Anomaly detection is a rapidly developing field that drives 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? Do you need to look for anomalies in entangled production data? Anomaly detection methods save our customers money in areas they thought nothing new could be invented. Literally from the computer we detected fraudulent behavior and rare events and saved significant amounts of money for our customers.
Your data is the key to profit in the digital era when efficiency is high. Do not waste time and money on fraudulent customers. Identify suspicious behavior and anomalies in production data. The investment in data has to start paying off, so let's profit from it together.
Anomaly detection examples
- Marketing. Today, it is no longer enough to segment customers into a few core segments. Personalization is essential. We will find atypical clients together and define an ideal offer for them.
- Fraud. The most popular area for anomaly detection because there are immediate benefits. You no longer need to worry about fair clients versus fraudulent ones. We will detect fraudulent behavior for you and save you both nerves and money.
- Mistakes. We find errors in the flood of text and anomalies in the flood of production data.
- Typically, fraud detection comes from the banking sector. A withdrawal of 300 Euros in Cambodia is a good example. You may have the experience that such a payment / withdrawal was rejected. This may happen due to e.g. real-time evaluation of your bank that rejects the transaction because there is a high chance of e.g. a copied card to your account, based on an evaluation based on some set indicators.
- Here we can mention the balance model in terms of accuracy, precision and recall. I.e. in some cases it is better if a VALID transaction gets rejected if there is a certain degree of a risk of fraud. This will make you unhappy if you indeed want to withdraw money. On the other hand, it will please you if it is a scam.
- Free phone calls through a friend’s plan probably cannot be avoided, at the most based on some rule enforcement and a subsequent inspection.
- Telco operators use anomaly detection for e.g. SIM SWAP or SIM Cloning detection.
- This includes instances when e.g. a SIM card is cloned to be used directly for illegal activity – customer data may be misused or if the customer’s account has been hacked and there is dual identification with SMS, perpetrators can make a money transfer and confirm the transaction with a code sent by SMS.
- This is used on the black market: a SIM card is cloned and then used either to make calls from third countries and the customers receives hefty bills. Sometimes phone calls are made in the same country and the customer may not even notice and the perpetrator uses the operator's services for free. Perpetrators may e.g. discuss illegal business the SIM card is tracked down to an innocent individual.
- CDR (Call detail records) are commonly used for this purpose.
The general goal of anomaly detection in healthcare is to identify fraudulent behavior of healthcare providers or any suspicious claims made in respect of healthcare interventions.
Usually, this can be achieved by collecting data from different sources (claims, geographic/demographic data, etc.) and by using statistical methods and machine learning algorithms such data is labeled as non-anomalous. Data marked as anomalous may lead to identifying potential fraudulent behavior of healthcare providers or fraudulent schemes involving various participants (healthcare providers, pharmacies, insurees).
Examples of outcomes:
- Detecting suspicious behavior between healthcare providers (physician – pharmacy).
- Detecting suspicious claims in respect of specific medicines using unified statistical analysis.
- And more
Typical benefits can include:
- Reduced time for assessment of claims for healthcare intervention.
- Identification of fraudulent schemes (e.g. patient sharing) which may lead to significant cost reduction for healthcare insurance companies.
- Cost reduction in respect of ad-hoc interventions provided by non-standard healthcare providers.
Data is constantly improving and data amounts are constantly increasing. Fraudsters keep up with the trend. Therefore, fraud prevention needs to constantly improve.
We detected fraudulent electricity consumers and health insurance frauds. Today's technological world offers continuous improvement of measured data and the amounts of data are on the rise. In addition, the ingenuity of the scammers and their adaptation to current defense schemes also brings an urgent need to improve anomaly detection techniques. Your defense measure portfolio should not fall short of current fraud detection needs. Whatever is your business sector, anomaly detection will be a pleasant surprise for you.
Do you also want to collect and analyze your data in a efficiend way? Contact us today.
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