BIG DATA ANALYTICS: ENSURING ACCURACY IN TRANSACTIONS
Machine learning algorithms play a major role in analyzing large volumes of transaction data. Researches state that about 10 percent of payments that insurance companies are liable for is related to fraudulent claims. This is where Big Data Analytics has a huge potential for detecting and preventing fraudsters. For example, insurance companies are actively discovering ways to improve their ability to make real-time decisions more quickly and accurately than ever before to optimize claim processing for their customers. They use big data analytics alongside machine learning to best serve both the customer and employee.
The machine learning algorithms take into account critical contextual information such as the device used, the customer’s location, etc. This makes decision-making accurate and efficient.
EXTRACTING CROSS-CHANNEL DATA: REDUCING TASKS OF FRAUD ANALYSTS
Fraud analysts leverage machine learning algorithms to empower their decision-making and to generate informed insights to overcome fraudulent attacks. Additionally, fraud analysts can efficiently utilize resources as data models which are constantly modified with new features of data extraction. Machine learning enhances a fraud analyst’s task of implementing fraud strategies. This involves implementation cycles, data access, ability to scale as the industry grows, and also to stay aware of fraudulent attacks.
REDUCING FALSE POSITIVES
A false positive error is the major concern of cybersecurity these days. Misinterpretation of a non-malicious activity leads to a false positive. The pressing concern with this issue is that a false positive is an incorrect security alert that leads to monetary losses. For instance, there are scenarios where an e-commerce site can omit a particular online buyer. This buyer but could be a real customer. Such situations lead to a reduction in Internet visibility.
AI and machine learning can avoid genuine customers from getting rejected as fraudsters, from trained patterns in data sets. This also minimizes the labor cost and saves time considerably.
MITIGATING COMPLIANCE RISKS
According to the “Report To The Nations On Occupational Fraud”, 5% of a company’s revenue is lost to fraudsters. This figure amounts to $140,000 per year as a median loss, annually. This is where Artificial Intelligence plays a major role. AI systems can help companies stay compliant with the local, federal and state standards.
In addition to external regulations, AI systems prove to be useful for internal policies as well.
Artificial Intelligence and Machine Learning are keys to effective and accurate credit card fraud detection and prevention mechanism in corporate firms. To learn more on how to tackle fraud challenges in your organization, drop a call and our experts can help you with a step-by-step guide on strategies for the same!