Fraud alleviation is one of the most desirable artificial intelligence (AI) services as it can provide immediate return on investment. Already, many companies are making a profit through AI and machine learning (ML) systems, which detect and prevent fraud in real time.
"Maintaining AI" on digital fraud
According to a new report, Highmark Inc.'s Financial Investigations and Providers Review (FIPR) division generated $ 260 million in savings that would otherwise be lost in 2019 against fraud, waste and abuse. The company has saved $ 850 million over the past five years.
“We know that the vast majority of providers are doing the right thing. We also know that millions of medical care are lost each year to fraud, waste and abuse, "said Melissa Anderson, Executive Vice President and Chief Executive Officer and Chief Audit and Compliance Officer of Highmark Health" using technology and other blueprints and Working with law enforcement We continuously develop our processes, and we are proud of the nationality of the best. "
The FIPR detects fraud in client services with the help of an in-house team of investigators, accountants and programmers, as well as seasoned professionals who perform unusual activities, such as registered nurses and former law enforcement. Human audits, performed to identify unusual claims and assess the feasibility of supplier bills, are used as training data for AI systems that can adapt and respond more quickly to questionable consumer behavior change.
As fraudulent actors become increasingly aggressive and vigilant in their tactics, organizations are looking to AI to avoid growing threats.
"We know it is much easier to stop these bad actors before the money comes in, then pay and you have to hire them," said Kurt Spear, Vice President of Financial Investigation at Highmark Inc.
Elsewhere, Teradata, an AI firm specializing in selling bank fraud detection solutions, claims in a case study that it has helped Danske Bank reduce its counterfeit positions by 60% and has increased its actual detection of fraud by 50%.
Other service providers are particularly keen on detecting AI fraud, especially in the healthcare sector. A recent poll by Optum found that 43% of health industry leaders say they strongly agree that AI will become an integral part of TV fraud, waste, or abuse.
In fact, AI spending is growing tremendously, with total operating costs reaching $ 15 billion by 2024, the most sought-after solutions being network optimization and fraud alleviation. According to the Association of Certified Fraud Investigators (ACFE), it is an inauguration Anti Fraud Technology Assessment Report, It is expected that organizations' funds will be spent on AI and machine learning to reduce online fraud by 2021.
Health care fraud would be alleviated for an industry that is plagued by many structural inefficiencies.
The United States spends about $ 3.5 trillion annually on health-related services. This stunning amount corresponds to about 18% of the country's GDP and is on average twice that of developed countries. However, despite this huge expense, the quality of health services is lacking. According to a recently released 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 health care system is incredibly inefficient, with about 25% of all its finances going down, mostly unsuitable – that's $ 760 billion annually at best and $ 935 billion annually.
Most of the money is spent on unnecessary administrative complexity, including billing and coding waste – this alone is responsible for $ 265.6 billion annually. Drug prices are another major source of waste, amounting to about $ 240 billion. Lastly, excessive treatment and care failed to pay another $ 300 billion in costs.
And even these astronomical costs can be estimated. According to the management firm Numerof and Associates, a 25% estimate of waste can be conservative. Instead, the firm estimates that about 40% of the country's health care costs have been spent, largely due to administrative difficulties. The firm adds that fraud and abuse make up about 8% of healthcare waste.
What does healthcare fraud look like?
Most cases of fraud in the health sector are organized by criminal groups and some health care providers, which is dishonest.
According to the National Anti-Corruption Association, the most common types of health fraud in the United States are:
- A fictitious fictitious services tax that has never been. Frauds may use their patient information or obtain it through identity theft in order to be able to substantiate a claim or make claims for procedures that have never been implemented in reality.
- Poisoning the Service Bill for the procedures that were actually provided. Instead of failing to pay bills, another favorite among healthcare frauds is known as "upgrading" – higher cost treatment and shifting patient diagnosis to a more serious condition requiring more expensive paper care.
- Extra services tax to cover more insurance coverage costs.
