Press Releases

B21 Launches Cryptocurrency Investment and Portfolio Management App in India as Legalization of Crypto Trading Paves Way for Investment in Digital Assets (

B21 also partners with Minance to provide crypto investment services, research-based market insights and additional features

BANGALORE, July 01, 2020 (GLOBE NEWSWIRE) — (Via Blockchain Wire) Digital asset investing company B21 ( today announced the launch of its B21 Invest app in India. B21 Invest makes it easy to purchase and manage a portfolio of cryptocurrencies including Bitcoin, Ethereum, and EOS straight from a mobile phone. B21 Invest supports investment for as little as INR 2,000 ($25) per transaction, which can be in a single asset like Bitcoin or across a customized investment portfolio. Users in India can invest using local fiat currency and local payment methods including Unified Payments Interface (UPI), debit cards and bank transfers. 

B21 Invest, available free of charge on the App Store and Google Play, simplifies the entire onboarding and investment process by allowing users to create their own secure, customized portfolio of cryptocurrencies without going through multiple “Know Your Customer” (KYC) and wallet set-up hurdles. Users can instantly rebalance their portfolio, monitor profitability on their dashboard, or liquidate their positions at any time with a few clicks. Users can also withdraw money globally via bank transfer (ACH) and international wire. 

The Supreme Court of India recently reversed a circular issued by the Reserve Bank of India (RBI) which prohibited banks from providing services to crypto traders, exchanges, and other businesses dealing in cryptocurrencies. The reversal has led to renewed interest in digital asset investing and a spike in exchange trading volumes.  

“India is fast becoming a major market for digital asset investing as the use of mobile technology expands alongside interest in alternative asset classes,” said Nitin Agarwal, Founder and Director B21. “B21 is proud to provide a method by which people can easily invest in digital assets directly from their mobile phones without having to learn how to navigate complex trading exchanges or managing their private keys. We are excited to launch our local Indian payment methods which will make investing even easier and more efficient.”

B21 has also announced a collaboration with Minance, a wealth management firm offering an extensive range of investment products from derivatives and equities to private assets and international equities. B21 will offer Minance clients a gateway for purchasing cryptocurrency, with Minance providing investors with research-based insights on the market, along with a cryptocurrency tax calculator and additional features to be introduced in the coming year. 

“Minance research has proven that it’s wise to build a portfolio starting with the largest cryptocurrencies by market cap,” said Anurag Bhatia, CEO and Head of Investments at Minance. “The team at B21 has cracked this. Instead of building a platform with an overwhelming array of cryptocurrencies to evaluate, they’ve curated the top digital assets, providing investors a simplified gateway to this investment class.”

“Banning cryptocurrency only serves to curb innovation,” added Agarwal. “Crypto and decentralized systems are at the forefront of the next big technological advancement in human history and the cornerstone to Web 3.0. Cryptocurrency and blockchain systems have yet to reach their full potential and we are keen to help the mass market access to these attractive investment sectors.” 

B21 is available to customers across 65 countries including the U.S. The firm has maintained an office in Bangalore since 2019 and plans to grow its client base in India to two hundred thousand active users by the end of 2020. 

About B21
B21 has created the simplest way to invest in and manage crypto asset portfolios. The company’s  solution provides the next generation of investment tools designed to appeal to a global mass market. It allows users to easily buy, sell, trade and manage their crypto assets, all through a user-friendly and intuitive interface.  The B21 Invest app is operated by Digital Software Solutions Inc. under exclusive license from B21C Limited. Asset custodial accounts are provided by Prime Trust. For more information and terms and conditions, please visit




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Press Releases

ISW Holdings Announces Official FINRA Approval of Corporate Name Change as Operations Expand (

LAS VEGAS, June 30, 2020 (GLOBE NEWSWIRE) — via NetworkWire – ISW Holdings, Inc. (OTC: ISWH) (“ISW Holdings” or the “Company”), a global brand management holdings company, is pleased to report that the Financial Industry Regulatory Authority, Inc. (“FINRA”) has approved the Company’s official name change to “ISW Holdings, Inc.” (from “International Spirits & Wellness Holdings, Inc.”).

“We are now manifestly a diversified global brand management holdings company, with active commercial interests spanning the wellness, renewable energy, home healthcare, digital colocation, spirits and cryptocurrency mining sectors,” remarked Alonzo Pierce, president and chairman of ISW Holdings. “This name change simply reflects the need to bring our company name into alignment with our underlying narrative and strategic vision.”

The official name change follows a series of quarters featuring strong growth from the Company’s Home Healthcare business as well as its recent entrance into a promising joint venture partnership with Bit5ive LLC, a global leader in cryptocurrency mining and innovative turnkey mining solutions.

Given these advances, management felt the Company’s prior name no longer reflected a coherent picture. The new corporate name creates a platform that will make sense given the communications the Company anticipates in the weeks ahead as its commercial activity in the cryptocurrency mining equipment space picks up steam.

Pierce continued, “We look forward to an exciting summer and a tremendous second half of the year as we ramp up activity following our Bit5ive deal. We have some powerful steps in the works right now, and we look forward to updating our current and prospective shareholders along with the general public as we continue to expand operations and build on a diversified foundation targeting multiple high-growth markets.”

About ISW Holdings
ISW Holdings, Inc. (ISWH), based in Nevada, is a diversified portfolio company comprised of essential business lines that serve consumer product demands. Our expertise lies in strategic brand development, early growth facilitation, as well as brand identity through our proprietary procurement process. Together with our partners, we seek to provide a structure that meets large scalability demands, as well as anticipated marketplace needs. We are able to meet these needs through a variety of strategic innovative processes. ISWH is creating and managing brands across a spectrum of disruptive industries. It maneuvers its proprietary companies through critical stages of market development, which includes conceptualization, go-to-market strategies, engineering, product integration and distribution efficiency. The Company has also partnered with a well-known software development and consulting company, Bengala Technologies LLC, which is developing significant enhancements in the supply chain management space; and, the partnership has a vitally needed patent pending.

