THE EDGE: AI-Based Anti-Corruption Auto Processing Software That YOU Can Build Your Own Copy Of. FORK THIS!

 

SOFTWARE REVEALED THAT EXPOSES CROOKS IN PUBLIC OFFICE

- New open-source, and free, public software let's any citizen get any corrupt official arrested. Any voter can use the software from the comfort of their living room. The AI replicates itself (Like A benign digital version of Covid) across the entire web.

- You can download a copy of the software or build-your-own version of it from freely available code at Github, CERN and Linux repositories.

- We have consulted to the SEC and the GAO on this technology.

- After suffering millions of dollars of losses from public official's Insider Trading schemes, we decided to do something about that!

 

Illegal and corrupt Congressional insider trading tends to be something we don't hear about until it's hit the big news networks and newspapers as the SEC goes for the throat of the accused. By then, unfortunately, those committing it have made their gains, usually in the multi-millions of dollars, and the damage has been done to the stock, its company, investors and the American Way. Covert stock market trades are now the #1 form of bribes in California and Washinton, DC.

Quite frankly, the jail time assessed doesn't correct the damage done, and the fines rarely aid the investors, or the voters, in getting their money and their democracy back. Many of those hurt are Average Joe's and Jill's who were just trying to save their retirement nest eggs. Shame is the tool that works best on the corrupt!

These crimes involve an investment banker spouse and a Senator or other top official, using information, which was not available to the public, buying and selling a company's stock in an underhanded manner. In many cases bribes have been paid with Google, Tesla or Facebook stock in a covert manner. It is particularly onerous when a Senators buys Tesla, Google, Facebook or Solyndra stock, and makes laws that only benefits Tesla, Solyndra, etc, while sabotaging their competitor constituents. Because the dealings involved are pretty much done on the sly, it's been difficult, until now, for the governing body of the SEC to prove illegal insider trading, unless one of the cohorts tattles on the others or their actions become glaringly obvious. In some cases, a sharp mind around the action may take notice and become what's called a whistle-blower.

Previously, writes Andrew Beattie of Investopedia: "... insider trading is often difficult for the SEC to spot. Detecting it involves a lot of conjecture and consideration of probabilities." That was the 'old days', though. Today, the new AI software can bust through these scams like a hot knife through butter!

With this new open-source, free, public spy agency-class software, detecting illegal insider trading is actually less complicated than it sounds.

To the eyes of this new super-powerful AI observer server bot and peer-to-peer databases, it is easy work.

You, the citizen, just type the politician or agency employee name into a field and hit the "analyze" button. A few minutes later you receive a multi-page PDF report similar to an FBI report on the target. You can either research the subject in more detail or send copies of the report to the FBI, GAO, OSC, SEC or other enforcement group.

The software is an automated AI temporal matching system which includes 24/7 analysis of all stock trades involving politicians to its information source, politician finances, communications and policy participators. it uses some of the same software code used by the CERN mega-research center in Switzerland.

The technology Core Evaluation Points:

  • Analyst estimates - these come from what an analyst estimates that a company's quarterly or annual earnings will be. They are important because they help approximate the fair value of an entity, which basically establishes it price on the stock exchange.
  • Share volume - this reflects the quantity of shares that can be traded over a certain period of time. There are buyers and there are sellers, and the transactions that take place between them contribute to total volume.

One Way The AI Detects Congressional Insider Trades

Metricized signs of illegal insider trading occur when trades occur that break out of the historical pattern of share volume traded compared to beneficiary participation's of those connected to company and political entity. Another clue of the illegal insider trading is when a lot of trading goes on right before earnings announcements. That tends to be a sign that someone already knows what the announcement is going to indicate, and it's an obvious violation. One module of the new software hunts these trends around-the-clock in an unmanned manner like a detective who never needs to sleep.