- To make a patient diagnosis, do more tests and procedures that are, in fact, completely unnecessary just to make more insurance payments.
- Paying for each medical procedure fee that leads to higher prices – this practice is known as "non-payment".
- The patient overpayment will either reimburse or subtract the amount, then overpay for the insurance carrier.
The rise of HIV in fraud analysis
Traditionally, the most common method of managing fraud has been to enforce man-made rules. It is still the most common practice to date, but thanks to the quantum leap of computing and node data, AI-based machine learning algorithms are becoming increasingly attractive and, most importantly, practical.
Still what is machine learning? Machine learning refers to algorithms that are designed to "learn" as humans do and continually encourage this learning process over time without human supervision. The algorithm's output accuracy can be improved continuously by providing their data and information in the form of observations and real-world interactions.
In other words, machine learning is the science of computer action, which is not directly programmed.
There are different types of machine learning algorithms depending on the needs of each situation and industry. Hundreds of new learning algorithms are published daily. They are usually grouped into:
- Learning style: Supervised teaching, mastering, partial teaching;
- Function form: Classification, Regression, Decision Tree, Deep Learning, etc.
In the context of health fraud analysis, machine learning excludes the use of predefined rules – even of phenomenal complexity.
Machine learning allows companies to effectively identify what transactions or behaviors are most likely to be fraudulent while reducing false positives.
In an industry where billions of different transactions can be made every day, AI-based analytics can be an amazing idea thanks to the ability to be able to automatically detect large amounts of data.
The process itself can be difficult because the algorithms have to explain the patterns in the data and apply data science in real time to distinguish between normal behavior and abnormal behavior.
This can be a problem because a misunderstanding of how AI and fraud-specific data work can lead to the development of algorithms that essentially learn to do the wrong things. Just as people can learn bad habits, so can a poorly designed machine learning model.
In order for online fraud detection to succeed based on AI technology, you need to check these three crucial boxes for these platforms.
First, supervised machine learning algorithms need to be refined and refined based on decades of transaction value data to minimize false positives and improve response time. This is more difficult to do because it is done because the data needs to be structured and evaluated correctly – depending on the size of the project, this may take years to resolve.
Second, machine learning must keep up with the more sophisticated forms of online fraud. After all, AI is used by both auditors and fraudsters. Lastly, to scale AI fraud detection platforms, they need a large-scale, universal activity data network (eg transactions, filings, etc.) to measure ML algorithms and improve the accuracy of fraud detection scores.
According to a new market research report released earlier this year, the market for healthcare fraud analytics tools is expected to reach $ 4.6 billion by 2025, up from $ 1.2 billion in 2020.
This increase is attributable to more and more complex frauds in the health sector.
To increase healthcare fraud, companies offer a variety of analytics solutions that flag fraud – some of which are models-based, but AI-based technologies are likely to provide a backbone for all kinds of analytics in the future. This includes descriptive, predictive and reconstructive analysis.
Some of the most important companies in the market for Healthcare Fraud Analysis include IBM Corporation (US), Optum (US), SAS Institute (US), Change Healthcare (US), EXL Service Holdings (US), Cotiviti (US), Wipro Limited (Wipro ) (India), Bandwidth (US), HCL (India), Canadian Global Information Technology Group (Canada), DXC Technology Company (US), Northrop Grumman Corporation (US), LexisNexis Group (US) and Pondera Solutions (US).
It has been said that there are many opportunities today to prevent fraud. However, the evolving landscape of e-commerce and hacking always presents new challenges. However, these challenges require innovation that can respond to and respond to fraud. საერთო მნიშვნელი, გადახდის თაღლითობიდან ბოროტად გამოყენებისთვის, როგორც ჩანს, არის მანქანათმცოდნეობა, რომელიც მარტივად შეიძლება მასშტაბური იყოს დიდი მონაცემების მოთხოვნების დასაკმაყოფილებლად, ბევრად უფრო მოქნილობით, ვიდრე ტრადიციული მეთოდები.