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AI and Cryptocurrency – How They Can Work Together Effectively

AI

AI and Cryptocurrency – How They Can Work Together Effectively

There will soon come a time when artificial intelligence will be running on top of cryptocurrency systems like Blockchains with its capability to increase machine learning capacity and create new financial products. It will take the technology leaps and bounds further in making it one of the mainstream emerging technologies.

According to the research being conducted about the future of AI, the market is estimated to grow to a whopping $190 billion worth of industry by 2025. Considering how much the market is expected to grow, Blockchain and AI convergence are inevitable.

Both the emerging technologies have been around for a decade now and deal with data and value. Where Blockchain enables a secure storage and sharing path of data, AI analyzes and generates significant insights from data to create value.

Having such similarities, there is no doubt that both the technological realms can be merged to create a more advanced and efficient machine learning blockchain system to benefit the masses. Let’s have a look at how Blockchain and AI are a perfect match.

How Blockchain and AI Is the Perfect Match

The following are some key pointers and examples that evidently showcase how combining Blockchain and AI is a consequent step forward in the right direction for increased efficiency and profitability.

Blockchain connecting with the AI basics

Firstly, it is essential to know that most of the hype surrounding startups integrating Blockchain with artificial intelligence is exactly just that, hype. Such companies are far too young and inexperienced in the industry to be talking about a big game. With few clients and less commercialization, it is understandably not possible to carry out such advance convergence.

The majority of such companies have raised money through the initial coin offering or the ICO. This means that the solutions they offer are as thoroughly evaluated as they would have been had the company raised a significant amount of venture capital money.

However, it is quite so possible that these companies may become successful in the future, but until then, they just create useless hype about the advancements in this technology.

Many people limit the usage of Blockchain technology and associate it with just cryptocurrency transactions. As a digital ledger that can record economic transactions, Blockchain can be expanded to virtually record almost anything of value.

There can be both public and private blockchains. Where the public ones are open to the public, the private or ‘Permissioned’ blockchains are restricted for usage by ‘invitation-only’ and mainly used in the corporate environment. This also makes them faster than public forums as the users are mainly trusted and verified personnel making the transactions verifiable faster.

One of Blockchain’s more important features is that it allows even the unrelated parties to carry out a transaction and share data through a mutual ledger. As cryptography validates the transactions, it makes it more efficient for participants not to rely on third-party evaluators to carry out a transaction. Deploying cryptography ensures that data transactions are secure, incorruptible, and irreversible once recorded.

Artificial Intelligence is not a term making rounds for a decade now. It very much comprises of every new technology that has near-human intelligence to carry out a task. AI models are used to assess, understand, classify, and predict using relevant data sets. Machine learning then cleanses the data as it gathers insights creating better useful data sets for use.

As it is evident, data is the central component to AI and Blockchain that allows a secure and collaborative effort towards data sharing. Both Blockchain and AI ensure the trustworthiness of data and extract valuable insights from it.

How Microsoft is Improving Machine Learning for Blockchain

According to the research conducted at Microsoft, the company is working on finding out ways to design efficient collaborative machine learning models hosted on public blockchains. The incentive behind this effort is to make AI decentralized and a more collaborative forum using Blockchain.

While there is no doubt that advances are being made in machine learning, the benefits that are being created as the results of these efforts are not as openly available. The masses have limited resources and cannot always access cutting-edge technology such as machine learning systems.

Such systems are highly centralized and used as the proprietary datasets. Not only are they costly to recreate, but even the best models can become outdated if not consistently refreshed with new data.

The idea is to allow advanced AI models and bigger datasets to be easily accessible, sharable, updated and retrained to increase the adoption, acceptance, and overall effectiveness of AI. People will soon be able to adopt this easy and cost-effective method to run and access advanced machine learning models through regular devices such as laptops and smartphone browsers and collectively participate in improving data sets and models.

Therefore, Microsoft is keen on developing what they call a Decentralized & Collaborative AI on the Blockchain framework. It will significantly increase AI community collaborations to retrain such models with valuable datasets on public blockchains. The machine learning models would be made free for public use as they would know the code they are interacting with.

Some applications that Microsoft is looking forward to integrating are virtual assistants and recommender systems like used by Netflix to recommend shows to its audience. Considering such models, Blockchain makes sense because of the increased security and how trustworthy it is for the participants.

The well-established nature of the blockchain system and the associate smart contracts ensure that the models will always perform up to the specific requirement. As the models are consistently updated on the Blockchain used unhinged by the user’s local device, every user gets to see the one genuine version of the model.

Hence, even though Microsoft’s framework isn’t favorable for operating at large scale for now, but sooner or later, it will be the norm. There is little to no doubt that organizations like Microsoft are doing advanced research and practical work to converge AI and cryptosystems like Blockchain. There is no doubt that cryptocurrency is the future of money. So it is in the best interests of the organization to start working on merging Blockchain and AI for improved benefits.

How can an organization merge Blockchain and AI?

Just as Microsoft, more advancement is made for combining Blockchain and AI for fulfilling specific usage requirements. Such cases will depend on the company’s specific needs, but the core preference would be related to data. This will allow companies to improve their digital and data capabilities by developing a combination of AI and Blockchain solution to fit their operations.