The software red alerts are issued when trades are linked closer to the actual earnings and politicians bills instead of what the predicted earnings were. In a corruption case, it's clear the trades - especially made by politicians close to the company - stemmed from information that was not readily available to the general public.

In other words, at the time an insider makes a trade, the trade has a stronger relationship to earnings guidance rather than to earnings results achieved.

Part Of The Insider Trading Detection AI Uses 'Dynamic Time Warping (DTW)'

In econometrics, which is a concept frequently used by quantitative analysts to evaluate stock market prices, dynamic time warping (DTW) is an algorithm that can be used for measuring similarity between two data sequences by calculating an optimal match between the two. This sequence "matching" method is often used in time series classification to properly "line things up."

The method, coupled with AI machine learning ensemble methods, can provide a clear path between the trades made by insiders and public data used to make the trades.

This is a product of artificial intelligence that has been expanded by Indexer, Splunk, Palantir and other firms fast becoming experts in products that can be used to advance the art of manipulating political and social trends in business and markets by using social media, financial data and news stories. The new software process has taken that sort of approach to the next level and targeted every member of Congress, their staff, family and friends. The first emphasis is on California and Washington, DC public figures.

In a hypothetical example, a group of executives failed to trade by industry standards by leveraging material non-public information and policy manipulation. Although consensus estimates called for higher commodity prices at the end of 2015, it appears key executives traded for their personal accounts as a result of the forecast provided by a specialist system within the firm that was adept at predicting prices alongside lobbyist manipulations. Flash-boy trading is now dirtier and powered by Google-class server systems.

In the hypothetical scenario the software aggregates executive trades in 2014 and 2015 and finds a strong link between buys and sells of executive stock options, which line up with material non-public estimates of commodity prices that were provided by the specialist system.

For example, in a "Exec Sell and Exec Buys" graph, a green line represents sells, while a black line represents buys. In the corresponding period, one finds a red line represents unrevised prices provided by the specialist system, and green line represents consensus estimates.

During Q1-2014, there was $28M in purchases of executive stock options, while in Q2-2014, there was $25M in sales of executive stock options. The specialist system called for Q3-2014 commodity prices to make a precipitous decline going into the end of 2014. Remember, under this scenario, no revisions were made to the specialist systems' price forecast. In this example, executives were afforded a significant advantage using price predictions from the specialist system.

In a final bullet chart, there was a dynamic time warping distance between trades and consensus estimates of 7.23, but this distance is only 2.19 when comparing specialist system estimates and executive trades. Please note, the closer the distance score is to zero, the more similar the trades are to the estimates they are measured against.

We have applied this process to companies well-known for influence buying like, Google, Tesla and Facebook

It's obvious that the tech executives involved did not follow industry standards in their actions and make public the "insider" information they had access to prior to the trades they made. The lobbyists they hired promoted this rigged trend and paid off Senators with perks. These are the kind of violations the SEC and other governing bodies can look to in attempting to protect the trading public and the integrity of financial marketplaces. Artificial intelligence tools are a major factor in assisting the tracking of insider trading.

"Every facet of our everyday lives has been impacted, infiltrated and greatly influenced by artificial intelligence technologies," says Vernon A. McKinley, a multi-jurisdictional attorney, based in Atlanta. "In fact, the U.S. government and its multiple agencies have developed specialized intelligence units to detect, track, analyze and prosecute those unscrupulous individuals seeking to profit from the use of such tools, specifically in the financial industry, and to protect the integrity and strength of the U.S. economy and its investors." Now these tools are being turned against the corrupt!

The public can now detect trading anomalies in financial situations using this artificial intelligence software on their desktop computers. No public official will ever be able to do these kinds of corruptions, again, without getting caught.

This approach has already had an impact on how political insiders trade on Wall Street and in financial markets around the world.

This technology can end this corruption forever!

