Fraud mitigation is one of the most sought-after artificial intelligence (AI) services because it can provide an immediate return on investment. Already, many companies are experiencing lucrative profits thanks to AI and machine learning (ML) systems that detect and prevent fraud in real time.
‘Keeping an AI’ on digital fraud
According to a new report, Highmark Inc.’s Financial Investigations and Provider Review (FIPR) department generated $260 million in savings that would have otherwise been lost to fraud, waste, and abuse in 2019. In the last five years, the company saved $850 million.
“We know the overwhelming majority of providers do the right thing. But we also know year after year millions of health care dollars are lost to fraud, waste and abuse,” said Melissa Anderson, executive vice president and chief audit and compliance officer, Highmark Health. “By using technology and working with other Blue Plans and law enforcement, we have continually evolved our processes and are proud to be among the best nationally.”
FIPR detects fraud across its clients’ services with the help of an internal team made up of investigators, accountants, and programmers, as well as seasoned professionals with an eye for unusual activity such as registered nurses and former law enforcement agents. Human audits performed to detect unusual claims and assess the appropriateness of provider payments are used as training data for AI systems, which can adapt and react more rapidly to suspicious changing consumer behavior.
As fraudulent actors have become increasingly aggressive and cunning with their tactics, organizations are looking to AI to mitigate rising threats.
“We know it is much easier to stop these bad actors before the money goes out the door then pay and have to chase them,” said Kurt Spear, vice president of financial investigations at Highmark Inc.
Elsewhere, Teradata, an AI firm specialized in selling fraud detection solutions to banks, claims in a case study that it helped Danske Bank reduce its false positives by 60% and increased real fraud detection by 50%.
Other service operators are looking to AI fraud detection with a keen eye, especially in the healthcare sector. A recent survey performed by Optum found that 43% of health industry leaders said they strongly agree that AI will become an integral part of detecting telehealth fraud, waste, or abuse in reimbursement.
In fact, AI spending is growing tremendously with total operating spending set to reach $15 billion by 2024, the most sought-after solutions being network optimization and fraud mitigation. According to the Association of Certified Fraud Examiners (ACFE) inaugural Anti-Fraud Technology Benchmarking Report, the amount organizations are expected to spend on AI and machine learning to reduce online fraud is expected to triple by 2021.
Mitigating fraud in healthcare would be a boon for an industry that is plagued with many structural inefficiencies.
The United States spends about $3.5 trillion on healthcare-related services every year. This staggering sum corresponds to about 18% of the country’s GDP and is more than twice the average among developed countries. However, despite this tremendous spending, healthcare service quality is lacking. According to a now-famous 2017 study, the U.S. has fewer hospital beds and doctors per capita than any other developed country.
A 2019 study found that the country’s healthcare system is incredibly inefficient, burning through roughly 25% of all its finances which basically go to waste — that’s $760 billion annually in the best case scenario and up to $935 billion annually.
Most money is being wasted due to unnecessary administrative complexity, including billing and coding waste — this alone is responsible for $265.6 billion annually. Drug pricing is another major source of waste, account for around $240 billion. Finally, over-treatment and failure of care delivery incurred another $300 billion in wasted costs.
And even these astronomical costs may be underestimated. According to management firm Numerof and Associates, the 25% waste estimate might be conservative. Instead, the firm believes that as much as 40% of the country’s healthcare spending is wasted, mostly due to administrative complexity. The firm adds that fraud and abuse account for roughly 8% of waste in healthcare.
What does healthcare fraud look like?
Most cases of fraud in the healthcare sector are committed by organized crime groups and a fraction of some healthcare providers that are dishonest.
According to the National Healthcare Anti-Fraud Association, the most common types of healthcare frauds in the United States are:
- Billing fictitious services that were never provided. Fraudsters might use their own patient information or obtain it through identity theft in order to fabricate claims or embellish claims with charges for procedures that were never carried out in reality.
- Bloating the service bill for procedures that were actually provided. Instead of billing services that were never provided, another favorite among healthcare fraudsters is known as “upcoding” — billing higher-priced treatments and inflating the patient’s diagnosis to a more serious condition that requires more expensive care on paper.
