Things to Do in L.A. Tap to enable a layout that focuses on the article. Mortgage lenders probed over how they handled L.A. wildfire insurance payouts Altadena residents and council members gather at a fire-damaged residential property on East Las Flores Drive in support of Senate Bill 1090 on June 29, 2026, in Altadena, CA. Two thirds of Altadena homes were destroyed in the Eaton fire on January 7, 2025. SB 1090 would help protect fire survivors while preserving opportunities for affordable housing. This is read by an automated voice.

A scammer jailed for making false home insurance claims about supposedly stolen Lego has been listed as one of last year's most high-profile insurance fraudsters. The person was sentenced to 28 months in prison after an investigation found the claims were made up, insurance trade body the ABI said. Other insurance fraud cases flagged by the ABI include someone who staged car crashes with people they met online and a complex travel insurance scam involving multiple fake identities and documents. The average value of a fraudulent claim reached £14,300 last year, the second-highest level on record, the ABI said. There was £1.34bn worth of fraudulent claims in 2025, a 14% increase on the £1.18bn detected the previous year.

Things to Do in L.A. Tap to enable a layout that focuses on the article. From Watts to L.A. wildfires: The rise of California's troubled FAIR Plan New homebuyers are having a hard time finding fire insurance coverage in cities such as Menifee, Calif. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

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AI-assisted modernization can reduce the time and complexity of transforming legacy systems while creating a foundation for faster innovation, says Asifa Sherazi, CIO of health insurance at Bupa and Sanjeev Tripathi, senior VP, region head of BFSI, healthcare, and public sector at Infosys. For years, legacy technology has been a problem companies knew they needed to solve, but one they often struggled to tackle. The cost, complexity, and risk of replacing business-critical systems could make modernization feel like a disruption to manage instead of an opportunity to pursue. But with the rise in customer expectations and the changes AI brought to the economics of software development, that calculation is changing. Bupa's modernization of its My Bupa mobile application offers a case study in what becomes possible when a legacy migration is treated as a business transformation rather than a technology rewrite. Bupa CIO of health insurance Asifa Sherazi describes the risks of waiting for legacy systems to become an emergency: "The end-of-life technology is a risk that compounds quietly, and then arrives all at once." For Bupa, moving its application from Xamarin to native Swift and Kotlin improved the app rating from 3.7 to 4.7, while the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. "What they'll notice is that when they need us, often at a stressful moment, it just simply works," Sherazi says. Sanjeev Tripathi, senior vice president and region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys, contends that AI is helping change the equation. "The emergence of AI is fundamentally shifting the economics of modernization," he says, reducing the effort, risk, and time traditionally associated with these programs. At Bupa, combining AI-assisted reverse engineering with forward engineering helped deliver the transformation in approximately 60% less time than would have been possible in the pre-AI era.

You Know Who Really Hates AI? Insurance Claims Adjusters Of the Glassdoor reviews from claims adjusters that mentioned AI, a staggering 98 percent were negative. "AI is just a tool," one person tells WIRED. "It should never be given the keys." On the job review platform Glassdoor, one faction hates artificial intelligence more than any other. "Pushing AI to the point that you are asking humans not to use their thoughts and brains is such a turn off," reads one representative review.

Things to Do in L.A. Tap to enable a layout that focuses on the article. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search. Attorneys representing State Farm said they discovered "nonexistent case citations which appear to have been AI hallucinations" across a recent batch of motions.

Things to Do in L.A. Seen through a magazine, Christin Evans is photographed at her bookstore, the Booksmith, in San Francisco. Evans says health insurance costs have leaped 17% this year, forcing her to cut staff hours. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

Machine learning algorithms and their training datasets have grown substantially in both size and complexity over the past decade. This increased model complexity has made it challenging to interpret and predict their behavior in unobserved scenarios. Hence, many applications that involve societal decisions still rely on simple, interpretable models like linear regression, often after feature engineering. Examples of such applications include predicting national housing prices, estimating wages across industries, forecasting loan amounts across banks, predicting life insurance premiums across groups, and projecting energy consumption across communities [CGKMN24]. A shared safety and sometimes legal concern across the above applications is the potential for wildly different model qualities for different distributions, i.e., outputting a notably worse model for some source data distributions [Dat14; BS16; HPS16; VVB18; SBFVV19; BHJKR21; CGNSG23; Cho16; KLMR18; ADW19; CGKMN24; SVWZ24].

In settings where ML models are used to inform the allocation of resources, agents affected by the allocation decisions might have an incentive to strategically change their features to secure better outcomes. While prior work has studied strategic responses broadly, disentangling misreporting from genuine adaptation remains a fundamental challenge. In this paper, we propose a causally-motivated approach to identify and quantify how much an agent misreports on average by distinguishing deceptive changes in their features from genuine adaptation. Our key insight is that, unlike genuine adaptation, misreported features do not causally affect downstream variables (i.e., causal descendants). We exploit this asymmetry by comparing the causal effect of misreported features on their causal descendants as derived from manipulated datasets against those from unmanipulated datasets. We formally prove identifiability of the misreporting rate and characterize the variance of our estimator. We empirically validate our theoretical results using a semi-synthetic and real Medicare dataset with misreported data, demonstrating that our approach can be employed to identify misreporting in real-world scenarios.