Today’s businesses are facing unprecedented cybersecurity threats. Fraudulent activities cost organizations billions of dollars every year. Therefore, companies need to detect fraud efficiently and safeguard their data from potential cyberattacks. Organizations can use artificial intelligence (AI) data extraction for fraud detection to preemptively identify fraudulent behavior and other risks before they take place. AI data extraction is a type of machine learning that enables computers to analyze unstructured data, such as text documents, images, and audio files, by identifying keywords and phrases. With AI data extraction, businesses can easily find the relevant information they need while reducing the risk of exposure to cyberattacks. This article explains how AI can help businesses with fraud detection using AI data extraction and other types of AI.

What is AI Data Extraction for Fraud Detection?

Artificial intelligence is a computer system that can mimic human thought and decision-making. AI data extraction is the process of automating data extraction from unstructured data. It is also referred to as computer-assisted data extraction, automated extraction, and natural language processing. Using AI data extraction, computers analyze unstructured data, including text documents, images, and audio, to identify keywords and phrases. Computers can then use this information to extract data, such as a person’s name, address, and Social Security number. Businesses can use AI data extraction to quickly and easily search large amounts of unstructured data and find the necessary information while reducing the risk of cyberattack exposure.

How does AI Data Extraction Help with Fraud Detection?

Artificial intelligence can be used to identify suspicious behavior, such as the buying or selling of goods using stolen credit cards. One of the first uses of AI data extraction for fraud detection was identifying credit card numbers in customer receipts. If a computer system identifies a suspicious number, fraud investigators can contact the cardholder to confirm that the purchase was legitimate. Businesses also use AI data extraction to analyze unstructured data, such as customer records and online chats, to identify suspicious activity. For example, organizations can use AI data extraction to determine the frequency, pattern, and frequency of words used to predict future fraudulent activities. AI data extraction can also be used to analyze images and audio files to detect fraudulent activities, such as creating misleading advertisements.

Limitations of AI Data Extraction for Fraud Detection

Computer systems have difficulty detecting unusual or unexpected activities. For example, AI data extraction may not be able to identify unique or novel patterns of fraudulent activities. Fraudsters can take advantage of these weaknesses by creating new types of fraud, such as synthetic identities and synthetic statements. AI also has difficulty dealing with variations in language, such as slang and colloquialisms. Fraudsters can take advantage of these variations by using code words and other languages that computers cannot understand.

Additionally, AI data extraction can be fooled by novelty and novelty. Novelty refers to a new method that is not yet known by the computer system. Novelty can be used to disguise fraudulent activities.

Types of Artificial Intelligence Used for Fraud Detection

Natural language processing (NLP) – NLP is a type of AI data extraction that analyzes unstructured data. It extracts and identifies data based on keywords and phrases. To identify potential fraud, natural language processing can be used to analyze a wide range of unstructured data, such as customer service chats and emails, marketing materials, and product reviews. Statistical modeling – Statistical modeling is a type of AI data extraction that analyzes data sets to make predictions. Statistical models use algorithms to analyze incoming data and identify unusual or suspicious activities. Statistical models can analyze large volumes of data, such as transactional and financial records, to identify potential risks. Machine learning – Machine learning is a type of AI data extraction that enables computers to learn without being explicitly programmed. It can learn from past and new data to identify potential risks and accurately predict future outcomes. Machine learning can be used to analyze a wide range of unstructured data, such as customer records and product reviews, to identify potential fraud.

Conclusion

The power of AI data extraction for fraud detection is its ability to analyze unstructured data to identify keywords and phrases. This enables computer systems to easily extract data, such as a person’s name, address, and Social Security number, from any type of unstructured data. Businesses can use AI data extraction to quickly and easily search large amounts of unstructured data and find the necessary information while reducing the risk of cyberattack exposure. The D2 data extraction platform from www.deepdatum.ai provides advanced data extraction from digital and non-digital documents. To know more about how DeepDatum can help, email us at ask@deepdatum.ai