Forward Looking Statements: This press release may contain forward-looking statements that involve risks and uncertainties. These statements relate to future events or our future financial performance. In some cases, you can identify forward-looking statements by terminology including “could,” “may,” “will,” “should,” “expect,” “plan,” “anticipate,” “believe,” “estimate,” “predict,” “potential” and the negative of these terms or other comparable terminology. While these forward-looking statements, and any assumptions upon which they are based, are made in good faith and reflect our current judgment regarding the direction of our business, actual results will almost always vary, sometimes materially, from any estimates, predictions, projections, assumptions or other future performance suggested in this report. Except as required by applicable law, we do not intend to update any of the forward-looking statements so as to conform these statements to actual results. Investors should refer to the risks disclosed in the Company’s reports filed from time to time with OTC Markets (

For more information, visit

Company Contact:
Investor Relations

Corporate Communications:
InvestorBrandNetwork (IBN)
Los Angeles, California
310.299.1717 Office


Press Releases

DMG Energizes Additional 1,000 ASIC Miners (


  • DMG completes installation and energizing of an additional 90 petahash of miners as part of previously announced purchase
  • DMG’s current self-mining has now increased to 110 petahash
  • DMG to provide Executive Compensation Disclosure in upcoming Management Information Circular

VANCOUVER, British Columbia, June 30, 2020 (GLOBE NEWSWIRE) — DMG Blockchain Solutions Inc. (TSX-V: DMGI) (DMGGF:OTC US) (FRANKFURT:6AX) (“DMG” or the “Company”), a diversified blockchain and technology company, is pleased to announce that it has installed and energized the previously announced purchase of 1,000 Micro BT M30 miners.

DMG acquired these miners via equipment financing as opposed to any further dilutive equity financings. The Company’s 2020 goal, which requires additional financing, is 500 petahash of self-mining and DMG continues to provide hosting services to third party clients, in line with its blended model of hosting and self-mining.

DMG also announces in respect of its 2020 annual meeting of shareholders (the “Meeting”), that due to the health implications and constraints associated with the COVID-19 pandemic, the Company is relying on the exemption provided in the B.C. Securities Commission Instrument 51-516 – Temporary Exemptions from Certain Requirements to File or Send Securityholder Materials and similar exemptions provided by members of the Canadian Securities Administrators in Alberta and Ontario to postpone the filing of its executive compensation disclosure for the fiscal year ended September 30, 2019 required under Section 9.3.1 (2.2) of National Instrument 51-102 – Continuous Disclosure Obligations. The Company will include such executive compensation disclosure as well as its annual financial statement request form in the management information circular to be prepared in connection with the Meeting. Updates regarding the date and format of the Meeting will follow. 

About DMG Blockchain Solutions Inc.

DMG is a diversified cryptocurrency and blockchain platform company that is focused on the two primary opportunities in the sector – mining public blockchains and applying permissioned blockchain technology. DMG focuses on mining bitcoin, providing hosting services for industrial mining clients, earning revenues from block rewards and transaction fees, developing data analytics and forensic software products, working with auditors, law firms, and law enforcement to provide technical expertise. DMG’s permissioned blockchain technology is focused on developing enterprise software for the supply chain management of controlled products. DMG’s strategy is to become the domain experts across the business verticals it focuses on. DMG’s management team includes seasoned crypto experts, forensic & financial professionals and blockchain developers with deep relationships throughout the industry, with previous experience working at Bitfury, PwC, EY, Cisco and UBS.

For more information on DMG Blockchain Solutions visit:

On behalf of the Board of Directors,
Daniel Reitzik, CEO & Director

For further information, please contact:

DMG Blockchain Solutions Inc.

Investor Relations: John Martin
Toll Free: 1-888-702-0258
Direct: 778-868-6470

Cautionary Note Regarding Forward-Looking Information

This news release contains forward-looking information based on current expectations. Statements about the Company’s plans to increase petahash (PH) by self-mining, price of bitcoin, plans and intentions, other potential transactions, holding the 2020 shareholders’ meeting, acquisition of customers, product development, events, courses of action, and the potential of the Company’s technology and operations, among others, are all forward-looking information. Forward-looking statements consist of statements that are not purely historical, including any statements regarding beliefs, plans, expectations or intentions regarding the future. Such information can generally be identified by the use of forwarding looking wording such as “may”, “expect”, “estimate”, “anticipate”, “intend”, “believe” and “continue” or the negative thereof or similar variations. The reader is cautioned that assumptions used in the preparation of any forward-looking information may prove to be incorrect. Events or circumstances may cause actual results to differ materially from those predicted, as a result of numerous known and unknown risks, uncertainties, and other factors, many of which are beyond the control of the Company, including but not limited to, business, economic and capital market conditions; the ability to manage operating expenses, which may adversely affect the Company’s financial condition; the ability to remain competitive as other better financed competitors develop and release competitive products; regulatory uncertainties; access to equipment; market conditions and the demand and pricing for products; the demand and pricing of bitcoins; security threats, including a loss/theft of DMG’s bitcoins; DMG’s relationships with its customers, distributors and business partners; the inability to add more power to DMG’s facilities; DMG’s ability to successfully define, design and release new products in a timely manner that meet customers’ needs; the ability to attract, retain and motivate qualified personnel; competition in the industry; the impact of technology changes on the products and industry; failure to develop new and innovative products; the ability to successfully maintain and enforce our intellectual property rights and defend third-party claims of infringement of their intellectual property rights; the impact of intellectual property litigation that could materially and adversely affect the business; the ability to manage working capital; and the dependence on key personnel. DMG may not actually achieve its plans, projections, or expectations. Such statements and information are based on numerous assumptions regarding present and future business strategies and the environment in which the Company will operate in the future, including the demand for its products, the ability to successfully develop software, that there will be no regulation or law that will prevent the Company from operating its business, anticipated costs, the ability to secure sufficient capital to complete its business plans, the ability to achieve goals and the price of bitcoin. Given these risks, uncertainties and assumptions, you should not place undue reliance on these forward-looking statements.

