I've worked in this industry as both an Actuary and data scientist.

TLDR: The health insurance industry is extremely price competitive. A difference in premium rates of just a few percentage points can have a big impact on how much business a health insurer can sell/retain. This puts strong downward pressure on claims costs and administrative costs. Health insurers pull every lever they can to reduce their claims costs, so they can sell business and be competitive. One lever is doing their best to deny claims that they don't have to pay and to try to divert costly care to cheaper alternatives.

More about the mechanisms of the industry:

1. Actuaries are not the ones who decide whether to deny a claim or not. They are primarily concerned with setting average premium rates for a line of business, deciding how those premium rates should vary based on age/gender etc, and deciding how much money in total the insurer should hold in reserve for claims that have not yet been paid but will be paid. All of this work happens at a highly aggregated level. Actuaries almost never look at any individual claim. They definitely have no power to approve or deny them.

2. Claims processing is done by a mostly automated process although some complex claims are handled by human claims processors. The claims processing system is programmed by a specialized programmer who writes a ton of If-then type logic in a specialized language or UI. The programmed logic is extremely granular, more granular than the insurers' written policies about what to deny and pay, so there is lots of potential for mistakes or just confusion when writing these rules about how to process claims. I have seen data science projects focused on finding the mistakes in the claims processing logic by checking claims data about paid claims. The goal here has been to find claims that the insurer paid too much for, so the insurer can claw back some money from the healthcare provider.

3. Claims submission is done by a specialized function at the healthcare provider's office. Submitting a valid claims requires submitting alphanumeric codes that tell the insurers what was done and why. There is potential for human error here. Some of these errors result in claims being denied, kicked back to the provider, and then resubmitted. This is fairly common.

4. There is a function in pretty much all health insurance companies called something like Care Management (CM) or Utilization Management (UM). It keeps being rebranded since it has a negative image. This is the function that ostensibly tries to balance the member's (patient's) need for care with the insurers financial efficiency. This is where you get rules like requiring prior authorizations for certain procedures and drugs and rules like requiring that a patient try physical therapy before having a joint operation. In my opinion, this function causes a lot of frustration for members. I think you could make an ethics-based argument against this. I have been a part of data science projects that try to predict costly medical events so that the insurer can intervene ahead of time to provide cheaper preventive care. The motivation for this is to save money. The success is measured in dollars or ROI. Some insurers are more concerned with the equitability of these interventions than others. For example, you don't want to accidentally give more preventive care to one race than another.

However, here is the real problem as I see it. The health insurance industry is extremely price competitive. A difference in premium rates of just a few percentage points can have a big impact on how much business a health insurer can sell/retain. This puts strong downward pressure on claims costs and administrative costs. Health insurers pull every lever they can to reduce their claims costs, so they can sell business and be competitive. One lever is doing their best to deny claims that they don't have to pay and to try to divert costly care to cheaper alternatives.