2. Unfortunately, cumulative losses rarely engage in implementing a new algorithm due to professionals lack of understanding and almost mechanical reliance on the older methodologies.
3. Example #1: PSA (Prostate Specific Antigen Test):
A. The algorithm chosen does not consider that PSA testing is inaccurate (Two major studies have confirmed: The popular PSA test save few lives and often lead to risky and unnecessary treatments for large numbers of men.
B. Complete disregard for financial pressures: Knowing that you're now PSA + significantly impacts your coverage/insurance options (this becomes the insured's problem and counseling does not incorporate this).
C. Prostate cancer is progresses slowly, however, surgical manipulations can significantly increase the risk of metastasis (malignant spreading to other areas of the body).
D. Patients under treatment have actually died when avoiding treatment could have prolonged lives.
E. Doctors that undergo daily routine screens are unable to advise due to liability and therefore full reliance on the algorithm is at stake. Therefore, the success of the patient depends on the quality of the algorithm.
4. Example #2: Auto insurance needs a dynamic and mathematics based algorithm that has horizontal and vertical analysis rather than outdated +ve/-ve pathways:
Insurance companies prefer 'good' customers. The ones that make regular payments (without being delinquent) and don't have claims. This is a simple formula that can wreak havoc when expanded without an advanced approach.
A. Insurance companies are complaining: The 'good' pool size of individuals has becomes increasingly difficult to find as times change leading to unstable revenues.
B. Patrons and customers should ask their agents: How do the monies collected in revenues help to enhance the 'good' pool size so that a positive and larger 'good' pool can even exist? In lay terms, do insurance companies use their profits to help their definition of not-desirables become desirables, effectively?
C. Current targets set by insurance companies are not based on a dynamic combinatorial algorithm library (DCAL):
Desirable: Higher credit scores; Higher education; Full coverage on their vehicles; Continuous coverage; Lives in good areas; Owns a home; Between ages 22- 60 ; without claims in the past 3 years; Owns multiple cares versus single cars or lease; no health risks
Undesirable: Inner City- Urban areas; No/low Education; Liability only coverages; Poor credit history; Prior claims; Lapse in coverage; Old cars; contains health risks
D. Targeting without DCAL are poor ways to identify your sample size. In fact, it can lead to more problems over time. For example: Category Desirable vs. Category Undesirable can 'shift' and being a homeowner can be increasingly riskier than a renter who can leave and live in a city or country with more jobs (i.e. foreclosures). Without using DCAL, being an owner is 100%desirable and being a renter is 100% undesirable. Another example: Urban areas that offer mass transportation would mean that drivers have more options to avoid high accident time periods leading to lower accidents. Without using DCAL, urban living is considered 100% undesirable.
E. Here is another indicator that having an algorithm with "macro-indicators" will help. It states that during anytime, whenever the volatility indicator (also known as the 'fear' index rises and stays above 40 units for 6 consecutive months, a dynamic shift in the consumer markets and behavior will occur). Most companies don't use algorithms because mathematics is scary or unknown to them. The companies that do invest in some form of tracking don't crunch certain information due to the costs of implementation/maintenance
This is a consumer accessible example of the volatility index showing that we've maintained above 40 units from September 26, 2008 through March 31 2009 (6+ consecutive months). There is also meaning when consecutive Presidential terms contain a 40 unit spike (Clinton adminstration (1993-2001) , Bush administration (2001-2009) and Obama administration (2009-present) ) that policies are not holding up, increased stresses exists on the current model and external forces are eating away at the financial and political foundation. Consumers and companies must adapt or suffer the consequences:
"If you live without direction or decisions, then someone or something will provide you with directions or decisions that you may not desire" ... Pastor 2001
F. What would happen if these monies are instead used to insure financial companies that make bad bets on the stock market? And would this lead to higher premiums and lesser terms for the average consumer trying to purchase insurance? (i.e. AIG)
5. Conclusion: Older and costly algorithms need to be replaced with highly effective methodologies to consider outcome effects. These are called directed combinatorial algorithm libraries (DCAL).
The current prostate cancer algorithm is only a stepwise path of positive/negatives without an integration of mathematical considerations formulated to account for and to consider that prostate cancer is extremely slow growing and left unchanged poses little to no risk. If detected, confounds the insurance carriers model of finding healthy customers for both auto and health insurance.
The current insurance module is incomplete, inaccurate and unsustainable. Insurance carriers must account for better ratios in case there is an environmental plethora of disasters that appear due to climate change. The current ratio does not provide adequate protections and could cripple the already overburdened consumer.