The very first step needs to be taken by the executives to identify the specific business needs and whether creating an AI and Blockchain system would address that need. This can become easier if the organization has already worked on AI and taken initiatives in other operations because now you can integrate Blockchain to improve them.

Similarly, if the company owns valuable data, they can monetize by converging a blockchain environment and sharing the data with AI model creators. For instance, a progressive car company like Tesla probably has a good collection of valuable data collected by its cars. They can put it on a blockchain system as their self-driving cars will continue to collect huge amounts of data that they can use to improve the neural networks powering self-driving operations and functions.

With a trusted name as Tesla, the public would not be too complacent about maintaining their privacy. Blockchain would allow the company to make the driver information anonymous to ensure privacy while collecting data to improve neural nets in use.

The company can even share anonymous data with car insurance companies. It would allow the insurers to price their insurance packages for self-driving cars more efficiently and with an educated mind, given how the risk profile of a self-driving car is different from that of a regular car.

The whole packaged win-win situation here is that where the company would improve its cars, the public would get advanced transportation, complete privacy, and the right insurance for the right price without getting exploited.

Using Digital Investment Assets for Trading through Blockchain

You must be already aware of how Blockchain is already a ready-made, and good-to-go digital ledger used to store and trade financial instruments such as cryptocurrencies and cryptographic tokens. However, Blockchain is still a nascent technology, been only around for a few years. Where cryptocurrency has definitely taken the world by storm, cryptographic tokens are comparatively more nascent.

Hence, it is evident that there is no probable activity and enough data yet to apply AI to financial products like a cryptocurrency that are traded through Blockchain. However, the upgrading technology and data sets show a promising future for AI taking insights from these data sets to create financial products and trade them autonomously.

How can an organization merge Blockchain and AI?

The convergence of artificial intelligence and Blockchain would be a huge step forward, and the process will cover four distinct yet inter-linked stages.

Stage I: Proof of concepts

Stage II: Asset tokenization

Stage III: Digital Investment Assets DIA

Stage IV: AI agents trading DIA

The four stages will represent how Blockchain is proof of concepts initially. On the second stage, assets are tokenized and traded. Tokens can represent underlying security methods, physical assets, cash flows, and utilities. This reduces the alleged transaction cost and decreases the time taken for settlement to improve audit accountability.

AI and Blockchain Applications

There is no denying that a decade back if someone would have presented us with an idea of magical internet money called crypto in the future, we would have laughed and made fun of the person for coming up with Superman and Kryptonite theories. Fast forward to ten years down the line, and cryptocurrency not only exists, but there are real-world integrations of its blockchain system with AI.

Smart computing power

Think of a machine learning code that would upgrade and retrain when given the right data. That is exactly what AI affords the users to tackle tasks more efficiently and intelligently.

Diverse data sets

The combination of Blockchain and AI can create smarter and decentralized networks to host various data sets. Creating a blockchain API would enable the intercommunication of AI agents resulting in diverse codes and algorithms to be built upon diver data sets, ensuring development.

Data protection

It doesn’t matter if data is medical or financial. Certain data types are too sensitive to be handled by a single company and their coding system. Storing such data on a blockchain and accessed through AI would give its users a huge advantage of personalized recommendations, suggestions, and notifications while securely storing data.

Data monetization

Data monetization would make both AI and advance Blockchain easily accessible to smaller companies. As of now, developing and growing AI is costly for organizations, especially those who do not own data sets. A decentralized market would create space such companies for which it is otherwise too expensive.

Trusting AI for decision making

AI is growing smarter with time. Through the use of blockchain systems like crypto, transactions will become smarter, making the process easier to audit.

Conclusion

All in all, the collaborative effort of blockchain technology and AI is still majorly an undiscovered territory. One of the main reasons why we still have yet to see a commercialized joint adoption of the Blockchain system and artificial intelligence is that the upscale implementation of their convergence is quite challenging.

Many businesses, although having ventured on with AI, are skeptical when it comes to conjoining Blockchain. They are in their early stages for testing the waters for AI and Blockchain coming together in isolation. As they continue to figure it out for appropriate public distribution, the convergence of the two technologies has had its fair share of scholarly attention as well. Yet still, projects solely developed to promote the groundbreaking match are still primarily not catered to.

There is no doubt that the potential of this combination is clearly there and developing, but how it will play out for future public use can be anybody’s call.

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Claudia Jeffrey is currently working as a Junior Finance advisor at Crowd Writer, an excellent platform to get assignment help UK. She is a self-proclaimed crypto-influencer. She has gained significant expertise and knowledge in this regard over the years and likes to share it with an interested audience.

risk

Why COVID-19 is a Galvanizing Moment for Eliminating Physical and Digital Supply Chain Risk

When the COVID-19 pandemic began, the resulting economic fallout was felt across borders and industry alike. From manufacturing to financial services, every industry has been scrambling to minimize the impact of the pandemic on the bottom line. For many businesses, this has helped serve as an urgent wake-up call to take proactive steps to identify and eliminate risk across their global supply chains, which typically span several tiers of suppliers dispersed across the world. Real-time supply chain risk visibility plays a critical role in avoiding business disruptions.