The securities of DMG are considered highly speculative due to the nature of DMG’s business.

Factors that could cause actual results to differ materially from those in forward-looking statements include, failure to obtain regulatory approval, the continued availability of capital and financing, equipment failures, lack of supply of equipment, power and infrastructure, failure to obtain any permits required to operate the business, the impact of technology changes on the industry, the impact of Covid-19 or other viruses and diseases on the Company’s ability to operate, secure equipment, and hire personnel, competition, security threats including stolen bitcoins from DMG or its customers, consumer sentiment towards DMG’s products, services and blockchain technology generally, failure to develop new and innovative products, litigation, increase in operating costs, increase in equipment and labor costs, failure of counterparties to perform their contractual obligations, government regulations, loss of key employees and consultants, and general economic, market or business conditions. Forward-looking statements contained in this news release are expressly qualified by this cautionary statement. The reader is cautioned not to place undue reliance on any forward-looking information. The forward-looking statements contained in this news release are made as of the date of this news release.  Except as required by law, the Company disclaims any intention and assumes no obligation to update or revise any forward-looking statements, whether as a result of new information, future events or otherwise.  Additionally, the Company undertakes no obligation to comment on the expectations of, or statements made by third parties in respect of the matters discussed above.

Neither the TSX Venture Exchange nor its Regulation Service Provider (as that term is defined in the policies of the TSX Venture Exchange) accepts responsibility for the adequacy or accuracy of this news release. 

Press Releases

BIGG Digital Assets Inc. Subsidiary Blockchain Intelligence Group Launches Bitcoin Cash on QLUE™ Forensics Platform (

VANCOUVER, British Columbia, June 30, 2020 (GLOBE NEWSWIRE) — BIGG Digital Assets Inc. (“BIGG” or the “Company”) (CSE: BIGG; OTCQB: BBKCF; WKN: A2PS9W), owner of Blockchain Intelligence Group ( (“BIG”), a leading developer of blockchain technology search, risk-scoring and data analytics solutions, is pleased to announce that Bitcoin Cash (BCH) has been launched as part of QLUE Release v1.16 ( QLUE also supports BTC, ETH, ERC20 and LTC.

Bitcoin went through a hard fork in July 2017 which in turn created Bitcoin Cash. There was suddenly a mirror image of the legacy Bitcoin blockchain that investigators and financial institutions alike have interest in when gauging liabilities and risk. Blockchain Intelligence Group’s QLUETM product now addresses those risks by allowing financial institutions and law enforcement agencies to have visibility and tracing capabilities into Bitcoin Cash. Adding Bitcoin Cash is an addition to QLUE’s already impressive cryptocurrency tracking and tracing abilities.

BIG’s QLUETM (Qualitative Law Enforcement Unified Edge) is used by Law Enforcement, Banks, Exchanges, ATM Operators to perform due diligence and blockchain security investigations at greater forensic depth by exploring where the related funds are coming from and going to, and the profiles of the entities involved. QLUETM allows for the export of an investigation and in turn this can be saved on file for regulators, or to request a subpoena or be provided as evidence in court. 

BIG’s President, Lance Morginn, commented, “QLUETM is being touted by the industry as having the most user friendly interface which makes it a lot easier to onboard new law enforcement or compliance officers. The addition of BCH makes QLUETM even more valuable to our current and prospective customers for investigations thus expanding our revenue potential and market share of the crypto forensics market globally.”

On behalf of the Board

Lance Morginn
President & Director

About BIGG Digital Assets Inc.

BIGG Digital Assets Inc. (BIGG) believes the future of crypto is a safe, compliant, and regulated environment. BIGG invests in products and companies to support this vision. BIGG owns two operating companies: Blockchain Intelligence Group ( and Netcoins (

Blockchain Intelligence Group (BIG) has developed a Blockchain-agnostic search and analytics engine, QLUETM, enabling Law Enforcement, RegTech, Regulators and Government Agencies to visually track, trace and monitor cryptocurrency transactions at a forensic level. Our commercial product, BitRank Verified®, offers a “risk score” for cryptocurrencies, enabling RegTech, banks, ATMs, exchanges, and retailers to meet traditional regulatory/compliance requirements.

Netcoins develops brokerage and exchange software to make the purchase and sale of cryptocurrency easily accessible to the mass consumer and investor with a focus on compliance and safety. Netcoins utilizes BitRank Verified® software at the heart of its platform and enables crypto transactions via retail locations globally, a self-serve crypto brokerage portal and an Over-The-Counter (OTC) trading desk.

For more information and to register to BIGG’s mailing list, please visit our website at  Or visit SEDAR at

Forward-Looking Statements:

Certain statements in this release are forward-looking statements, which include completion of the search technology software and other matters. Forward-looking statements consist of statements that are not purely historical, including any statements regarding beliefs, plans, expectations or intentions regarding the future. Such information can generally be identified by the use of forwarding-looking wording such as “may”, “expect”, “estimate”, “anticipate”, “intend”, “believe” and “continue” or the negative thereof or similar variations. Readers are cautioned not to place undue reliance on forward-looking statements, as there can be no assurance that the plans, intentions or expectations upon which they are based will occur. By their nature, forward-looking statements involve numerous assumptions, known and unknown risks and uncertainties, both general and specific that contribute to the possibility that the predictions, estimates, forecasts, projections and other forward-looking statements will not occur. These assumptions, risks and uncertainties include, among other things, the state of the economy in general and capital markets in particular, and other factors, many of which are beyond the control of BIGG. Forward-looking statements contained in this press release are expressly qualified by this cautionary statement. Undue reliance should not be placed on the forward-looking information because BIGG can give no assurance that they will prove to be correct. Important factors that could cause actual results to differ materially from BIGG’s expectations include, consumer sentiment towards BIGG’s products and Blockchain technology generally, technology failures, competition, and failure of counterparties to perform their contractual obligations.

