FRAUD AND DATA ANALYTICSFighting fraud in Healthcare with Data

fraud can be broadly defined as an intentional act of deception involving financial transactions for purpose of personal gain.

Fraud is a crime, and is also a civil law violation./frɔːd/

financial

How fraudulent is Singapore?

Factors contributing

to Fraud

53% Identified weak or overridden internal controls as a leading enabler of fraud

30% Cited collusion between employees and third parties

17% Cited collusion between employees

Source: Singapore Fraud Survey 2014

Occurrence

29%vs 21% in 2011

Detection

20%vs 15% in 2011

Perpetrators

58%vs 47% in 2011

Healthcare expenditure lost to fraud annually

Global estimatesUSD 415 Billion

Europe56 Billion Euro

Source: European Healthcare Fraud & Corruption Network

Vulnerability of Healthcare• Large amounts of money involved, multiple parties

processes with high risk of bribery.

• High degree of information imbalances and an inelastic demand for services.

• Healthcare providers usually assumes a cultural role as trusted healers who are above suspicion.

• Claims amounts tends to be insignificant and thus lack of attentive focus.

Health Insurance Fraud & AbuseScale & Impact

• It is estimated that as much as 10% of total healthcare spending in the United States are due to fraudulent activities, amounting to a cost of about $115 billion annually.

• In the United Kingdom, the Insurance Fraud Bureau estimates that the loss due to insurance fraud in the United Kingdom is about £1.5 billion ($3.08 billion), causing increase in insurance premium.

• Some estimate that $260 billion (180 billion euros) or approximately 6% of global healthcare spending is lost to fraud each year. This is the equivalent to the GDP of a country like Finland or Malaysia being stolen on an annual basis.

Fraud is a huge issue – it is widespread and expensive. Many people think its fine to defraud insurers, for instance, 30% would not report someone else who defrauded an insurer. Physicians often game the system to get coverage for patients.

Why commit fraud?Pressure or Motivation

Opportunity Rationalization

FraudTriangle

HOW CAN DATA ANALYTICS HELP?Using Data Analytics to detect anomalies

Three Line of Defense Model

Data Analytics

5 steps in Data Analytics

1. Import Data

2. Prepare Data

3. Analyze Data

4. Report Findings

5. Automate

Examples of analytic applications for healthcare

• Duplicate Claims – Identify any duplicate claims being submitted

• Age-specific procedures – Identify potentially suspicious exceptions to age-specific procedures

• Gender-specific procedures – Identify potentially suspicious exceptions to gender-specific procedures

• Physician specialty – Identify trending for physician’s codes which are outside of their field of specialty

Maturity level of Data Analytics

Case Study - Fraud in Singapore’s Healthcare sector• 2 dental clinics suspended from offering subsidized care to middle- and

low-income Singaporeans and Pioneer generations

• Stripped of ability to offer patients subsidies under the CHAS scheme

• Both clinics continuously made claims that breached MOH rules and guidelines

• Non-compliance can sometimes be due to simple administrative errors, such as: recording of wrong dates

• Possibility of fraud happens when claimed procedures does not match actual treatment, or when claims are made for procedures that were not done.

In summary…

Healthcare fraud poses a serious financial drain on the healthcare systems in many jurisdictions and prevents the effectiveness of providing healthcare

to those in need.

Organizations can deploy sophisticated anti-fraud data analytics to help detect fraud and misconduct

as well as to understand the root causes of irregularities.

THANK YOU!