The Economic Risk

There is an immense economic risk that needs to be considered when a business operates a global supply chain. At the start of the pandemic, we witnessed the inevitable ripple effects across not just multiple industries but also across multiple different tiers of suppliers. For example, 3.74% of sub-tier suppliers in the Department of Defense’s ecosystem closed as a result of the pandemic. 75% of small businesses have reported that they have only enough cash in hand for 2 months or less. As suppliers struggle or go out of business, significant supply chain disruptions are common.

This instability coupled with the multitude of other economic crises facing the world, such as ongoing trade friction with China, could precipitate a fundamental collapse of global business as we know it. We must monitor our supply chains for more points of exposure to risks than ever before.

The Data Security Risk

With computer hacking having increased 330% since the start of the pandemic, global businesses also need to account for the cybersecurity risks involved with having a supply chain across multiple countries and potentially hundreds or thousands of suppliers. The data systems of global suppliers are a potential entry point to a brand’s or government agency’s data systems, presenting a major challenge across the global supply chain. Organizations must be able to assess and continuously monitor the strength of supplier data security measures and the changing cybersecurity-related risk associated with their suppliers.

Even after the pandemic subsides, the need for real-time risk monitoring in the extended digital supply chain will persist, especially as cybersecurity attacks grow in sophistication.

New Technology for Physical and Digital Supply Chain Risk Management

When it comes to monitoring risk associated with multiple tiers of suppliers, the majority of businesses are still way behind. According to Gartner, only 27% of companies perform ongoing third-party monitoring and only 2% directly monitor their 4th and 5th party suppliers. Although companies know they’re vulnerable to disruption by a sub-tier supplier, not enough are being directed or given the tools to actively monitor them effectively.

Historically, the majority of businesses attempt to identify, assess and manage supply chain risk manually and only periodically. This is because, previously, automation technology focused on making sense of large amounts of extended supply chain ecosystem data has not been up to the task. Much has changed. The global machine learning market was valued at just $1.58B in 2017 and is now expected to reach $20.83B in 2024, growing at a CAGR of 44.06%. New AI and machine learning-based technology is emerging rapidly and changing the game. This new technology can immediately illuminate risks across all tiers of a global supply chain because data on tens of millions of suppliers is continuously monitored from both a physical and digital supply chain perspective and across numerous risk factors.

Incorporating AI-powered solutions into your supply chain risk management strategy can automate the identification of risks that exist deep within a supply chain. In addition, adopting this technology ensures that an organization has continuous, real-time information to inform ongoing risk management efforts and identify problems before they threaten the business.

There is no way to know when the pandemic and its resulting implications will cease. Or when and where the next global event will happen. Looking ahead, successful businesses will be ready to continue functioning in a safe and secure way regardless of what issues they face. Supply chain-related blind spots and resulting disruptions can pose major complications for organizations that aren’t able to effectively identify and map risk. COVID-19 has driven a greater sense of urgency to shore up these problems. New technology for automated, continuous monitoring of supply chains end-to-end presents a new path toward operational resilience, business continuity, and overall health.

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Jennifer Bisceglie is the CEO of Interos, the first and only business relationship intelligence platform to protect enterprise ecosystems from financial, operations, governance, geographic, and cyber risk in every tier of enterprise supply chains, continuously.

vehicles

How Artificial Intelligence is Driving the Memory Market for Autonomous and Connected Vehicles

One of the important technologies that have emerged over the past few is that of artificial intelligence (AI). The technology is being utilized in various industries for making processes and operations simpler. Just like other industries, AI is also being widely utilized in the automotive industry for making vehicles safer and more secure. The technology is being utilized in infotainment systems that are now serving as personal assistants, aiding the driver by offering efficient navigational support, and responding to voice commands. This increasing utilization of AI is creating wide data storage capacity.

Autonomous and connected cars are generating large amounts of data, since they are extensively making use of electronic functions for providing greater efficiency, greater safety, driver assist capabilities, richer telemetric and entertainment functions, and communication between local networks and vehicles. Owing to these factors, the global memory market for autonomous and connected vehicles generated a revenue of $4,310.8 million in 2019 and is predicted to advance at a 23.9% CAGR during the forecast period (2020–2030), as per a report by P&S Intelligence. The major applications of the memory market in the automotive industry are telematics, navigation, and infotainment.

Out of these, the largest amount of data was generated by navigation features in the past, which can majorly be attributed to the surging adoption of these systems in vehicles. Navigation systems generate data related to alternative routes, shortest route, and traffic or checkpoints on the road, and need efficient storage mechanism. Apart from this, the telematics application is also predicted to make create demand for data storage capacity in the coming years, which is particularly because of the increasing preference for autonomous and connected vehicles. The system captures data via sensors, radars, and cameras.

Different types of memories in the automotive industry are NOT-AND (NAND) flash, dynamic random-access memory (DRAM), and static random-access memory (SRAM). Among all these, the demand for DRAM has been the highest up till now, owing to their effective storage of data and relatively low cost. Both commercial and passenger vehicles generate data, thereby creating a need for memory; however, the largest demand for memory was created by passenger cars in the past. This is because of the fact that passenger vehicles are produced more than commercial vehicles. Furthermore, new technologies are first implemented in passenger vehicles for testing purposes in the automotive industry.