A module of the software uses data from The Center for Responsive Politics, ICIJ Panama Leaks records, Swiss Leaks records and FEC files to reveal covert routes. Famous politicians own part of Tesla Motors, Facebook, Google, Netflix, YouTube and other companies they helped get government money for. All of their competing constituents have suffered for it or been put out of business by exclusive deals that only Tesla Motors, Facebook, Google, Netflix and YouTube got. That is a crime and charges have been filed with federal law enforcement.

A large volume of forensic research proves that Silicon Valley Cartel tech firms receive benefits from politicians and politicians,at the same time, benefit from these firms.

This evidence on the exchange of benefits between politicians and firms proves an agreement between the politicians and the companies. This agreement, however, cannot be in the form of a written contract as writing direct fee-for-service contracts between a politician and a firm is considered bribery (Krozner and Stratmann 1998; 2000). In addition, either party to this agreement might renege on its promise and the other party cannot resort to the courts.

Procon.org, for example, reports: “Less than two months after ascending to the United States Senate, and before becoming President, one Senator bought more than $50,000 worth of stock in two speculative companies whose major investors included some of his biggest political donors. One of the companies was a biotech concern that was starting to develop a drug to treat avian flu. In March 2005, two weeks after buying thousands of dollars of its shares, this Senator took the lead in a legislative push for more federal spending to battle the disease. The most recent financial disclosure form this Senator . . . shows that he bought more than $50,000 in stock in a satellite communications businesswhose principal backers . . . had raised more than $150,000 for his political committees.” See more examples from the Citizens for Responsibility and Ethics in Washington (CREW) report (2009).)

The literature and eye-witness experience proves that politically-connected Silicon Valley tech firms monthly obtain economic favors, such as securing favorable legislation, special tax exemptions, having preferential access to finance, receiving government contracts, or help in dealing with regulatory agencies. The evidence proves that Google's support, for example, can help in winning elections. For example, firms can vary the number of people they employ, coordinate the opening and closing of plants, and increase their lending activity in election years in order to help incumbent politicians get re-elected. (SeeRoberts 1990; Snyder 1990; Langbein and Lotwis 1990; Durden, Shorgen, and Silberman 1991; Stratmann 1991, 1995, and 1998; Fisman 2001;Johnson and Mitton 2003; Ansolabehere, Snyder, and Ueda 2004; Sapienza 2004, Dinç 2005; Khwaja and Mian 2005; Bertrand, Kramarz,Schoar, and Thesmar 2006; Faccio 2006; Faccio, Masulis, and McConnell 2006; Jayachandran 2006; Leuz and Oberholzer-Gee 2006; Claessens,Feijen, Laeven 2008; Desai and Olofsgard 2008; Ramanna 2008;Goldman, Rocholl, and So 2008, 2009; Cole 2009; Cooper, Gulen, and Ovtchinnikov 2009; Correia 2009; Ramanna and Roychowdhury 2010;Benmelech and Moskowitz 2010.)

The software can see that the share ownership of politicians serves as a mechanism to quid-pro-quo their relationships with big tech firms, is as follows: The ownership of politicians plays multiple distinct (but not necessarily independent) roles; one that relies upon the amount of ownership and one that does not. First, as investors in firms, politicians tie their own interests to those of the firm. Thus, harming (benefiting) the firm means harming (benefiting) the politician and vice versa. By owning a firm's stock, politicians commit their personal wealth to the firm and reduce a firm’s uncertainty with regard to their actions toward the firm. This will,in turn, enhance the firm's incentive to support the politician-owner during both current and future elections in order to prolong the incumbency period for as long as possible. Firms have their lobbyists push to be able to know the amount of ownership likely to be material to politicians. This knowledge, in turn, enables them to judge whether the politician’s interest is aligned with the firm’s interest and optimize quid-pro-quo.

The Political Action Committee (PAC) contribution of firms (which is a direct measure of benefits flowing from firms to politicians) is a significant determinant of ownership allocations by members of Congress. The ownership of Congress members in firms that contribute to their election campaigns is roughly 32.8% higher than their ownership in noncontributing firms even after accounting for factors that are associated with both ownership and contribution (such as familiarity, proximity and investor recognition).