- Billing unnecessary services in order to generate more insurance-covered expenses.
- Doctoring a patient’s diagnosis in order to run more tests and procedures that, in reality, are totally unnecessary just to generate more insurance payments.
- Billing each step of a medical procedure, leading to higher prices — this practice is known as ‘unbundling’.
- Waving patient co-pays or deductibles and then over-billing the insurance carrier.
The rise of AI in fraud detection analytics
Traditionally, the most prevalent method for fraud management has been human-generated rule sets. To this day, this is the most common practice but thanks to a quantum leap in computing and Big Data, AI-based solutions based on machine learning algorithms are becoming increasingly appealing and — most importantly — practical.
But what is machine learning anyway? Machine learning refers to algorithms that are designed “learn” like humans do and continuously tweak this learning process over time without human supervision. The algorithms’ output accuracy can be improved continuously by feeding them data and information in the form of observations and real-world interactions.
In other words, machine learning is the science of getting computers to act without being explicitly programmed.
There are all sorts of various machine learning algorithms, depending on the requirements of each situation and industry. Hundreds of new machine learning algorithms are published on a daily basis. They’re typically grouped by:
- Learning style: supervised learning, unsupervised learning, semi-supervised learning;
- Form of function: classification, regression, decision tree, deep learning, etc.
In a healthcare fraud analytics context, machine learning eliminates the use of preprogrammed rule sets – even those of phenomenal complexity.
Machine learning enables companies to efficiently determine what transactions or set of behaviors are most likely to be fraudulent, while reducing false positives.
In an industry where there can be billions of different transactions on a daily basis, AI-based analytics can be an amazing fit thanks to their ability to automatically discover patterns across large volumes of data.
The process itself can be complex since the algorithms have to interpret patterns in the data and apply data science in real-time in order to distinguish between normal behavior and abnormal behavior.
This can be a problem since an improper understanding of how AI works and fraud-specific data science techniques can lead you to develop algorithms that essentially learn to do the wrong things. Just like people can learn bad habits, so too can a poorly designed machine learning model.
In order for online fraud detection based on AI technology to succeed, these platforms need to check three very important boxes.
First, supervised machine learning algorithms have to be trained and fine-tuned based on decades worth of transaction data to keep false positives to a minimum and improve reaction time. This is harder said than done because the data needs to be structured and properly labeled — depending on the size of the project, this could take staff even years to solve.
Secondly, unsupervised machine learning needs to keep up with increasingly sophisticated forms of online fraud. After all, AI is used by both auditors and fraudsters. And, finally, for AI fraud detection platforms to scale, they require a large-scale, universal data network of activity (i.e. transactions, filed documents, etc) to scale the ML algorithms and improve the accuracy of fraud detection scores.
According to a new market research report released earlier this year, the healthcare fraud analytics market is projected to reach $4.6 billion by 2025 from $1.2 billion in 2020.
This growth is attributed to more numerous and complex fraudulent activity in the healthcare sector.
In order to tackle rising healthcare fraud, companies offer various analytics solutions that flag fraudulent activity — some are rule-based models, but AI-based technologies are expected to form the backbone of all types of analytics used in the future. These include descriptive, predictive, and prescriptive analytics.
Some of the most important companies operating today in the healthcare fraud analytics market include IBM Corporation (US), Optum (US), SAS Institute (US), Change Healthcare (US), EXL Service Holdings (US), Cotiviti (US), Wipro Limited (Wipro) (India), Conduent (US), HCL (India), Canadian Global Information Technology Group (Canada), DXC Technology Company (US), Northrop Grumman Corporation (US), LexisNexis Group (US), and Pondera Solutions (US).
That being said, there is a wide range of options in place today to prevent fraud. However, the evolving landscape of e-commerce and hacking pose new challenges all the time. To keep up, these challenges require innovation that can respond and react rapidly to fraud. The common denominator, from payment fraud to abuse, seems to be machine learning, which can easily scale to meet the demands of big data with far more flexibility than traditional methods.