In the past, North America emerged as the largest memory market for autonomous and connected vehicles, and the situation is predicted to be the same in the coming years as well. This can be ascribed to the presence of a large number of automotive technology companies and increasing sales of connected and autonomous vehicles in the region. Moreover, the disposable income in people in North America is high as well, owing to which, they are able to spend more on luxury vehicles that are equipped with advanced, connectivity, safety, and autonomous features.

Hence, the demand for memory in autonomous and connected vehicles is growing due to the increasing demand for safety features in vehicles.

Source: P&S Intelligence

customer service AI

How Is Customer Service AI Improving Work for Employees?

Customer service is an area that always needs attention and often needs improvement. No matter how strong your systems and your personnel, smart organizations are looking for a competitive edge in this field. Therefore, the work your employees perform in the customer service department is a critical focus for any successful business.

With that in mind, we wanted to take a closer look at how Customer Service AI is making some significant improvements in this area. By heeding the advice and explanations we give you here, your employees will be able to provide a more thorough and effective service to your customers. In turn, the number of satisfied customers will increase significantly, and you won’t have to emphasize the search for new customers anymore.

What’s more, the overall loyalty to your brand will increase, as well as the reputation your brand has among consumers and competitors.

Ways AI Is Improving Customer Service Work

First of all, it’s important to understand that AI is not about replacing your employees in any way. When you deploy Customer Service AI solutions to your customer service sector smartly and efficiently, your employees gain the support they need to perform their jobs much better than they ever could before. That’s precisely where the main benefit of AI lies – in human-AI collaboration.

The most obvious example of this is the use of chatbots in customer service. AI-powered chatbots are now capable of performing many tasks when it comes to the relationship between your company and your customers. They can handle specific repetitive tasks and even resolve simpler issues your customers have. By doing that, your employees are left to work on more complex issues, without having to waste time giving the same answers and dealing with the same problems that tend to repeat themselves within most companies.

What’s more, AI-powered chatbots are available 24/7, so you don’t have to worry about overstretching your employees through several shifts or hiring more people to handle more demands. AI chatbots become the frontline of your customer service, providing the answers to the questions your employees don’t have to worry about anymore. Beyond chatbots, AI can also ensure the organization within the customer service department is at its most efficient and that no unnecessary errors occur.

Chatbots will know when complex issues arise and will seamlessly transfer those requests to human employees who will handle the problem more effectively. This becomes a symbiosis when quality solutions are implemented, and the customer never notices the transition.

As you can already assume, all of this improves the overall satisfaction of your customers, as they no longer have to wait hours for a dedicated agent to give them a response.

The Bottom Line

In essence, AI is not just improving the customer service industry and the work employees do there, it’s revolutionizing it. If you want to be part of that revolution, your organization needs to seriously consider implementing a quality AI-driven service desk that will completely alter the work your employees perform and the service your customers receive.

Aisera offers that kind of solution, and you can test it out to see how it works right now by requesting a personalized demo from us.

amazon's

What Logistics and Warehouse Businesses Should Learn From Amazon’s Mistakes During the Pandemic

Amazon has dominated the COVID-19 news because of its ability to get some medical supplies and the reliance of people on ecommerce to protect them as they shop. It’s been a good time for the company’s financials, with significant increases in sales and secure positioning for its other services.

Unfortunately for Amazon, it was also in the news because of product mishaps, fulfillment concerns, worker illnesses, and poor handling of concerns. What the brand did, and didn’t do, can be a useful guide for smaller warehouse and logistics companies to follow.

The best lessons are from Amazon’s mistakes because few 3PLs and service companies are big enough to survive similar mishaps.

Take care of your partners

Amazon faced a tough situation, just like all of us. We all got some things wrong. The hope is that they won’t turn into long-standing issues. For Amazon, it’s unclear if that’s the case, but the thing with the most significant potential for prolonged harm is how it communicated and worked with its partners.

The biggest misstep from a partner standpoint would be when it announced a halt to accepting shipments from some third-party sellers and gave little guidance on what this meant. Sellers flooded Amazon’s forums to ask questions, and rumors spread just as fast as valid answers. People were upset, scared for their businesses, and frustrated that Amazon might not be a viable marketplace in the future.

While Amazon did eventually move back to allowing all third-party shipments for its FBA program, some harm has been done. Companies are looking at moving to do their own fulfillment — which was rewarded by the Amazon AI at some points during the pandemic — to prevent any future move from Amazon bringing an entire small business to a halt.

Amazon may be trying to tackle some of that relationship harm with efforts like waiving some storage fees or supporting more fulfillment operations. Still, it’s unclear how much harm happened.

Diversify and simplify when you can

An estimated one-third of top Amazon sellers are in China. It is believed to source some of its own products from China, and many of its smaller sellers also get products or drop-ship directly from the region. The spread of the pandemic and closure of factories, as well as shipping issues, then hit Amazon and its sellers quite hard.

