Tag: fraud detection

Harvard Probe Finds Honesty Researcher Engaged in Scientific Misconduct

Mary Pat Campbell

Link: https://www.wsj.com/us-news/education/harvard-investigation-francesa-gino-documents-9e334ffe

Excerpt:

A Harvard University probe into prominent researcher Francesca Gino found that her work contained manipulated data and recommended that she be fired, according to a voluminous court filing that offers a rare behind-the-scenes look at research misconduct investigations.

It is a key document at the center of a continuing legal fight involving Gino, a behavioral scientist who in August sued the university and a trio of data bloggers for $25 million.

The case has captivated researchers and the public alike as Gino, known for her research into the reasons people lie and cheat, has defended herself against allegations that her work contains falsified data. 

The investigative report had remained secret until this week, when the judge in the case granted Harvard’s request to file the document, with some personal details redacted, as an exhibit. 

….

An initial inquiry conducted by two HBS faculty included an examination of the data sets from Gino’s computers and records, and her written responses to the allegations. The faculty members concluded that a full investigation was warranted, and Datar agreed.

In the course of the full investigation, the two faculty who ran the initial inquiry plus a third HBS faculty member interviewed Gino and witnesses who worked with her or co-wrote the papers. They gathered documents including data files, correspondence and various drafts of the submitted manuscripts. And they commissioned an outside firm to conduct a forensic analysis of the data files.

The committee concluded that in the various studies, Gino edited observations in ways that made the results fit hypotheses. 

When asked by the committee about work culture at the lab, several witnesses said they didn’t feel pressured to obtain results. “I never had any indication that she was pressuring people to get results. And she never pressured me to get results,” one witness said. 

Author(s): Nidhi Subbaraman

Publication Date: 14 March 2024

Publication Site: WSJ

Problematic Paper Screener

Mary Pat Campbell

Link: https://dbrech.irit.fr/pls/apex/f?p=9999:1::::::

https://www.irit.fr/~Guillaume.Cabanac/problematic-paper-screener

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Excerpt:

🕵️ This website shows reports the daily screening of papers (partly) generated with:► Automatic SBIR Proposal Generator► Dada Engine► Mathgen► SCIgen► Tortured phrases… and Citejacked papers 🔥⚗️ Harvesting data from these APIs:► Crossref, now including the Retraction Watch Database► Dimensions► PubPeer

Explanation: https://www.irit.fr/~Guillaume.Cabanac/problematic-paper-screener/CLM_TorturedPhrases.pdf

Author(s): Guillaume Cabanac

Publication Date: accessed 16 Feb 2024

Insurance Fraud on the March

Mary Pat Campbell

Link: https://www.insurancejournal.com/blogs/right-street/2024/02/12/760360.htm

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Excerpt:

Some of the most chilling examples of insurance fraud are grisly affairs revealing the darkest of humanity’s dark side:

  • John Gilbert Graham placed a time-release bomb on a plane in which his mother was traveling, for the life insurance payment. The bomb exploded. In addition to Graham’s mother all 43 other passengers and crew perished.
  • Utah physician Farid Fata administered chemotherapy to hundreds of women who did not have cancer. Fata submitted $34 million in fraudulent claims to Medicare and private insurance companies.
  • Ali Elmezayen staged a freak car accident which took the lives of his two autistic children and nearly drowned his wife. He collected a $260,000 insurance payout, but his crime was discovered. He was sentenced to 212 years in prison.
  • A Chicago federal grand jury charged 23 defendants with participating in a fraud scheme swindling $26 million from ten life insurers. The scheme featured submission of fraudulent applications to obtain policies, and misrepresenting the identity of the deceased.

There are thousands of other equally horrific insurance fraud stories. The annual Dirty Dozen Hall of Shame report describes some of the most egregious, and contributes to an understanding of how far fraudsters will go to cheat insurance companies.

….

Improvements in predictive modeling and the introduction of artificial intelligence (AI) have strengthened insurers abilities to identify, and ultimately investigate, submitted claims that may be fraudulent. At the same time, however, AI is also being used as a weapon to penetrate insurers’ fraud detection systems. Techniques being used include AI-created fake photographs of cars of a particular make and model showing damage that is not real, but used to extract a claims payment. Some insurers are no longer accepting photos because they may be doctored, and are returning to adjustors physically visiting the allegedly damaged car. A nefarious life insurance scam includes AI-enabled manipulation of ones voice so that a criminal third party gets past insurers’ voice recognition technology, and initiates a policy being surrendered to a non-policyholder, non-beneficiary. It seems that for every additional layer of protection insurers introduce, the criminals are keeping up, if not forging ahead.

Author(s): Jerry Theodorou, R Street

Publication Date: 12 Feb 2024

Publication Site: Insurance Journal

[109] Data Falsificada (Part 1): “Clusterfake”

Mary Pat Campbell

Link: https://datacolada.org/109

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Excerpt:

Two summers ago, we published a post (Colada 98: .htm) about a study reported within a famous article on dishonesty (.htm). That study was a field experiment conducted at an auto insurance company (The Hartford). It was supervised by Dan Ariely, and it contains data that were fabricated. We don’t know for sure who fabricated those data, but we know for sure that none of Ariely’s co-authors – Shu, Gino, Mazar, or Bazerman – did it [1]. The paper has since been retracted (.htm).

That auto insurance field experiment was Study 3 in the paper.

It turns out that Study 1’s data were also tampered with…but by a different person.

That’s right:
Two different people independently faked data for two different studies in a paper about dishonesty.

The paper’s three studies allegedly show that people are less likely to act dishonestly when they sign an honesty pledge at the top of a form rather than at the bottom of a form. Study 1 was run at the University of North Carolina (UNC) in 2010. Gino, who was a professor at UNC prior to joining Harvard in 2010, was the only author involved in the data collection and analysis of Study 1 [2].

Author(s): Uri Simonsohn, Leif Nelson, and Joseph Simmons

Publication Date: 17 Jun 2023

Publication Site: Data Colada

5 insurance use cases for machine learning

Mary Pat Campbell

Link: https://www.dig-in.com/opinion/5-use-cases-for-machine-learning-in-the-insurance-industry

Excerpt:

4. Fraud detection

Unfortunately, fraud is rampant in the insurance industry. Property and casualty insurance alone loses about $30 billion to fraud every year, and fraud occurs in nearly 10% of all P&C losses. ML can mitigate this issue by identifying potential claim situations early in the process. Flagging early allows insurers to investigate and correctly identify a fraudulent claim. 

5. Claims processing

Claims processing is notoriously arduous and time-consuming. ML technology is a tool to reduce processing costs and time, from the initial claim submission to reviewing coverages. Moreover, ML supports a great customer experience because it allows the insured to check the status of their claim without having to reach out to their broker/adjuster.

Author(s): Lisa Rosenblate

Publication Date: 9 Sept 2022

Publication Site: Digital Insurance

Underdispersion in the reported Covid-19 case and death numbers may suggest data manipulations

Mary Pat Campbell

Link: https://www.medrxiv.org/content/10.1101/2022.02.11.22270841v1

doi: https://doi.org/10.1101/2022.02.11.22270841

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Abstract:

We suggest a statistical test for underdispersion in the reported Covid-19 case and death numbers, compared to the variance expected under the Poisson distribution. Screening all countries in the World Health Organization (WHO) dataset for evidence of underdispersion yields 21 country with statistically significant underdispersion. Most of the countries in this list are known, based on the excess mortality data, to strongly undercount Covid deaths. We argue that Poisson underdispersion provides a simple and useful test to detect reporting anomalies and highlight unreliable data.

Author(s): Dmitry Kobak

Publication Date: 13 Feb 2022

Publication Site: medRXiV

Are some countries faking their covid-19 death counts?

Mary Pat Campbell

Link: https://www.economist.com/graphic-detail/2022/02/25/are-some-countries-faking-their-covid-19-death-counts

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Excerpt:

Irregular statistical variation has proven a powerful forensic tool for detecting possible fraud in academic research, accounting statements and election tallies. Now similar techniques are helping to find a new subgenre of faked numbers: covid-19 death tolls.

That is the conclusion of a new study to be published in Significance, a statistics magazine, by the researcher Dmitry Kobak. Mr Kobak has a penchant for such studies—he previously demonstrated fraud in Russian elections based on anomalous tallies from polling stations. His latest study examines how reported death tolls vary over time. He finds that this variance is suspiciously low in a clutch of countries—almost exclusively those without a functioning democracy or a free press.

Mr Kobak uses a test based on the “Poisson distribution”. This is named after a French statistician who first noticed that when modelling certain kinds of counts, such as the number of people who enter a railway station in an hour, the distribution takes on a specific shape with one mathematically pleasing property: the mean of the distribution is equal to its variance.

This idea can be useful in modelling the number of covid deaths, but requires one extension. Unlike a typical Poisson process, the number of people who die of covid can be correlated from one day to the next—superspreader events, for example, lead to spikes in deaths. As a result, the distribution of deaths should be what statisticians call “overdispersed”—the variance should be greater than the mean. Jonas Schöley, a demographer not involved with Mr Kobak’s research, says he has never in his career encountered death tallies that would fail this test.

….

The Russian numbers offer an example of abnormal neatness. In August 2021 daily death tallies went no lower than 746 and no higher than 799. Russia’s invariant numbers continued into the first week of September, ranging from 792 to 799. A back-of-the-envelope calculation shows that such a low-variation week would occur by chance once every 2,747 years.

Publication Date: 25 Feb 2022

Publication Site: The Economist