Do you have a lot of data and do you need to profit from it? Anomaly detection methods save our customers money where they thought they would not invent anything.
Anomaly detection is a rapidly developing area that drives big enthusiasm among companies and finds application in many areas from marketing, banking, insurance to medicine and technology (robotics, aviation, etc.).
Do you have a lot of data and need to profit from it? Are you concerned about fraudulent customer behavior? Or do you need to look for anomalies in the tangle of production data? Anomaly detection methods save our customers money where they thought they would not invent anything. Literally from the computer, we discovered fraudulent behavior or rare events and saved clients' costs.
Your data is the key to profit in the digital era when efficiency is high. Do not waste fraudulent customers, identify suspicious behavior or anomalies in production data. The investment in data has to start paying off, so let's make a profit from it together.
Anomaly detection examples
- Marketing. Today, it is no longer enough to segment customers into a few core segments when personalization is essential. We will find atypical clients together and an ideal offer for them.
- Fraud. The most popular area for anomaly detection because its benefits are immediate. You no longer have to worry about which client is fair to you and which is a fraud. We will find them for you and you will save your nerves and money.
- Mistakes. We find errors in the flood of text and anomalies in the flood of production data.
The general goal of the anomaly detection in healthcare is to identify fraudulent behavior of healthcare providers or any suspicious claims of healthcare interventions.
Usually, this can be achieved by Marking collected data from different sources (claims, geographical/demographic information, etc.) by using statistical methods and machine learning algorithms as non/anomalous. Such data marked as anomalous may lead to identifying potential fraudulent behavior of healthcare providers or fraudlent schemes involving various participants (healthcare providers, pharmacies, insurees).
Examples of the outcomes can be:
- Identifying suspicious relationships between healthcare providers (doctor – pharmacy).
- And more
- Finding out suspicious claiming of specific medicaments using univariate statistical analysis.
Typical benefits can include:
- Decrease of time for assessment of claims from healthcare intervention.
- Identification of fraudulent schemes (e.g. patient sharing) which may lead to significant reduce of costs for healthcare insurance company.
- Decrease of costs by ad-hoc intervention to “non-standard” healthcare providers.
- Typically fraud detection is from bank transactions - a selection of 300 from Cambodia (example). You may have had the experience to refuse such a payment / withdrawal. This may be e.g. Real-time evaluation of the model of the bank that rejected the transaction, because there was a high chance that it was eg. "Copied" card to your account, which was evaluated by some indicators.
- Here we can mention the balance model in terms of accuracy, resp. precision and recall. Ie. In some cases, it is better if you reject a VALIDNA transaction if there is a degree of risk of fraud. This will make your life worse, if you really want to choose it, on the other hand, it will please you if it is a scam.
- Free flat rate through a friend probably do not spare the model - max some rule based and after the subsequent inspection.
- Teleco operators use anomaly detection, for example for SIM SWAP or Cloning SIM detection.
These are eg. instances where you do not “clone” the SIM card and can continue to use it directly for illegal activity - they will misuse your data or if you have a hacking account with dual identification on SMS, they can send money and confirm it in SMS where you get some code .
Cl Or Cloning SIM is used on the black market, where you do not clone your SIM, and then either make calls from your account from some third countries - you will receive high lump sum payments or even calmly in that country, and you may not even notice, and the perpetrator uses the operator's services for free. Or they can e.g. argue about some illegal activity and you truly track the SIM card to some "innocent" person.
CD CDR (Call detail records) data is commonly used for this.
Data is constantly "improving", we have more and more, and the adaptation of fraudsters is also noticeable. Therefore, techniques need to be constantly improved.
We uncovered black electricity consumers and fraudsters on health insurance. Today's technological world offers continuous improvement of measured data, which we have increasingly. In addition, the ingenuity of the scammers and their assimilation to current defense schemes also brings an urgent need to improve anomaly detection techniques. These should not fall short of your measure portfolio. Whatever your business, anomaly detection will surprise you.
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