The forward-looking statements contained in this press release are made as of the date of this press release. Except as required by law, BIGG disclaims any intention and assumes no obligation to update or revise any forward-looking statements, whether as a result of new information, future events or otherwise. Additionally, BIGG undertakes no obligation to comment on the expectations of, or statements made by, third parties in respect of the matters discussed above.

The CSE does not accept responsibility for the adequacy or accuracy of the content of this Press Release.

Press Releases

Cryptologic Completes Conversion of Outstanding Debentures (

TORONTO, June 30, 2020 (GLOBE NEWSWIRE) — Cryptologic Corp. (“Cryptologic” or the “Company”) (CSE:CRY) today announces that the Company has completed the conversion of its 8% extendible convertible unsecured debentures (the “Conversion”) into common shares (the “Common Shares”) as described in the Company’s press release dated June 19, 2020. Cryptologic converted the principal amount of $34,500,000 at a conversion price of $1.00 (the “Conversion Price”), and made payment of all accrued interest to the date of Conversion by issuing Common Shares at a price equal to the accrued interest divided by the Conversion Price. After the issuance of 35,880,000 Common Shares as a result of the Conversion and payment of accrued interest in Common Shares, the Company now has 48,599,162 outstanding Common Shares.

The Company today also announces a leadership transition. Effective today, Joshua Lebovic has been appointed Interim Chief Financial Officer, following the departure of Jordan Greenberg, the Company’s previous Chief Financial Officer. The Company thanks Mr. Greenberg for his very capable service.

For information please contact:

Joshua Lebovic
Interim Chief Financial Officer
(647) 715-3707

About Cryptologic Corp.

Cryptologic Corp. is currently a cryptocurrency mining company that is focused on divesting its crypto mining assets and exploring acquisition opportunities in sectors outside of cryptocurrency mining.

Cautionary Note Regarding Forward-Looking Information

Certain statements in this press release, including statements with respect to the number of Common Shares to be issued as a result of the Conversion and the payment of accrued interest at the Conversion Price and the anticipated effective date of Conversion, contain forward-looking information which can be identified by the use of forward looking terminology such as “believes”, “expects”, “may”, “desires”, “will”, “should”, “projects”, “estimates”, “contemplates”, “anticipates”, “intends”, or any negative such as “does not believe” or other variations thereof or comparable terminology. No assurance can be given that potential future results or circumstances described in the forward-looking statements will be achieved or will occur. By their nature, these forward-looking statements necessarily involve risks and uncertainties, including the risk that the number of Common Shares to be issued is materially higher or lower than as set out herein, or there is a change in the effective date of the Conversion and other risks and uncertainties discussed herein, that could cause actual results to significantly differ from those contemplated by these forward-looking statements. Such statements reflect the view of the Company with respect to future events and are based on information currently available to the Company and on assumptions, which it considers reasonable. Management cautions readers that the assumptions relative to the future events, several of which are beyond management’s control, could prove to be incorrect, given that they are subject to certain risk and uncertainties, and that actual results may differ materially from those projected. Other factors which could cause results or events to differ from current expectations include, among other things, the impact of general economic, industry and market conditions. Management disclaims any intention or obligation to update or revise any forward-looking statements whether as a result of new information, future events or otherwise, except as required by applicable securities laws. The reader is cautioned not to place undue reliance on forward-looking information. The Canadian Securities Exchange has not reviewed, approved or disapproved the content of this news release.


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Press Releases

Overstock Awarded Federal General Services Administration Contract for Commercial E-Marketplace Platform (

Pilot program to allow company to fulfill orders from government agencies for key products

SALT LAKE CITY, June 29, 2020 (GLOBE NEWSWIRE) —, Inc. (NASDAQ:OSTK) was awarded a U.S. government contract with the General Services Administration (GSA) as one of three online retailers providing business-to-business (B2B) e-commerce capabilities for federal agencies. The GSA Commercial e-Marketplace Acquisition contract leverages Overstock’s technology and 20 years of experience in B2B and B2C e-commerce to the government with easy access to furniture, supplies, and other commercial off-the-shelf (COTS) goods.

“We’re thrilled GSA has selected us for this exclusive group of online retailers to participate in this proof-of-concept pilot program which will allow us to supply key items like office furniture and equipment to government agencies,” said Jonathan Johnson, CEO of “The commercial marketplace platforms are to provide the GSA with increased oversight on government spending. We are confident our technology and industry relationships will help make GSA’s goal a reality.”

GSA expects to have the e-commerce platform ready to launch in approximately thirty days. The platform will allow GSA to test the use of commercial e-commerce portals for purchases below the micro-purchase threshold of $10,000 using a proof-of-concept (for up to three years).

As part of GSA’s e-commerce platform, Overstock will help federal agency employees save time and taxpayer dollars on the company’s commercial marketplace and will help GSA streamline its spend data to increase efficiency. According to GSA, open market purchases on government purchase cards represent an estimated $6 billion annually.

The platform will offer a wide assortment of products from established partners, and will allow government personnel to easily find, research, and order necessary COTS products, including many items from small businesses. Along with the commercial e-commerce platform, Overstock will provide concierge customer care service to aid with efficiency and ensure a top-of-the-line experience for government purchasers.