The committee assignments of politicians is a proxy for whether their relations with firms are enforced (Krozner and Stratmann 1998). Silicon Valley tech firms like Facebook, Tesla and Google obtain private benefits out of their mutual relations with politicians. When the strength of the association between ownership and contributions at the firm level increases, the provision of government contracts to those firms increases.

Members of Congress, candidates for federal office, senior congressional staff, nominees for executive branch positions, Cabinet members, the President and Vice President, and Supreme Court justices are required by the Ethics in Government Act of 1978 to file annual reports disclosing their income, assets, liabilities, and other relevant details about their personal finances.

Personal financial disclosure forms are filed annually by May 15 and cover the preceding calendar year. The Center for Responsive Politics (CRP) collected the 2004–2007 reports for Congress members from the Senate Office of Public Records and the Office of the Clerkof the House. The Center then scanned the reports as digital images, classified the politicians’ investments into categories including stocks, bonds, and mutual funds, and built a database accessible via a web query.

Using CRP's data, you can use the software to collect the shares in S&P 500 firms held by members of Congress between 2004 and 2007, for example.You can collect the stock ownership data for every firm that joined the S&P 500 Index any time between January 2004 and April 2009;regardless of when it joined the index,  and the software can obtain all the available stock ownership data for that firm between 2004 and 2007. Likewise, if a firm dropped out of the index at any time during 2004–2008, the software, nevertheless, will retain the firm in a sample for the target period. As such, the sample would include stocks in hundreds of unique firms owned by politicians between 2004 and 2007, for example.

Politicians are required to report only those stocks whose value exceeds $1,000 at the end of the calendar year or that produce more than $200 in income. They are CURRENTLY not required to report the exact value of the holding, but instead must simply check a box corresponding to the value range into which the asset falls. The CRP then undertakes additional research to determine the exact values of these stocks. When the Center makes these determinations, it reports them instead of the ranges and I use these values in my study. When only the range is available, you should use its midpoint as the holding's value. You would, thus have data on the stock holdings of hundreds politicians for that time period.

Using the software, you can search for all Political Action Committees (PACs) associated with tech firms. It then collects data on each contribution these PACs made to candidates (both the winners and losers) running for the Senate and House elections. Tricky corrupt Silicon Valley firms establish several PACs, each in a different location, and each of these PACs can contribute to the same candidate. In such cases, the software would total, for each candidate, every contribution he or she received from PACs affiliated with the same firm. To parallel the investment data sample period, for example, the software collects every contribution made from the 2003–2004 cycle up to and including the 2007–2008 cycle. Many Silicon Valley tech firms use deeply covert Fusion GPS, Perkins Coie, BlackCube, Psyops-type service to make very hidden additional payola payments to California politicians.

For sources, for example, the software collects government contract data from Eagle Eye Publishers, Inc., one of the leading commercial providers of Federal procurement and grant business intelligence and http://www.usaspending.org. Eagle Eye collects its contract data from Federal Procurement Data System–Next Generation (FPDS-NG), the contract data collection and dissemination system administered by the U.S. General Services Administration (GSA). FPDS-NG provides data on procurement contracts awarded by the U.S. Government. When these contracts are awarded to company subsidiaries, Eagle Eye searches for the names of their parent companies and assigns each subsidiary to its appropriate parent. The software collects both the number and aggregate value of government contracts that were awarded to sample firms between 2004 and 2007 in this example time-frame..

The software reveals, for example, that one Representative is a ten-term member of Congress and a senior member of the House Financial Services Committee. They arranged a meeting between the Department of Treasury and One United Bank, a company with close financial ties to themselves, involving both investments and contributions.

“In September 2008, the Representative asked then-Secretary of the Treasury Henry Paulson to hold a meeting for their friends in banks that had suffered from Fannie Mae and Freddie Mac losses.