Different points at the supply chain all ran out of goods or production capabilities, which started limiting what was available and hurt revenue for everyone involved. Diversifying sources and partners, both in goods and location, could have mitigated some of this risk.

Logistics professionals should look at regional needs and concerns right now. Identify where your product lines could struggle and if there are potential replacements for materials. If you’re a 3PL or providing other warehouse services, consider expanding to multiple locations. This can help you get goods to the end-customer faster as well as protecting fulfillment operations during COVID and similar black swan events.

Safeguarding people is just the minimum

At least seven of Amazon’s employees have died from the coronavirus, and the company has been very unclear about how many others have become ill. There is a new lawsuit by employees around the company’s contact tracing and potential exposure of employees — worth noting that the lawsuit doesn’t seek damages, just an injunction forcing Amazon to follow public health standards.

Throughout the pandemic, Amazon has taken heat for how it has treated its workers. This covered safety equipment and protections, sick leave and sending people home, and how it responded to labor demands. And, much of the anger is deserved.

The pandemic is scary and should be taken seriously. It was Amazon’s responsibility to make its employees feel like they were taken care of and protected.

Hopefully, this has served as a wakeup call for logistics and warehouse businesses. Your people matter, far beyond just what they contribute to the health of your business. There’s also a good chance your business will be judged by how you treat your teams. The world now makes much of this information public, too, if you need that extra layer of fear to get going and ensure your teams are safe, protected, and following the right policies.

Protect long-term customers and your business model

Consumers are spending more money on Amazon and shopping more often, largely due to the pandemic, but they’re not as happy about it. People saying they were either “very” or “extremely” satisfied with Amazon’s service fell from 73% to 64% from June 2019 to now.

The biggest frustrations have been delays in shipping and unavailable products. People view that they’re paying for the service, and its interrupted supply chain is still creating waves. Prime shoppers aren’t able to get the fast, two-day shipping on all purchases, despite being the most lucrative customers. Amazon has actually seen a decline in customer satisfaction over the last five years, according to that same report.

Growing discontent is a threat. Logistics and warehouse businesses don’t have the size of Amazon or the weight to throw around. If your customers aren’t getting what they’re paying for, they’ll move on to another service provider. The same is true if you’re late, damaging goods, or getting orders wrong. There are few real alternatives to Amazon, but there are many alternatives to all of us.

That’s perhaps the most important lesson in all of this for the logistics profession. Amazon needs to learn it before a genuine rival rises to compete, but it’s a good focus for warehouses starting today.

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Jake Rheude is the Director of Marketing for Red Stag Fulfillment, an ecommerce fulfillment warehouse that was born out of ecommerce. He has years of experience in ecommerce and business development. In his free time, Jake enjoys reading about business and sharing his own experience with others.

AI

8 Ways your AP Process Leaks Spend – and How AI can Prevent it

Today’s companies put huge efforts into negotiating the best terms with their suppliers. Procurement teams regularly spend weeks or months going back and forth on contract terms and volume discounts to get the most bang for their buck.

Too often, these savings aren’t realized. Suppliers may ignore the negotiated terms when invoicing, and AP teams, faced with a deluge of invoices and limited time to get payments out the door, only sample select transactions and only do basic 2 or 3 way matching of volume and price. This inevitably means costly invoice problems fall through the cracks — from mismatched invoice and contract terms, to unapplied discounts, to completely bogus charges, and more.

Optimizing your AP process may seem like a big undertaking, but it’s much easier than it might seem, and worth the effort. According to The International Association of Contracts and Commercial Management (IACCM), companies that work to improve controls over invoice payment will see a return of more than 4 percent of invoice value.

Even if you’re ready to improve your AP process, one pesky question remains: How do you actually do it? Once upon a time, it would have been necessary to hire more people to check every transaction. But today, technology can provide a crucial and cost-effective assist for overstretched AP teams.

Artificial intelligence (AI) is becoming more and more common in business contexts. Nearly 90 percent of companies planned to increase AI spend in 2019, according to a Deloitte survey. However, the idea of actually using AI may feel a little unrealistic for some. While more and more corporations are automating AP processes, 30 percent of businesses still rely on manual invoice processing, according to The Institute of Finance and Management.

If you’ve already implemented other technologies in your workflow, AI can fit in seamlessly. AI-powered spend automation software integrates with existing expense management, invoice automation, contract management, and ERP systems to augment rather than disrupt your status quo.

8 common (and costly) invoice problems

Here are just a few of the problems AI-powered solutions can help your team avoid during the spend audit process:

1. Fraudulent invoices: When it comes to invoice fraud, if you can dream it, chances are fraudsters have tried it: From inflated invoices, to completely made-up charges, to shell companies, to vendor impersonation, and more.

Too often, the calls are coming from inside the house. The Association of Certified Fraud Examiners (ACFE) found that occupational fraud (fraud committed by employees against employers) resulted in more than $7 billion in total losses in 2018. AI systems with a compliance component can spot risk factors commonly associated with fraud so your team has a chance to review these invoices manually before they’re paid out.