“Our focus has always been to build phenomenal technology and to create the best possible shopping experience for our customers, whether that customer is in the private or government sector,” said Dave Nielsen, President of Overstock Retail. “This GSA contract aligns nicely within our business model and leverages our decades of experience in building e-commerce technology, relationships in the retail industry, and our robust supply chain and site infrastructure. We’re excited for this opportunity and to grow our B2B business.”

Ron Hilton, Overstock’s Chief Sourcing and Operations Officer, added, “We appreciate GSA’s recognition of our expertise and our leadership in e-commerce. Our assortment for this platform will parallel our assortment on ensuring quality products at a great value alongside added benefits for our government clients.”

GSA provides centralized procurement for the federal government, offering billions of dollars’ worth of products, services, and facilities that federal agencies need. This e-commerce platform is part of GSA’s Federal Marketplace Strategy to modernize and simplify the buying and selling experience for customers, suppliers, and acquisition professionals. GSA’s acquisition solutions supply federal purchasers with cost-effective high-quality products and services from commercial vendors. GSA helps federal agencies build and acquire office space, products and other workspace services, and oversees the preservation of historic federal properties. Its policies covering travel, property and management practices promote efficient government operations.

About Overstock, Inc Common Shares (NASDAQ:OSTK) / Digital Voting Series A-1 Preferred Stock (Medici Ventures’ tZERO platform:OSTKO) / Series B Preferred (OTCQX:OSTBP) is an online retailer and technology company based in Salt Lake City, Utah. Its leading e-commerce website sells a broad range of new home products at low prices, including furniture, décor, rugs, bedding, home improvement, and more. The online shopping site, which is visited by tens of millions of customers a month, also features a marketplace providing customers access to millions of products from third-party sellers. Overstock was the first major retailer to accept cryptocurrency in 2014, and in the same year founded Medici Ventures, its wholly owned subsidiary dedicated to the development and acceleration of blockchain technologies to democratize capital, eliminate middlemen, and re-humanize commerce. Overstock regularly posts information about the Company and other related matters on the Newsroom and Investor Relations pages on its website,

O,,, Club O, Main Street Revolution, and Worldstock are registered trademarks of, Inc. Other service marks, trademarks and trade names which may be referred to herein are the property of their respective owners.

This press release contains certain forward-looking statements within the meaning of Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934. Such forward-looking statements include all statements other than statements of historical fact, including but not limited to statements regarding the contract with the General Services Administration and Overstock’s business-to-business operations.  Additional information regarding factors that could materially affect results and the accuracy of the forward-looking statements contained herein may be found in the Company’s Annual Report on Form 10-K for the fiscal year ended December 31, 2019, which was filed with the SEC on March 13, 2020, in our Form 10-Q for the quarter ended March 31, 2020, which was filed with the SEC on May 7, 2020, and in our subsequent filings with the SEC.


Investor Relations:
Alexis Callahan


Overstock Media Relations

Press Releases

iQSTEL’s Subsidiary itsBchain Reaches Preliminary Agreement With Cynopia for Next Phase of Blockchain-based Settlement and Payment Marketplace Platform (

NEW YORK, NY, June 29, 2020 (GLOBE NEWSWIRE) — via NEWMEDIAWIRE – iQSTEL Inc. (OTC: IQST), a leading-edge 21st Century Enhanced Telecommunications Service Provider, is pleased to announce the next phase of our blockchain-based Settlement and Payment Marketplace platform has begun. Cynopia LTD, a London, UK firm, will be providing consulting and development services for this platform.

Cynopia LTD ( is led by Francesco Fiacchi, formerly VP and Director of Mobile VAS WAU Movil (30+ carriers directly connected), business developer for Inspired Broadcast in the UK, and financial consultant for Deloitte, with 25 years experience in IT and Telecom industries. His Linkedin profile:  

Mr. Fiacchi comments: “We are really proud to be part of itsBchain’s blockchain projects. We recently completed several Blockchain development ventures related to the cryptocurrency industry, and we look forward to challenging ourselves and bringing our extensive Blockchain and Artificial Intelligence (AI) knowledge and experience to itsBchain.”

“The blueprint for our Settlement and Payment Marketplace platform has been completed and we look forward to working with Cynopia and Francesco Fiacchi to bring about the production platform. Their expertise in Blockchain and AI complements our goals perfectly. Our estimated ‘go live’ date is sometime in Q1 of 2021,” Mr. Iglesias concluded.

About iQSTEL Inc.:

iQSTEL Inc (OTC: IQST)  is a US-based publicly listed company offering leading-edge 21st Century Enhanced Telecommunications Services with a focus on a wide range of cloud-based enhanced services to the Tier-1 and Tier-2 carriers, corporate, enterprise, as well as the retail market. iQSTEL through its subsidiaries Etelix, SwissLink, QGlobal SMS, SMSDirectos, IoT Labs, IoT Smart Gas Platform, itsBchain offers a “one-stop-shopping” for international and domestic VoIP services, IP-PBX services, SMS exchange for A2P and P2P, OmniChannel Marketing, Internet of Things (IoT) applications (IoT Smart Gas Platform), 4G & 5G international infrastructure connectivity, as well as blockchain-based platforms: Mobile Number Portability Application (MNPA) and Settlement & Payments Marketplace for VoIP, SMS and Data.

About USA, LLC: USA LLC ( is a wholly owned subsidiary of iQSTEL Inc. USA, LLC is a Miami, Florida-based international telecom carrier founded in 2008 that provides telecom and technology solutions worldwide, with commercial presence in North America, Latin America, and Europe. Enabled by its 214-license granted by the Federal Communications Commission (FCC), Etelix provides International Long-Distance voice services for Telecommunications Operators (ILD Wholesale), and Submarine Fiber Optic Network capacity for internet (4G and 5G). Etelix was founded in 2008 and has been profitable since inception. 