The Treasury Department complied and held a session with approximately a dozen senior banking regulators, representatives from those banks, and their trade association. Officials of One United Bank have close ties to the Representative and attended the meeting along with the Representative's chief of staff. Kevin Cohee, chief executive officer of One United, used the meeting as an opportunity to ask for bailout funds.

. . . Former White House officials stated they were surprised when One United Officials asked for bailout funds. . . . In December 2008, the Representative intervened again, asking Treasury to host another meeting to ensure their banks received part of the $700 billion allocated under the Troubled Asset Relief Program. . . . Within two weeks, on December 19, 2008, One United secured $12.1million in bailout funds. . . . This was not the first time the Representative used their position to advance the interests of the bank. the Representative's spouse became a shareholder in One United in 2001, when it was known as the Boston Bank of Commerce. In 2002, Boston Bank of Commerce tried to purchase Family Savings, a friend of the Representative in Los Angeles. Instead, Family Savings turned to a bank in Illinois. The Representative tried to block the merger by contacting regulators at the FDIC. The Representative publicly stated they did not want a major bank to acquire a bank that the Representative was friends with.

When the Representative's efforts with the FDIC proved fruitless, the Representative began a public pressure campaign with other community leaders. Ultimately,when Family Savings changed direction and allowed Boston Bank of Commerce to submit a winning bid, the Representative received credit for the merger. The combined banks were renamed One United. . . . In March 2004, the Representative acquired One United stock worth between $250,001 and $500,000, and the Representative's spouse purchased two sets of stock, each worth between $250,001 and $500,000. In September 2004, the Representative sold their stock in One United and their husband sold a portion of his. That same year, the husband joined the bank’s board. . . . One United Chief Executive Kevin Cohee and President Teri Williams Cohee have donated a total of $8,000 to the Representative's campaign committee. . . .On October 27, 2009, less than two months before One United received a $12 million bailout, the bank received a cease-and-desist order from the FDIC and bank regulatory officials in Massachusetts for poor lending practices and excessive executive compensation . . . the bank provided excessive perks to its executives, including paying for Mr. Cohee’s use of a $6.4 million mansion . . .” (Ref: CREW report 2009,pp. 123–125)

Thanks to Crony quid-pro-quo revelations by an earlier version of the software, you can also see that Fisker Automotive, Inc.'s $529 Million U.S. Taxpayer Loan Approval by the Department of Energy was dirty. Fisker Automotive's Chief Operating Officer Bernhard Koehler pleaded with the Department of Energy in a panicked Saturday midnight hour email to receive a $529 million loan as the company was 2 weeks from Chapter 7 liquidation, that it was laying off most of its employees, that no private sector investors would fund the company without DOE guarantees, and that Fisker was unable to raise any further equity funding from independent private-sector investors given the company's financial condition.These statements were made to a Loan Officer at the DOE . No private sector Loan underwriting (approval) committee would ever grant a low interest loan to a desperate buyer that had just confessed it was in a state of insolvency and was about to layoff most of its staff. Yet within a few weeks the DOE would approve a $529 Million Credit Facility to Fisker. Despite the DOE Loan Officer official's sworn testimony at April 24th's House Oversight Committee that the DOE used "same private sector underwriting standards when approving Fisker and other approved Taxpayer Funded Loans" - likely perjury based in documents.

In a 'U.S. GOVERNMENT CONFIDENTIAL EMAIL': FISKER AUTOMOTIVE: August 2009: Co-Founder Bernhard Koehler emails U.S. Dept. of Energy Loan Officer in Sat. midnight Panic admitting VC Firms all declined to invest, and company is out of cash. Weeks later the U.S.Department of Energy approves $529M U.S. Taxpayer Funded Loans to FISKER. NO PRIVATE SECTOR Lender would every authorize a Loan for even $5 Million let alone $529 Million after receiving this email stating private sector investors had examined the company and declined equity investments, that they might loan money as more secure Debt, and the Chief Operating Officer of the company further stating that the borrower is totally insolvent. (Weeks after this email the U.S. Federal Government Dept. of Energy Loan Committee Approves Fisker Automotive as a credit-worthy borrow for $529 Million in U.S. Taxpayer Funded Loans). Fisker got the cash because President Obama said to "give it to them" in order to please his campaign financiers.