2. Duplicate invoices: Up to two percent of the average company’s invoices a duplicates, according to AuditNet. This may seem like a relatively small number, but for businesses doling out millions or billions on business activities, the figure is far from trivial.

Some vendors might double up charges on purpose, but often duplicate invoices are mistakes (after all, your vendors’ finance teams are overworked too). While some invoice automation systems try to catch these double charges, they usually only succeed if the invoices are labeled with the same number or have the exact same total — which isn’t always the case, particularly if there’s someone scheming behind the scenes.

3. Missing discounts: You fought hard for volume discounts, but how often are you checking invoices to make sure they’re applied? AI-based systems can often  compare contract and invoice terms automatically to make sure you’re not missing out on early payment, loyalty, or quantity discounts. You’ll be notified of any missing discounts so you can remedy the situation before you pay. In the case of early payment discounts, this software notifies you that the invoice should be prioritized to get payment out in ample time.

 4. Mismatched service levels: You signed up for the standard package, but you’re being charged for the premium offering. This type of mismatch is all too easy to overlook amid your monthly deluge of invoices.

The correct AI solution can compare agreed-upon service levels in your contract with every invoice you receive to make sure that this type of costly problem doesn’t fly under the radar. When it comes to physical items, it can ensure you receive all the items you’re being billed for before you pay, by double-checking shipping documents against inventory systems.

5. Double payments: Double payments can happen as a result of vendors submitting duplicate invoices, but the problem can also originate from your own team. Accounting systems hold up an invoice for all sorts of reasons, e.g., it requires further approval or it failed a match. In many cases, an employee might intervene to get the invoice paid manually (to meet a deadline or because they’re being pestered by a supplier or don’t want to damage a relationship). Meanwhile, the invoice is still in your system and when the hold is later cleared up, it’s processed and paid… again.

This is another one of those sources of spend leakage that most companies never become aware of. AI-powered systems constantly cross-check invoices and payments and flag any duplicate payments before you send them out, so the money never leaves the front door.

6. Exorbitant pricing: It can be difficult and time-consuming to keep track of the market rate for all the various services and products your business requires. AI can regularly compare your current costs to thousands of other sources to determine whether your invoices reflect the market rate for the goods or services provided. It can also flag individual invoices where your price exceeds the market rate.

Knowledge is power, and this information helps your business negotiate more effectively with existing suppliers or look to new ones if there’s an opportunity for cost savings without sacrificing quality.

7. Unsatisfactory work activity: When it comes to hiring contractors, there are situations when it’s particularly difficult to understand and assess whether they’re fulfilling their agreed-upon duties, like professional and IT services. AI-based tools can ingest nearly unlimited data to build a profile of what comprises satisfactory work activity — e.g., regular activity in Slack or over email — and highlight changes in the typical patterns. This helps you verify that you’re paying contractors fairly for the work product they’re providing.

8. Overpaying for software: Are you licensed for seven software seats, but only using three? It’s not uncommon for organizations to overpay for software licenses without ever realizing it. AI-based software keeps tabs on your organization’s software usage and compare it to the charges on your monthly invoices to help alert you to savings opportunities.

How AI can help

Implementing a best-in-class AI solution can support a consistent process and add an additional layer of scrutiny. These solutions make it possible to audit 100% of invoice spend prior to payment, automatically and near-instantaneously checking every invoice in your system for risk factors before they’re paid, and flagging the highest risk items for your team to review. This will help your team get ahead of problems and potential leakage, rather than try to recover it afterwards.

Below are the critical requirements for considering an AI solution for AP spend management:

1. Audit 100%, prepayment. Automatically audit 100% of invoices before reimbursement with AI.

2. Understand documents. Instantly scan every line of every invoice to understand charges and track the correct spend category.

3. Enrich with intelligence. Check online sources to identify better prices for similar goods and services.

4. Assess and refine risk. Flag suspicious addresses or billing changes to avoid fraud. Spot duplicate charges from other invoices, other invoice systems, or from expenses.

5. Streamline process. Integrate into your existing AP automation system to audit every invoice in real time to spot errors, waste, and fraud.

Conclusion

The best AI software can help your team regain control over your spend by checking every single transaction to identify high-risk invoices in your pipeline — saving time, streamlining processes, and ultimately reducing spend leakage.

If your AP team’s efforts to find problematic spend feels neverending, you’re not alone — but it doesn’t have to be that way. AI has changed the paradigm for modern finance teams, giving them greater visibility into their AP process and the time they need to address the highest risk issues. Not only can AI transform the way finance teams operate, it also saves them business money by spotting problems consistently and before invoices are paid. By implementing a leading AI solution, your team can audit 100% of spend, make sure that every invoice complies with its contract terms, and ensure you’re receiving every savings opportunity you’re entitled to — all while paying your bills on time.

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Anant Kale founded AppZen in 2012 to bring AI into back offices around the world. As CEO he is responsible for the product vision and execution of the company’s broad mission. Previously he was the VP of Applications at Fujitsu America from 2009-2012, responsible for product management, and delivery of Fujitsu’s applications and infrastructure for enterprise. He has 15+ years of experience in software development. He has an MBA and a BS in Finance and Engineering from Mumbai University.

response

Global Trade Magazine Launches COVID C.A.R.E. Business Response Program

Global Trade Magazine is ramping up efforts in supporting global businesses by utilizing a new set of tools found in its technology toolbox. Companies capable of adapting their technology through the crisis are doing so at a record pace as leading automotive giants are now churning out respirators instead of automobiles while whiskey producers scramble to make hand sanitizer to help meet demand. Global Trade Magazine is doing the same thing for global businesses and their customer base.

