Technological Advance has pros and cons. For example, identity authentication or frauds are challenges because anyone can impersonate you in the internet. But technology itself is the answer to all of these problems. Fraud detection is possible from artificial intelligence and machine learning algorithms.
E-commerce and banking industries are the most affected by the challenges of phishing, financial fraud, identity theft, document forgery and fake accounts.
According to McKinsey, card fraud can reach to nearly $44 billion by 2025. Therefore, we had to ac, so this is where artificial intelligence algorithms and machine learning come into the equation to prevent this problem.
AI vs Machine learning for fraud detection
To understand the usefulness of artificial intelligence and machine learning for fraud detection, we must remember what it involves.
Machine learning is an analytical approach where a machine learns specific patterns from data without human intervention.
Artificial intelligence refers to the different types of analysis used to complete specific and repetitive tasks.
Although artificial intelligence and machine learning are not synonymous, they can complement each other. Thanks to AI, the latter can:
- Create self-perfecting analytical models (through data analysis).
- Create scalable, accurate and highly efficient tools.
- Generate models that work like cybercriminals. In other words, crime works 24/7 just like a machine does.
For this reason, AI and machine learning represent an excellent strategy to face internet fraud.
Machine learning in fraud detection is a collection of AI algorithms fed with historical data to suggest risk rules, but mostly to block or allow some user actions (possibly fraudulent) and avoid them.
When training the machine learning engine, it must flag past cases of fraud and non-fraud to avoid false positives and improve the accuracy of its risk rules. Since it is a self-perfecting set of algorithms, the longer the algorithms run, the more accurate the rule suggestions and thus the actions you take against cybercrime.
How do AI and machine learning work for fraud detection?
AI can analyze huge amounts of transactions to discover fraud patterns; it uses this same information to detect fraud in real-time and even prevent it.
Machine learning for fraud detection typically uses the logistic regression learning technique that provides a result – “fraud” or “no fraud” – but since false positives require costly manual investigations, the most successful AI solutions in this area now involve different data management techniques, such as:
- Data mining
- Neural networks
- Pattern recognition
AI can also help companies create purchase profiles, which include data such as a person’s purchasing characteristics and tendencies, devices, geolocations when making transactions. This facilitates the creation of predictions about their behavior, detecting unusual and suspicious behaviors in time.
AI for fraud detection seeks to create machine learning algorithms capable of collecting and managing large amounts of raw data, finding anomalies and monitoring specific data to predict trends, analyze themes or phenomena and use this analysis to prevent fraud.
Steps to implementing AI and machine learning for fraud detection
- Determine the sources of data inputs, specifically the transactions to be analyzed by the algorithm, such as transaction value, product SKU, credit card type, IP data, device type, VPN, proxy or Tor usage, etc.
The more data, the more accurate the results will be.
- Generates the rules of use and scheduling: This step is what constitutes algorithm development. For example:
Block if IP is X.
Each rule you create shows a score, and as they are executed you can strengthen or relax the activation conditions.
We design the rules thinking about how the registered customer could affect the transaction value lost due to fraud.
- Activate the rules you developed and train the algorithm
When you activate the rules, the algorithms start to operate, filter and analyze the data.
In some cases, it is possible to manually create and adjust thresholds to trigger or optimize the rules. Providing this feedback is key to obtaining more accurate results.
In the end, you will have algorithms detecting and predicting possible fraudulent actions from actions they reject or review.
- Verify that rules work through historical data
All good artificial intelligence and machine learning software for fraud detection should provide the ability to audit operations and pass-through data to verify that the rules have worked.
It is even feasible to do this through tests where you activate and deactivate the rules to verify their effectiveness and accuracy.
In the machine learning field, a confusion matrix, or error matrix, is a table design that allows visualizing the performance of an algorithm. By using this, you can calculate the algorithm’s accuracy in a specific date range.
Our AI and Machine learning for fraud detection Case Study
Our Data & AI studio consists of a team of professionals who have been developing intelligent algorithms and tools for various purposes for years, including fraud detection. In this opportunity, we want to share with you our success case.
Our champion team faced the challenge of detecting and preventing fraud and abuse with artificial intelligence and big data technology.
We proposed making an agency to incorporate artificial intelligence in the processes of fraud prevention and detection and abuse in medical benefits and medicines in 6 months.
WE DID IT!
We implemented algorithms based on machine learning with the aim of making fraud predictions, early detections, anomalies identification, search for fraud patterns in real time, authorization by AI with ML.
We use the following tech stack:
AI and machine learning for fraud detection is a reality that many companies worldwide are already enjoying to perform secure digital operations. If you want to be one of them, contact us, and let’s start another success story together.