“The National Heath Care Anti-Fraud Association estimates conservatively that health care fraud costs the nation about $68 billion annually — about 3 percent of the nation’s $2.26 trillion in health care spending"
Health fraud has many faces that are well-known to insurers that have suffered one way or another from its consequences. In its more innocent forms, patients bypasses primary care providers and directly approach specialists under the perception that it makes for a more effective care. Under a more dreadful forms of fraud, patients falsify information on claims to receive unlawful reimbursements. Other common cases of members-driven fraud include doctors shopping where patients seek to obtain the same care from multiple clinicians to obtain controlled drugs that can then be sold in the black market. Pharmacies and physicians can jointly engage in preferential and selective referrals and unnecessary prescriptions for exchanging kickbacks and incentives. By mining historical claim and billing data, insurers have the opportunity to apply a variety of data mining and predictive analytics techniques to flag suspicious claims, profiling the perpetrators and bring legal actions against them, and identify the ones with the highest likelihood of saving or recovering costs.
Segmenting healthcare insurance policy holders across clinical (i.e., how sick they are) and claim behavioral dimensions (e.g, hospitalizations, emergency admissions, medication refills, etc.) will allow stratifying members by risk similarities and match, more appropriate for targeted treatment.
Developed a profile for each risk segment (e.g., high-utilizers, potential abusers, etc..) detailing dominant trends and persisting patterns associated with each group to better understand drivers and motivation for each type of behavior and to devise a deterrent course of action.
Predict the likelihood that an existing member engage in voluntarily or involuntarily fraudulent behavior or begin to show signs of abuse and over-utilization and deploy the scoring model in real-time to detect fraud and take immediate actions (e.g., block transaction, sanction member, etc..)
For traders and the general public, that there’s no guaranteed way to predict the movements of a particularly currency, or in fact any currency in the world is a common belief. Nevertheless, advancements in AI and the emergence of advanced breeds of deep learning techniques makes it possible today to build a memory-based model that will remember all events that take place ahead of any currency upward or downward movement in order to improve the odds and optimizes investment strategy on the long run.