“Responding to global business leader and customer questions and concerns will be more critical than ever now. Doing so effectively is a monumental task for many global trade players, yet doing so will be the difference in businesses keeping their operations moving and laying off hundreds or even thousands. We’ve re-engineered our Artificial Intelligence product to meet customer demands,” stated Eric Kleinsorge, CEO and Publisher of Global Trade Magazine.

The Global Trade COVID C.A.R.E. (Coronavirus Automated Response Effort) Local Response Program takes a unique approach in supporting global businesses and their efforts in responding to customer concerns by utilizing AI response systems. This integrated system Records, Responds, Alerts, Prioritizes and Completes requests from customers that need information and answers from global businesses in the global trade community. Instead of fearing this change, the Global Trade Mag team linked arms and stepped up to the challenge. From receiving requests and concerns to automated feedback, request prioritization, and system follow-ups, the Global Trade Response Program offers an integrated system of checks and balances that captures every request from every customer.

“We have been in the business of helping global companies communicate with their customers and now it’s our turn to help these businesses communicate and update these customers,” Kleinsorge concluded.

To request information on how this program can help your business, please click here or call (469) 778-2606.

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About GSLI/Global Trade Magazine

Global Site Location Industries (GSLI) is the parent company of Global Trade Magazine and was founded in 1994 by Eric Kleinsorge with a very specific goal in mind: grow local and global communities while bringing business projects to life through strategic economic development partnerships and customer management strategies. He is recognized in over 110 articles as an industry expert and has conducted interviews with well-known figures including George W. Bush, Colin Powell, Jay Leno, Jerry Jones, Rudy Giuliani, Mike Dell, and many more.

Not only do the companies support community and global branding, but we bring company goals to life through a tailored approach to attracting sustainable businesses and customer partnerships. We take pride in our reputation as an expert in assisting expanding and relocating companies partner with the world’s finest companies. For more than 20 years, GSLI has been the premier partner of choice for companies– both big and small, looking to create a solid economic and customer foundation primed for growth and success.

modex

MODEX Day Three: Robotics & Automation Continue Maturing

In typical Modex fashion, robotics and automation were among the hot topics discussed by keynote speakers, exhibitors, and attendees. A vast array of capabilities, sizes, and industry-specific robotics could be found throughout the show floor, each showing off a new capability. It’s clear that robotics continue to evolve and show no signs of slowing down progress in meeting demand within warehouses and distribution centers.

Mike Futch, President of Tompkins Robotics made this point very clear during his session on Wednesday afternoon titled, “The Lights Out DC/FC: How Close Can We Get?”

Futch addressed the use of various technologies to address workforce constraints while improving the effectiveness and performance of the supply chain.  He identified what advancements will assist in solving bottlenecks such as facility constraints, space issues, and the current situation in unemployment. As these challenges persist, robotics continues to mature.

“There’s a limited workforce, a limited number of people that can drive the distance to enter the immediate geographic region, and these larger buildings are competing for that workforce that’s already at a low unemployment rate along with offering increased wages and siphoning workers off of others. This is a real challenge for some markets.”

“Labor is scarce and we have record-low unemployment, typically to expand capacity from a volume perspective and companies are turning to more shifts. If you already have a tight labor market and you’re adding shifts, where are the workers coming from? And this creates a bigger problem.”

The workforce is a key constraint and while workforce rates are lower than others in some places, Futch states that companies are competing to stay ahead of demand through increased wages while solving the best approach to a limited workforce.

Machines continue to do the same things a human can do but without interruptions with repetitive, difficult, or taxing work that inevitably fatigues the human body. That being said, the industry still requires a skilled workforce and robotics should not be purchased for their appeal. It’s becoming clear that a blend of workers and robotics is a more common theme for integrating such advancements over the idea that robotics will “overtake” worker’s jobs. In fact, robotics is providing a way to re-establish worker tasks rather than eliminating the worker.

“Robotics has matured tremendously from where they were a few years ago. About 5-10 years ago, the pick-and-place robots at the show could not do the things they are capable of doing now. Two years from now, they’ll have the capability to do twice as much as now. Robotics is maturing and meeting the three R’s: improve rate, improve reliability, and improve the range of products and items,” he explained. ”

In terms of a fully automated DC, Futch added that about 60-85 percent of manual tasks can be automated realistically rather than a “lights out” center.

“Beyond the pick-and-place robots, other robots are doing the same thing: creating a blur of separation between what a human can do and what a machine can do.”

artificial intelligence

Artificial Intelligence Market to Reach $54 Billion by 2026

According to a new study published by Polaris Market Research, the global artificial intelligence market is anticipated to reach USD 54 billion by 2026. The advancements of robots and the rise in their deployment rate particularly, in the developing economies globally have had a positive impact on the global artificial intelligence market.

