There was a personnel earthquake in the summer of 2020 at the Teachers’ Retirement System in Springfield.
Ultimately, five high-ranking employees were removed from their positions, including executive director Richard Ingram. The tumult generated clouds of uncertainly that only recently started to clear, revealing improper and possibly criminal behavior.
Although mum at first, TRS officials recently released their first lengthy statement about what occurred, disclosing that a new employee purposely maintained a conflict of interest that he falsely claimed to have ended.
The OEIG report states the scandal dates back to 2018, when the TRS “began the process of constructing a new pension system that it called the Gemini Project.” Urbanek said the Gemini system recently went online.
That required hiring outside information technology professionals. Singh and his company — Singh 3 Consulting — were initially hired as a contractor. But in 2019, the TRS hired Singh as a permanent employee, the hiring predicated on Singh terminating his relationship with his company.
He told the TRS he had done so. But no one apparently ever checked, because subsequent investigations revealed Singh remained president and chief executive officer.
Earlier this year we repeated our analysis. This time we expanded it to cover a wider selection of open access journals, anticipating researchers and journals would be taking steps to prevent such errors appearing in their supplementary data files.
We were shocked to find in the period 2014 to 2020 that 3,436 articles, around 31% of our sample, contained gene name errors. It seems the problem has not gone away, and is actually getting worse.
Author(s): Mark Ziemann, Deakin University and Mandhri Abeysooriya, Deakin University
There has been significant disruption in how organisations conduct business and the way we work over the past year and a half. However, financial modellers and developers have had to continue to build, refine and test their models throughout these unprecedented times. Figure 1 below summarises the areas we have covered in the blog series and how they fit together to form the practical guidance of how to follow and implement the Financial Modelling Code.
In 2016, Mark Ziemann and his colleagues at the Baker IDI Heart and Diabetes Institute in Melbourne, Australia, quantified the problem. They found that one-fifth of papers in top genomics journals contained gene-name conversion errors in Excel spreadsheets published as supplementary data2. These data sets are frequently accessed and used by other geneticists, so errors can perpetuate and distort further analyses.
However, despite the issue being brought to the attention of researchers — and steps being taken to fix it — the problem is still rife, according to an updated and larger analysis led by Ziemann, now at Deakin University in Geelong, Australia3. His team found that almost one-third of more than 11,000 articles with supplementary Excel gene lists published between 2014 and 2020 contained gene-name errors (see ‘A growing problem’).
Simple checks can detect autocorrect errors, says Ziemann, who researches computational reproducibility in genetics. But without those checks, the errors can easily go unnoticed because of the volume of data in spreadsheets.
Have you ever built a perfect financial model without any errors? Thought not! And for that reason, all good modellers know they need to include some error checks. But what is not as clear is how many error checks you should have, when you should include them and what form they should take. Excel “helpfully” provided us with functions like ISERR, ISERROR and IFERROR but as you progress your modelling journey you should learn to avoid these functions. Plus, you also learn the sad truth that Excel can’t even do basic maths sometimes! Join us to hear from financial modelling specialist Andrew Berg, who has spent years building models, and so happily admits he has probably already made most of the mistakes you haven’t yet had a chance to! The good news is that he is willing to share the tips he has learned about the right types of error checks to add to your models so you don’t have to learn the hard way. ★Download the resources here ► https://plumsolutions.com.au/virtual-… ★Register for more meetups like this ► https://plumsolutions.com.au/meetup/ ★Connect with Andrew on Linkedin ► https://www.linkedin.com/in/andrew-be…
Not all 10% increases are created equal. And by that we mean, assumption effects are often more impactful in one direction than in the other. Especially when it comes to truncation models or those which use a CTE measure (conditional tail expectation).
Principles-based reserves, for example, use a CTE70 measure. [Take the average of the (100% – 70% = 30%) of the scenarios.] If your model increases expense 3% across the board, sure, on average, your asset funding need might increase by exactly that amount. However, because your final measurement isn’t the average across all the scenarios, but only the worst ones, it’s likely that your reserve amounts are going to increase by significantly more than the average. You might need to run a few different tests, at various magnitudes of change, to determine how your various outputs change as a function of the volatility of your inputs.
When the Defense Information Systems Agency sought a new satellite services acquisition on behalf of the Navy, it included a spreadsheet so bidders could fill in their prices. But the spreadsheet included the prices from the current contract, which were supposed to be inaccessible. For how things turned out, Smith Pachter McWhorter procurement attorney Joe Petrillo joined Federal Drive with Tom Temin.
Joe Petrillo: Sure. This is another excel spreadsheet disaster, and we talked about one a few weeks ago. It involved an acquisition of satellite telecom services for the Navy’s Military Sealift Command. It was an acquisition of commercial satellite telecommunications services. And they were divided into both bandwidth and non-bandwidth services. And the contract would be able to run to for up to 10 years in duration. Part of the contract, as you said, was an excel spreadsheet of the various different line items with blanks for offers to include their price. Unfortunately, this spreadsheet had hidden tabs, 19 hidden tabs, and those included, among other things, historical pricing information from the current contract. So Inmarsat, which was the incumbent contractor, holding that contract, notified the government and said, look you’ve disclosed our pricing information, do something about it. So the government deleted the offending spreadsheet from the SAM.gov website. But they understood and this was the case, third party aggregators had already downloaded it, and it was out there, it was available.