About SwissLink Carrier AG:

SwissLink Carrier AG ( is a 51% owned subsidiary of iQSTEL Inc. SwissLink Carrier AG is a Switzerland based international Telecommunications Carrier founded in 2015 providing international VoIP connectivity worldwide, with commercial presence in Europe, CIS and Latin America. SwissLink Carrier AG is a Swiss licensed Operator, having a domestic Interconnect with Swisscom, allowing their international Carrier Customers direct terminations via SwissLink into all Switzerland Fix & Mobile Networks. Since the takeover from Swissphone in November 2018 and the rename into SwissLink, they operate on a profitable level.

About QGlobal SMS LLC.:

QGlobal SMS LLC ( is a 51% owned subsidiary of iQSTEL Inc. QGlobal SMS is a USA based company and a commercial brand founded in 2020 specialized in international and domestic SMS termination, with emphasis on the Applications to Person (A2P) and Person to Person (P2P) for Wholesale Carrier Market and Corporate Market in the US. QGlobal SMS has commercial presence in the US, Mexico, Latin America, EMEA (Europe, Middle East, Asia), and Africa, through our SMS service providers based in Austin, TX and Miami, FL. Our Austin-based SMS service provider is specialized in the SMS traffic exchange between the US and Mexico, and our Miami-based SMS service provider is focused on the development of Latin America and the rest of the word. QGlobal SMS has robust international interconnection with Tier1 SMS Aggregators, guaranteeing its customers high quality and low termination rates, over more than 100 countries worldwide.

About Alcyon Cloud SMS S.A.S (Commercial Brand

Alcyon Cloud SMS S.A.S. (Commercial Brand, is a whole subsidiary of QGlobal SMS, a Colombian-based Application and Content Provider. Alcyon Cloud SMS ( is registered with the Secretary of Information and Communication Technology (ICT) in Colombia, offering services to government, enterprises, small and medium business, as well as end-users. Using SMSDirectos’ existing network, they plan to expand services from SMS to offer omnichannel products and services such as: SMS, Emails, RCS (Rich Communications Services), Social Media Channels (Whats App, Messenger, etc), WebRTC (Web Real-Time Communication), VoIP (IP-PBX, SIP Trunking) ChatBots (Artificial Intelligence Based), SMS to Email, and Email to SMS.

About IoT Labs MX SAPI:

IoT Labs MX SAPI (, a subsidiary of iQSTEL Inc., is an Internet of Things (IoT) Mexican technology development company, creator of the “IoT Smart Gas” Platform and Application. The IoT Smart Gas platform consists of an IoT field device installed on the LP gas tank (adaptable to virtually any gas or liquid storage tank) and, thanks to the Internet of Things (IoT) technology via Sigfox or GSM network connectivity, allows remote managed and improved logistic processes of refilling, usage tracking and tank monitoring in real-time by the Smart Gas mobile app. The new GSM tracking feature allows for mobile use including ground, air, and sea tank monitoring.

About itsBchain LLC.:

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How to detect unwanted bias in machine learning models (

In 2016, the World Economic Forum claimed we are experiencing the fourth wave of the Industrial Revolution: automation using cyber-physical systems. Key elements of this wave include machine intelligence, blockchain-based decentralized governance, and genome editing. As has been the case with previous waves, these technologies reduce the need for human labor but pose new ethical challenges, especially for artificial intelligence development companies and their clients.

The purpose of this article is to review recent ideas on detecting and mitigating unwanted bias in machine learning models. We will discuss recently created guidelines around trustworthy AI, review examples of AI bias arising from both model choice and underlying societal bias, suggest business and technical practices to detect and mitigate biased AI, and discuss legal obligations as they currently exist under the GDPR and where they might develop in the future.

A trustworthy model will still contain many biases because bias (in its broadest sense) is the backbone of machine learning. A breast cancer prediction model will correctly predict that patients with a history of breast cancer are biased towards a positive result. Depending on the design, it may learn that women are biased towards a positive result. The final model may have different levels of accuracy for women and men, and be biased in that way. The key question to ask is not Is my model biased?, because the answer will always be yes.

Searching for better questions, the European Union High Level Expert Group on Artificial Intelligence has produced guidelines applicable to model building. In general, machine learning models should be:

  1. Lawful—respecting all applicable laws and regulations
  2. Ethical—respecting ethical principles and values
  3. Robust—both from a technical perspective while taking into account its social environment

These short requirements, and their longer form, include and go beyond issues of bias, acting as a checklist for engineers and teams. We can develop more trustworthy AI systems by examining those biases within our models that could be unlawful, unethical, or un-robust, in the context of the problem statement and domain.

Historical cases of AI bias

Below are three historical models with dubious trustworthiness, owing to AI bias that is unlawful, unethical, or un-robust. The first and most famous case, the COMPAS model, shows how even the simplest models can discriminate unethically according to race. The second case illustrates a flaw in most natural language processing (NLP) models: They are not robust to racial, sexual and other prejudices. The final case, the Allegheny Family Screening Tool, shows an example of a model fundamentally flawed by biased data, and some best practices in mitigating those flaws.


The canonical example of biased, untrustworthy AI is the COMPAS system, used in Florida and other states in the US. The COMPAS system used a regression model to predict whether or not a perpetrator was likely to recidivate. Though optimized for overall accuracy, the model predicted double the number of false positives for recidivism for African American ethnicities than for Caucasian ethnicities.

The COMPAS example shows how unwanted bias can creep into our models no matter how comfortable our methodology. From a technical perspective, the approach taken to COMPAS data was extremely ordinary, though the underlying survey data contained questions with questionable relevance. A small supervised model was trained on a dataset with a small number of features. (In my practice, I have followed a similar technical procedure dozens of times, as is likely the case for any data scientist or ML engineer.) Yet, ordinary design choices produced a model that contained unwanted, racially discriminatory bias.