Augmented customer experience, expanded application areas, enhanced productivity, and big data integration have highly propelled the artificial intelligence market worldwide. Although, the absence of adequate skilled workforce, as well as threat to human dignity, are some of the factors that could affect the growth of the market. However, these factors are expected to have minimal impact on the market attributed to the introduction of advanced technologies.

An extraordinary increase in productivity has been achieved with machine-learning. For instance, Google, with the help of its experimental driverless technology has transformed cars including, Toyota Prius. The integration of various tools by artificial intelligence has helped in the transformation of business management. These tools include brand purchase advertising, workflow management tools, trend predictions among others. For example, Google’s voice accuracy technology has a 98% accuracy rate. Furthermore, Facebook’s DeepFace technology has a success rate of approximately 97% in recognizing faces. Such accuracy in technologies is further anticipated to bolster the market growth during the forecast period.

Currently, North America dominates the global artificial intelligence market attributed to the high government funding availability, existence of prominent providers in the region, and robust technical adoption base. Also, the region is expected to continue its dominance during the forecast period. Moreover, the adoption of cloud-based services in key economies, such as the US and Canada, is considering adding to the market growth in the North American region. The markets in Asia Pacific, MEA and South America region are expected to notice a high growth during the coming years. The growth in the Asia Pacific region is attributed to the increasing demand for artificial technologies by the developing economies. Thus, the region is anticipated to grow at the highest CAGR during the forecast period.

 

Major companies profiled in the report include Google Inc., Intel Corporation, Nvidia Corporation, Microsoft Corporation, IBM Corporation, General Vision, Inc., Qlik Technologies Inc., MicroStrategy, Inc., Brighterion, Inc., and Baidu, Inc. among others.

Key Findings from the study suggest North America is expected to command the market over the forecast years. APAC is presumed to be the fastest-growing market, developing at a CAGR of more than 65% over the forecast period. The artificial intelligence market is presumed to develop at a CAGR of over 55.9% from 2018 to 2026. The high implementation of artificial intelligence in several end-user verticals including, retail, automotive and healthcare is projected to boost the growth of the market over the forecast period. Several companies are making considerable investments to integrate artificial intelligence competencies into their portfolio of products. For instance, in 2016, SK Telecom and Intel Corporation signed an agreement for the development of the artificial intelligence-based vehicle-to-everything (V2X) technology as well as video recognition.

For More Information About Artificial Intelligence Market @ https://www.polarismarketresearch.com/industry-analysis/artificial-intelligence-market/request-for-customization
verification

Is It You Or An ID Thief? How AI Uses Document Verification To Keep You Safe.

It’s a moment most people have experienced.

You’re required to show your ID for something and you wait as the person studies both your face and the photo on the driver’s license, passport, or another document, making sure you’re not an impersonator trying to pull a fast one.

These days, artificial intelligence is playing a role similar to that security person, with software that allows validation of IDs remotely through digital document verification. This way you can do business through your smartphone, and someone on the other end can make sure you’re who you say you are and that a thief hasn’t stolen your identity.

And that’s especially important at a time when identity theft has been on the rise, says Stephen Hyduchak, CEO of Aver (www.goaver.com), an identity-verification service.

“Fraudsters are getting creative, but so is technology,” Hyduchak says. “It’s important to keep up because there are so many ways to create fake documents that allow someone to claim to be you and maybe even get away with it.”

Hyduchak says there are a few categories of document fraud:

Illegitimate documents. These documents are completely false. They have characteristics such as missing holograms or other current standards that are essential parts of a legitimate version of that document.

False documents. This is a document that belongs to one person, but that another person tries to use in an effort to authenticate himself.

Modified documents. This is when an original document is altered. Hyduchak says the alterations can be caught with software that detects whether fonts and text match the originals.

How do fraudsters even get the ID documents to start with? Hyduchak says it’s a matter of data security breaches – and often a combination of more than one breach. He gives this example. Just recently, the cryptocurrency exchange Binance, using a third-party Know-Your-Customer (KYC) provider, was the victim of a hack that leaked over 10,000 photographs of purported Binance KYC data. This breach affected up to 60,000 people.

“On Binance, users buy and sell cryptocurrency, something that is privacy-centric by its very nature, but still vulnerable,” Hyduchak says. “Coupling leaks like this with major data breaches like Equifax and Target, our personal information can be manipulated for the fraud with some basic photoshop work.”

A digital verification process is one way to head off any subterfuge, Hyduchak says. For example, his company has a program that works this way: The user captures a picture of their ID or passport using their smartphone. The user then takes a selfie to verify they are the same person pictured on the ID or passport. Facial recognition software compares the images through algorithms.

“As time goes on,” Hyduchak says, “I think you are going to see digital facial checks become the standard for ID verification, and that will eliminate most types of fraud.”

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Stephen Hyduchak is the CEO of Aver (www.goaver.com), an identity-verification service. Hyduchak worked in corporate finance for companies such as PRA Health Sciences before finding the entrepreneur bug. He began working on media and design for small businesses, which led him to consulting projects in the blockchain space, and eventually to founding Aver.