The same thing happened with Tesla Motors. Elon Musk and Tesla Motors were broke when DOE gave them the money.

PrivCo CEO Sam Hamadeh stated in an official statement: “The documents obtained by PrivCo paint a picture of how an insolvent,unproven automaker received $192 million in taxpayer funding. The Department of Energy made a loan that no rational lender would have made. This loan was the equivalent of staying execution on a company that was terminally ill to begin with." Tesla and Fisker could not have been taxpayer funded unless bribes and criminal quid-pro-quo was underway by President Obama and the U.S. Senator insider traders.

Since its ruling in Buckley v. Valeo, the U.S. Supreme Court has expressed concern regarding corruption or the appearance of corruption stemming from political quid pro quo arrangements and the deleterious consequences it may have on citizens’ democratic behavior. However, no standard has been set as to what constitutes “the appearance of corruption,” as the Court was and continues to be vague in its definition. As a result, campaign finance cases after Buckley have relied on public opinion polls as evidence of perceptions of corruption, and these polls indicate that the public generally perceives high levels of corruption in government. The present study investigates the actual impact that perceptions of corruption have on individuals’ levels of political participation. Adapting the standard socioeconomic status model developed most fully by Verba and Nie (1972), an extended beta-binomial regression estimated using maximum likelihood is performed, utilizing unique data from the 2009 University of Texas’ Money and Politics survey. The results of this study indicate that individuals who perceive higher levels of quid pro quo corruption participate more in politics, on average, than those who perceive lower levels of corruption.

 

SOURCE CODE RESOURCES FOR YOU TO FORK:

-Spoke: Spoke is a peer-to-peer texting platform for collaborative investigation with several forks under active development.
-Pollaris, A polling location lookup tool modified to track bad guys. You can integrate this with your website and other tools. An API is provided.
-Caucus App: A way to quickly calculate citizen and pro member evidence sets and report results from each investigator.
-Switchboard (FE and BE): This software takes new potential volunteers, or "hot leads," from your online channels and assigns them to state or section-based based volunteer leads for personal follow up calls offering ways to get involved with the investigations. This is also a great tool for investiagtion team recruitment.
-Automated organizing email: Your teams can work together to scale email outreach to the widest possible audience and bypass any cover-up.
-Redhook: Investigations run on data, and redhook is a tool that makes data happen. As a system, Redhook ingests web hook data and delivers it to Redshift/Civis in near real time.
-I90: This tool makes a long file name or hard to remember legal evidence document into a short, easy-to-remember, link.
- opendata.cern.ch: The CERN Database Open Source
- https://github.com: One of the collaborative development nets
- https://citizensleuths.com: An example of over 1000 public forensic groups working on crowd-sourced crime-fighting

You are building a forensic anti-corruption version of XKEYSCORE and submitting your results reports to law enforcement and news outlets. Simply look in torrents and code databases like GITHUB, and similar sites, for forensic database and mass collaboration code and you will have a working module up in no time at all if you are a Tier 2 coder, or better.

 

Summary
Amateur Web Sleuths are Solving Cold Cases, You can Too!  Read How
Article Name
Amateur Web Sleuths are Solving Cold Cases, You can Too! Read How
 
Description
The Skeleton Crew: How Amateur Sleuths Are Solving America's Coldest Cases, by Deborah Halber, describes how amateur web sleuths are helping with cold cases.
 
Author
TEAM1

Author: TEAM1

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