive engine when to play its part in the complete composition.

The conducted learning model extracts the accurate parts of the output and recycles the remnant through a process of transformation and rerouting to the relevant engine or engines. By using multiple cognitive engines simultaneously, the algorithm continually learns and improves its capabilities, building a more effective topography to complete the task. This dramatically improves the accuracy and performance.

Class consciousness

Conducted learning facilitates overcoming the limitations of narrowness through two methods: intra-class learning and interclass learning. Intra-class learning uses multiple cognitive engines in the same class (e.g. translation). Interclass learning employs several cognitive engines across different classes (e.g. translation and facial recognition).

Taking transcription as an example, with intra-class learning, conducted learning enables the transcription of a soccer match of the English League by first transcribing the words in the English language. It then fills in the gaps of running a transcription engine that is trained on sports terms. The next engine will cover words pronounced in a heavy British accent to catch unclear words that weren’t detected in high confidence, etc. All this is done in milliseconds.

With inter-class learning, if the auditory speech recognition engines cannot catch the accurate transcription of the names of the players, then visually trained engines, which use face recognition to match the player’s face or “read” their names off their t-shirts, will be activated to accurately complete the task.

Bending the curve

The implications of conducted learning go beyond achieving greater accuracy. It is a big leap for machines, but even more importantly, for AI’s ability to teach itself by one engine or class of engines informing the other about the data and completing the task.

The human innovation curve has now changed with the addition of AI to the calculation. AI will become the dominant factor, dramatically outpacing humans’ capability to invent on their own.

This article originally appeared on

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Could PayPal Make it Easier (and Faster) to Manage Blockchain Payments? By Miranda Marquit

In recent years, there has been a lot of hype surrounding cryptocurrencies and blockchain applications for business. Blockchain payments have been of special interest in many circles.

However, the reality is that all things blockchain aren’t perfect. New platforms are coming out regularly, but eventually, they have the potential to run into problems — especially when it comes to the time it can take to complete cryptocurrency transactions.

PayPal hopes to solve some of these programs with technology detailed in a recent patent filing.

The Speed Problem with Blockchain Payments

Blockchain has been hailed as a great transformative technology. However, the nature of blockchain is such that many transactions need to wait for a block to be added to the end of a chain on a network.

The result has been a real problem for Bitcoin. Transactions using Bitcoin payments can take more than 100 minutes to complete.

For blockchain payments to gain wider acceptance, they need to go through faster. This is what PayPal aims to accomplish.

PayPal’s Blockchain Patent

PayPal’s blockchain payments idea revolves around creating secondary wallets. In order to transact using cryptocurrencies, you need your own wallet. Parties exchange wallet information to complete transactions.

With PayPal’s system, a secondary wallet has unique private keys. The system is set up to transfer these keys with an exact cryptocurrency amount. Here is how PayPal explains it in the patent filing:

The systems and methods of the present disclosure practically eliminate the amount of time the payee must wait to be sure they will receive a virtual currency payment in a virtual currency transaction by transferring to the payee private keys that are included in the virtual currency wallets that are associated with predefined amounts of virtual currency that equal a payment amount identified in the virtual currency transaction.

This reduces the need to wait for a transaction to be included in a block at the end of the chain. Instead, the amounts involved can be verified almost immediately.

Making Blockchain Payments More Practical and Accessible

PayPal has been processing cryptocurrency payments for several years now. In fact, if you are a business owner, PayPal can help you manage your payment types, including cryptocurrencies.

If PayPal implements this system, it could further increase the accessibility of blockchain for business owners and their customers. The hope is also that this might bring down some costs. Right now, Bitcoin has become increasingly expensive. Many business owners balk at using Bitcoin for regular transactions and payroll.

One of the best things you can do as a business owner is to accept several payment varieties. The more types of payment you accept, the more customers you appeal to. There’s a growing number of consumers interested in using cryptocurrencies rather than government-issued money. By catering to them, you can reach a wider consumer base.

As the future of currency and payment continues, keep blockchain payments in mind. Accept credit cards. Accept other forms of payment. But don’t leave out cryptocurrencies. They’re part of the future of business.

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