The biggest issue in the COMPAS case was not with the simple model choice, or even that the data was flawed. Rather, the COMPAS team failed to consider that the domain (sentencing), the question (detecting recidivism), and the answers (recidivism scores) are known to involve disparities on racial, sexual, and other axes even when algorithms are not involved. Had the team looked for bias, they would have found it. With that awareness, the COMPAS team might have been able to test different approaches and recreate the model while adjusting for bias. This would have then worked to reduce unfair incarceration of African Americans, rather than exacerbating it.

Any NLP model pre-trained naïvely on common crawl, Google News, or any other corpus, since Word2Vec

Large, pre-trained models form the base for most NLP tasks. Unless these base models are specially designed to avoid bias along a particular axis, they are certain to be imbued with the inherent prejudices of the corpora they are trained with—for the same reason that these models work at all. The results of this bias, along racial and gendered lines, have been shown on Word2Vec and GloVe models trained on Common Crawl and Google News respectively. While contextual models such as BERT are the current state-of-the-art (rather than Word2Vec and GloVe), there is no evidence the corpora these models are trained on are any less discriminatory.

Although the best model architectures for any NLP problem are imbued with discriminatory sentiment, the solution is not to abandon pre-trained models but rather to consider the particular domain in question, the problem statement, and the data in totality with the team. If an application is one where discriminatory prejudice by humans is known to play a significant part, developers should be aware that models are likely to perpetuate that discrimination.

Allegheny family screening tool: unfairly biased, but well-designed and mitigated

In this final example, we discuss a model built from unfairly discriminatory data, but the unwanted bias is mitigated in several ways. The Allegheny Family Screening Tool is a model designed to assist humans in deciding whether a child should be removed from their family because of abusive circumstances. The tool was designed openly and transparently with public forums and opportunities to find flaws and inequities in the software.

The unwanted bias in the model stems from a public dataset that reflects broader societal prejudices. Middle- and upper-class families have a higher ability to “hide” abuse by using private health providers. Referrals to Allegheny County occur over three times as often for African-American and biracial families than white families. Commentators like Virginia Eubanks and Ellen Broad have claimed that data issues like these can only be fixed if society is fixed, a task beyond any single engineer.

In production, the county combats inequities in its model by using it only as an advisory tool for frontline workers, and designs training programs so that frontline workers are aware of the failings of the advisory model when they make their decisions. With new developments in debiasing algorithms, Allegheny County has new opportunities to mitigate latent bias in the model.

The development of the Allegheny tool has much to teach engineers about the limits of algorithms to overcome latent discrimination in data and the societal discrimination that underlies that data. It provides engineers and designers with an example of a consultative model building which can mitigate the real-world impact of potential discriminatory bias in a model.

Avoiding and mitigating AI bias: key business awareness

Fortunately, there are some debiasing approaches and methods—many of which use the COMPAS dataset as a benchmark.

Improve diversity, mitigate diversity deficits

Maintaining diverse teams, both in terms of demographics and in terms of skillsets, is important for avoiding and mitigating unwanted AI bias. Despite continuous lip service paid to diversity by tech executives, women and people of color remain under-represented.

Various ML models perform poorer on statistical minorities within the AI industry itself, and the people to first notice these issues are users who are female and/or people of color. With more diversity in AI teams, issues around unwanted bias can be noticed and mitigated before releasing into production.

Be aware of proxies: removing protected class labels from a model may not work!

A common, naïve approach to removing bias related to protected classes (such as sex or race) from data is to delete the labels marking race or sex from the models. In many cases, this will not work, because the model can build up understandings of these protected classes from other labels, such as postal codes. The usual practice involves removing these labels as well, both to improve the results of the models in production but also due to legal requirements. The recent development of debiasing algorithms, which we will discuss below, represents a way to mitigate AI bias without removing labels.

Be aware of technical limitations

Even the best practices in product design and model building will not be enough to remove the risks of unwanted bias, particularly in cases of biased data. It is important to recognize the limitations of our data, models, and technical solutions to bias, both for awareness’ sake, and so that human methods of limiting bias in machine learning such as human-in-the-loop can be considered.

Avoiding and mitigating AI bias: key technical tools for awareness and debiasing

Data scientists have a growing number of technical awareness and debiasing tools available to them, which supplement a team’s capacity to avoid and mitigate AI bias. Currently, awareness tools are more sophisticated and cover a wide range of model choices and bias measures, while debiasing tools are nascent and can mitigate bias in models only in specific cases.

Awareness and debiasing tools for supervised learning algorithms

IBM has released a suite of awareness and debiasing tools for binary classifiers under the AI Fairness project. To detect AI bias and mitigate against it, all methods require a class label (e.g., race, sexual orientation). Against this class label, a range of metrics can be run (e.g., disparate impact and equal opportunity difference) that quantify the model’s bias toward particular members of the class. We include an explanation of these metrics at the bottom of the article.

Once bias is detected, the AI Fairness 360 library (AIF360) has 10 debiasing approaches (and counting) that can be applied to models ranging from simple classifiers to deep neural networks. Some are preprocessing algorithms, which aim to balance the data itself. Others are in-processing algorithms which penalize unwanted bias while building the model. Yet others apply postprocessing steps to balance favorable outcomes after a prediction. The particular best choice will depend on your problem.

AIF360 has a significant practical limitation in that the bias detection and mitigation algorithms are designed for binary classification problems, and need to be extended to multiclass and regression problems. Other libraries, such as Aequitas and LIME, have good metrics for some more complicated models—but they only detect bias. They aren’t capable of fixing it. But even just the knowledge that a model is biased before it goes into production is still very useful, as it should lead to testing alternative approaches before release.

