Startup Pitches X-Rays and AI to Catch Fraudulent Returns

Clarity says its screening machines help merchants find counterfeit merchandise more quickly and accurately

By Liz Young of The WSJ. Excerpts:

"A startup says its technology can help retailers identify counterfeit merchandise returned for a refund, a way for merchants to catch fraudsters trying to exploit their returns policies.

Clarity on Thursday emerged from stealth mode and said returns-management company ReturnPro will begin using its machines to screen returns in three of its U.S. facilities."

"The company’s machine is similar in concept to an airport security X-ray screener. It uses artificial intelligence to learn what a given product is supposed to look like by scanning an example of the genuine item. The machine can then alert whether a returned product contains any different organic materials inside—a red flag for possible counterfeits—or identify if there are missing parts such as a power cord that was left out of the box."

"Clarity says its system can verify whether goods are real or fake in about 3 seconds. Its goal is to supplement the work of warehouse employees who examine each item, which is an imperfect process: Workers aren’t typically trained on what every single item is supposed to look like, and they don’t usually have time to go through individual packages in depth."

Related posts:

How This AI-Infused Warehouse Sorts Real Louis Vuitton Bags From Fakes: The RealReal’s Athena system helps validate goods submitted for resale, cutting down the timeline to list them for sale (2026)

Online Returns Fraud Finds a Home on Telegram, Costing Retailers Billions: Efforts to exploit retailers’ return programs are growing more organized, fueled by websites and messaging accounts that target merchants (2024) 

You Spent $6,000 on a Secondhand Chanel Bag. Now Find Out if It Is Real: Handbag authenticators are cashing in on buyers’ anxieties about fakes; hardware must be champagne not bronze; that pink leather is only made in France (2024)

How does a company selling used luxury goods spot fakes? (signalling and conspicuous consumption) (2019) 

Stagflation, Recession? Probably Not

See Why the Oil Shock Probably Won’t Derail the Economy. And One Way It Might: The U.S. is a net petroleum exporter and productivity is improving, but the bigger risk is stubborn inflation by Greg Ip of The WSJ. 

Stagflation combines the words stagnation and inflation. If oil prices rise, supply shifts to the left because the price of a resource has increased. Then prices increase and quantity decreases (which is the stagnation).

We had stagflation from 1975-83. The average unemployment rate was 7.7% and the average inflation rate was 7.8% during that 9 year period. The unemployment rate for 2025 was 4.3% and the inflation rate was  2.7%.

I hope Mr. Ip is right. Excerpts:

"The economy has grown more resilient to oil shocks, and a productivity renaissance is under way, helped by artificial intelligence. Both should help sustain growth and cushion cost pressures."

"The U.S. consumed 4% less gasoline in 2025 than in 2007, while producing 42% more goods and services (as measured by gross domestic product, adjusted for inflation). The share of households’ consumption of energy, including electricity, natural gas and gasoline, fell from 5.7% in 2007 to 3.7% last year."

"the shale revolution has turned the U.S. into a net exporter of petroleum and major exporter of liquefied natural gas. That means the hit to consumers is offset by a boost to producers."

"Russia’s invasion of Ukraine in 2022, which at one point pushed up oil by $45 a barrel, only trimmed U.S. growth by 0.13 percentage point that year, while raising inflation half a point"

"Employment growth has been sluggish for a year, due less to weaker demand for workers than a shrunken supply as immigration dries up."

"last year . . . output per hour worked, i.e. productivity, rose 2.8%."

"Hourly compensation rose 4.1% last year, yet adjusted for productivity, business labor costs were up just 1.3%."

"Stagflation happened in the 1970s not just because of high oil prices and lower productivity, but because the Fed systematically raised rates too little or cut too much, partly because of political pressure." [those Fed actions meant too much demand and that means inflation] 

The Seasonally Adjusted CPI Was up 0.267% in February

Here are the changes in the seasonally adjusted CPI for the six months ending in Dec: 

July 0.2284%

Aug 0.3483%

Sept. 0.2951% (There was no report for October due to the government shutdown)

Nov. 0.2523% (change from Sept)

Dec. 0.2978%

Jan. 0.1708% 

 

The last decline was March 2025 when it was -0.0500%. Before that it was June 2024 when it was -0.0029%.

See Consumer Price Index for All Urban Consumers: All Items in U.S. City Average from FRED (Federal Reserve Economic Data) compiled by the Research Division at the Federal Reserve Bank of St. Louis for data on the seasonally adjusted CPI.

That site shows a graph but if you click on the Download button you will get the actual numbers in Microsoft Excel.