Somewhere in PHE’s data pipeline, someone had used the wrong Excel file format, XLS rather than the more recent XLSX. And XLS spreadsheets simply don’t have that many rows: 2 to the power of 16, about 64,000. This meant that during some automated process, cases had vanished off the bottom of the spreadsheet, and nobody had noticed.
The idea of simply running out of space to put the numbers was darkly amusing. A few weeks after the data-loss scandal, I found myself able to ask Bill Gates himself about what had happened. Gates no longer runs Microsoft, and I was interviewing him about vaccines for a BBC program called How to Vaccinate The World. But the opportunity to have a bit of fun quizzing him about XLS and XLSX was too good to pass up.
I expressed the question in the nerdiest way possible, and Gates’s response was so strait-laced I had to smile: “I guess… they overran the 64,000 limit, which is not there in the new format, so…” Well, indeed. Gates then added, “It’s good to have people double-check things, and I’m sorry that happened.”
Exactly how the outdated XLS format came to be used is unclear. PHE sent me an explanation, but it was rather vague. I didn’t understand it, so I showed it to some members of Eusprig, the European Spreadsheet Risks Group. They spend their lives analyzing what happens when spreadsheets go rogue. They’re my kind of people. But they didn’t understand what PHE had told me, either. It was all a little light on detail.
The calculation error that upended the state’s largest pension fund has been traced back to a single month in 2015, according to an investigation from Spotlight PA.
The discovery came to light in a trove of documents obtained by reporters that found a tiny discrepancy that boosted the $64 billion Public School Employees Retirement System (PSERS) by a third of a percentage point in April of that year.
The consultant firm hired to review PSERS’ investment returns between 2011 and 2020, ACA Compliance Group, performed limited checks that skipped over the month in question, according to the report. The company that crunched the actual numbers, Aon, blamed the discrepancy on a data entry error.
No matter the fault, the miscalculation unraveled PSERS’ rate of return, dropping it from just above the mandated 6.36% threshold to prevent a contribution increase down to 6.34%. Now, about 100,000 workers who joined the system in 2011 or later will pay more beginning on July 1.
One of the reasons the Fastly outage seems so wide scale is that cloud computing service companies like Fastly are consolidating, leaving websites dependent on a shrinking number of providers. Even if there aren’t that many total outages, the fact that so many everyday sites rely on fewer cloud providers makes each individual outage feel pretty significant to an average internet user who just wanted to buy some stuff on Amazon and read the New York Times early Tuesday morning.
There are benefits to consolidation, explains Doug Madory, the head of internet analysis at the network monitoring company Kentik. For instance, a smaller number of cloud providers means it’s much easier to get those providers to deploy a particular security change. “The flip side is the liability [of] having a few megacompanies, whether they’re CDNs or other types of internet firms, responsible for a lot of our internet activities,” Madory told Recode.
In other words, when one of these megacompanies updates its systems and inadvertently causes an outage, the damage radius could be quite wide. This is what happened in 2011 when one of Amazon’s cloud computing systems, Elastic Block Store (EBS), crashed and brought Reddit, Quora, and Foursquare offline. After the incident, Amazon explained that engineers inadvertently caused technical problems that trickled down through its systems and caused the outage.
Less than half a year into the Biden Presidency, the Internal Revenue Service is already at the center of an abuse-of-power scandal. That news broke Tuesday when ProPublica, a website whose journalism promotes progressive causes, published information from what it said are 15 years of the tax returns of Jeff Bezos, Warren Buffett and other rich Americans.
Leaking such information is a crime, since under federal law tax returns are confidential. ProPublica says it received the files from “an anonymous source” and doesn’t know who provided them, how they were obtained, or what the source’s motives are.
Allow us to fill in that last blank. The story arrives amid the Biden Administration’s effort to pass the largest tax increase as a share of the economy since 1968. The main Democratic argument for a tax hike is that the rich should pay their “fair share.” The ProPublica story is a long argument that somehow the rich don’t pay enough. The timing here is no coincidence, comrade.
This still leaves the real scandal, which is that someone leaked confidential IRS information about individuals to serve a political agenda. This is the same tax agency that pursued a vendetta against conservative nonprofit groups during the Obama Administration. Remember Lois Lerner?
This is also the same IRS that Democrats now want to infuse with $80 billion more to chase a fanciful amount of uncollected taxes. As part of this effort, Mr. Biden wants the IRS to collect “gross inflows and outflows on all business and personal accounts from financial institutions.” Why? So the information can be leaked to ProPublica?
The bank administered a loan of some $1 billion, sending payments from Revlon to the lenders. Citibank mistakenly sent a wire transfer of the entire principal amount due when it only intended a single installment.
Under established law, the money that Citibank wired should be repaid because it was sent by mistake. But U.S. District Judge Jesse Furman upset settled law and allowed lenders to keep the money on the ground that the recipients did not have notice that the funds had been sent erroneously. If that became the rule, it would upset the important relationships among lenders, borrowers and trusted intermediaries.
Mistakes like this occur with surprising frequency. In 2017, the German bank KfW mistakenly transferred $5.4 billion to lenders. In China, the bank Rural Commercial Bank in Changsha thought that a customer’s 10-digit account number was actually the amount of money to be transferred, and mistakenly sent 1.2 billion yuan (around $190 million) to the customer. Deutsche Bank recently sent $6 billion to a U.S.-based hedge fund in error. In all these cases, the banks recovered the errant funds transfers almost immediately.