General awareness tool: LIME

The Local Interpretable Model-agnostic Explanations (LIME) toolkit can be used to measure feature importance and explain the local behavior of most models—multiclass classification, regression, and deep learning applications included. The general idea is to fit a highly interpretable linear or tree-based model to the predictions of the model being tested for bias.

For instance, deep CNNs for image recognition are very powerful but not very interpretable. By training a linear model to emulate the behavior of the network, we can gain some insight into how it works. Optionally, human decision-makers can review the reasons behind the model’s decision in specific cases through LIME and make a final decision on top of that. This process in a medical context is demonstrated with the image below.

Explaining individual predictions to a human decision-maker. The model predicts that a patient has the flu based on symptoms or lack thereof. The explainer, LIME, reveals to the doctor the weighting behind each symptom and how it fits the data. The doctor still makes the final decision but is better informed about the model's reasoning. Based on an image made by Marco Tulio Ribeiro

Debiasing NLP models

Earlier, we discussed the biases latent in most corpora used for training NLP models. If unwanted bias is likely to exist for a given problem, I recommend readily available debiased word embeddings. Judging from the interest from the academic community, it is likely that newer NLP models like BERT will have debiased word embeddings shortly.

Debiasing convolutional neural networks (CNNs)

Although LIME can explain the importance of individual features and provide local explanations of behavior on particular image inputs, LIME does not explain a CNN’s overall behavior or allow data scientists to search for unwanted bias.

In famous cases where unwanted CNN bias was found, members of the public (such as Joy Buolamwini) noticed instances of bias based on their membership of an underprivileged group. Hence the best approaches in mitigation combine technical and business approaches: Test often, and build diverse teams that can find unwanted AI bias through testing before production.

In this section, we focus on the European Union’s General Data Protection Regulation (GDPR). The GDPR is globally the de facto standard in data protection legislation. (But it’s not the only legislation—there’s also China’s Personal Information Security Specification, for example.) The scope and meaning of the GDPR are highly debatable, so we’re not offering legal advice in this article, by any means. Nevertheless, it’s said that it’s in the interests of organizations globally to comply, as the GDPR applies not only to European organizations but any organizations handling data belonging to European citizens or residents.

The GDPR is separated into binding articles and non-binding recitals. While the articles impose some burdens on engineers and organizations using personal data, the most stringent provisions for bias mitigation are under Recital 71, and not binding. Recital 71 is among the most likely future regulations as it has already been contemplated by legislators. Commentaries explore GDPR obligations in further detail.

We will zoom in on two key requirements and what they mean for model builders.

1. Prevention of discriminatory effects

The GDPR imposes requirements on the technical approaches to any modeling on personal data. Data scientists working with sensitive personal data will want to read the text of Article 9, which forbids many uses of particularly sensitive personal data (such as racial identifiers). More general requirements can be found in Recital 71:

[. . .] use appropriate mathematical or statistical procedures, [. . .] ensure that the risk of errors is minimised [. . .], and prevent discriminatory effects on the basis of racial or ethnic origin, political opinion, religion or beliefs, trade union membership, genetic or health status, or sexual orientation.

GDPR (emphasis mine)

Much of this recital is accepted as fundamental to a good model building: Reducing the risk of errors is the first principle. However, under this recital, data scientists are obliged not only to create accurate models but models which do not discriminate! As outlined above, this may not be possible in all cases. The key remains to be sensitive to the discriminatory effects which might arise from the question at hand and its domain, using business and technical resources to detect and mitigate unwanted bias in AI models.

2. The right to an explanation

Rights to “meaningful information about the logic involved” in automated decision-making can be found throughout GDPR articles 13-15… Recital 71 explicitly calls for “the right […] to obtain an explanation” (emphasis mine) of automated decisions. (However, the debate continues as to the extent of any binding right to an explanation.)

As we have discussed, some tools for providing explanations for model behavior do exist, but complex models (such as those involving computer vision or NLP) cannot be easily made explainable without losing accuracy. Debate continues as to what an explanation would look like. As a minimum best practice, for models likely to be in use into 2020, LIME or other interpretation methods should be developed and tested for production.

Ethics and AI: a worthy and necessary challenge

In this post, we have reviewed the problems of unwanted bias in our models, discussed some historical examples, provided some guidelines for businesses and tools for technologists, and discussed key regulations relating to unwanted bias.

As the intelligence of machine learning models surpasses human intelligence, they also surpass human understanding. But, as long as models are designed by humans and trained on data gathered by humans, they will inherit human prejudices.

Managing these human prejudices requires careful attention to data, using AI to help detect and combat unwanted bias when necessary, building sufficiently diverse teams, and having a shared sense of empathy for the users and targets of a given problem space. Ensuring that AI is fair is a fundamental challenge of automation. As the humans and engineers behind that automation, it is our ethical and legal obligation to ensure AI acts as a force for fairness.

Further reading on AI ethics and bias in machine learning

Books on AI bias

Machine learning resources

AI bias organizations

Debiasing conference papers and journal articles

Definitions of AI bias metrics

Disparate impact

Disparate impact is defined as “the ratio in the probability of favorable outcomes between the unprivileged and privileged groups.” For instance, if women are 70% as likely to receive a perfect credit rating as men, this represents a disparate impact. The disparate impact may be present both in the training data and in the model’s predictions: in these cases, it is important to look deeper into the underlying training data and decide if disparate impact is acceptable or should be mitigated.

Equal Opportunity Difference

Equal opportunity difference is defined (in the AI Fairness 360 article found above) as “the difference in true positive rates [recall] between unprivileged and privileged groups.” The famous example discussed in the paper of high equal opportunity difference is the COMPAS case. As discussed above, African-Americans were being erroneously assessed as high-risk at a higher rate than Caucasian offenders. This discrepancy constitutes an equal opportunity difference.

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