The Consumer Price Index for All Urban Consumers: All Items in U.S. City Average (CPIAUCSL) was 327.460 in Feb. and 326.588 in Jan. Since 327.460/326.5881 = 1.00267, that means it was up 0.267%. If we had that every month for 12 months it would be up 3.25%. 

It was 319.679 in Feb. 2025. Since 327.460/319.679 = 1.0243, that means it was up 2.43% over the last 12 months.

The non-seasonally adjusted CPI was 326.785 in Feb. and 319.082 in Feb. 2025. That was up 2.41%. So pretty close to the seasonally adjusted CPI. This is still above the Fed's target of 2.0% (although they prefer to use the Personal Consumption Expenditures Price Index which was 2.8% higher in Jan. 2026 than Jan. 2025).

For more information see Consumer prices rose 2.4% annually in February, as expected by Jeff Cox of CNBC. Excerpt:

"Prices consumers pay for a broad range of goods and services rose in line with expectations for February, offering a final look at inflation pressures before an oil shock tied to the Iran war rattled the outlook.

The consumer price index increased a seasonally adjusted 0.3% for the month, putting the 12-month inflation rate at 2.4%, according to Bureau of Labor Statistics data released Wednesday. Both numbers matched the Dow Jones consensus forecast.

Stripping out volatile food and energy prices, the core CPI posted a 0.2% monthly reading and 2.5% annual rate, compared with forecasts for 0.2% and 2.5%, also in line with the estimates.

The annual rates were unchanged from January, indicating that inflation was holding above the Federal Reserve’s 2% target but not getting worse.

While the report showed inflation broadly stable, prices rose modestly for shelter and services while several goods categories, including used vehicles and auto insurance, saw declines."

The article also discusses what types of products are going up in price and what is going down. There is a graph of the monthly year-over-year percent change in prices and core prices going back almost 4 years.  

Related material: 

Consumer Price Index for All Urban Consumers: All Items Less Food and Energy in U.S. City Average (CPILFESL) This is also from from FRED (Federal Reserve Economic Data), compiled by the Research Division at the Federal Reserve Bank of St. Louis. It has the seasonally adjusted core CPI.

 

Consumer Price Index Data from 1913 to 2025

 

Personal Consumption Expenditures Price Index 

 

The Bureau of Labor Statistics makes seasonal adjustments. See Consumer Price Index Summary.

 

The table below has the annual inflation rate since 1914 in the columns labeled CPI %Ch. or CPI percentage change. It is from Consumer Price Index Data from 1913 to 2026 and is not seasonally adjusted. It is also the December to December change in the CPI. That site also looks at how the 12 month average for the CPI changed from one year to the next.

 

Year	CPI %Ch.		Year	CPI %Ch.		Year	CPI %Ch.		Year	CPI %Ch.
1914	1		1944	2.3		1974	12.3		2004	3.3
1915	2		1945	2.2		1975	6.9		2005	3.4
1916	12.6		1946	18.1		1976	4.9		2006	2.5
1917	18.1		1947	8.8		1977	6.7		2007	4.1
1918	20.4		1948	3		1978	9		2008	0.1
1919	14.5		1949	-2.1		1979	13.3		2009	2.7
1920	2.6		1950	5.9		1980	12.5		2010	1.5
1921	-10.8		1951	6		1981	8.9		2011	3
1922	-2.3		1952	0.8		1982	3.8		2012	1.7
1923	2.4		1953	0.7		1983	3.8		2013	1.5
1924	0		1954	-0.7		1984	3.9		2014	0.8
1925	3.5		1955	0.4		1985	3.8		2015	0.7
1926	-1.1		1956	3		1986	1.1		2016	2.1
1927	-2.3		1957	2.9		1987	4.4		2017	2.1
1928	-1.2		1958	1.8		1988	4.4		2018	1.9
1929	0.6		1959	1.7		1989	4.6		2019	2.3
1930	-6.4		1960	1.4		1990	6.1		2020	1.4
1931	-9.3		1961	0.7		1991	3.1		2021	7
1932	-10.3		1962	1.3		1992	2.9		2022	6.5
1933	0.8		1963	1.6		1993	2.7		2023	3.4
1934	1.5		1964	1		1994	2.7		2024	2.9
1935	3		1965	1.9		1995	2.5		2025	2.7
1936	1.4		1966	3.5		1996	3.3
1937	2.9		1967	3		1997	1.7
1938	-2.8		1968	4.7		1998	1.6
1939	0		1969	6.2		1999	2.7
1940	0.7		1970	5.6		2000	3.4
1941	9.9		1971	3.3		2001	1.6
1942	9		1972	3.4		2002	2.4
1943	3		1973	8.7		2003	1.9

 

Here is a timeline graph of this data: