Eliminating False Negatives Using Analytics

false negatives is opposite of a false positive test. While these two terms are often used in the medical field.

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Circa 2020. The COVID-19 pandemic was spreading rapidly across the globe, and the healthcare infrastructure in several countries was struggling to cope with the influx of patients awaiting the results of an RT-PCR test. It was during this period one frequently heard the usage of two words: false positive and false negative.


For the uninitiated, a "false positive" test happens when there is a positive result for an outcome that is supposed to be negative. A "false negative" test is the opposite of a false positive test. While these two terms are often used in the medical field, they also have implications in software testing, finance, and cyber security. This article will discuss the impact of false negatives and how to eliminate them using analytics.  

The Trouble With False Negatives

False negatives can have severe consequences, such as missed diagnoses, missed opportunities, and increased risk. For banking and financial sector companies, if an incident or a transaction is flagged as "false negative," it implies that no crime or fraudulent activities have occurred. Since no offense is committed, the investigators often overlook such transactions. And when such frauds are detected, they damage the banks' credibility in the eyes of their customers and stakeholders.


Anti-money laundering (AML) compliance is a requirement for banking and financial institutions to ensure that the system is not used to launder "dirty" money. For example, imagine a scenario involving false negatives where a well-known bank's security software fails to detect money-laundering activities during routine transactions. The result would be that regulators would clamp down on the bank, and the bank's reputation and financial stability would be at risk. Worse, such incidents undermine the security and compliance measures the bank implemented in the system.

The prevalence of false negatives while screening for diseases had grave implications for the patients. During the early days of COVID-19, any individual who exhibited cough and fever symptoms was recommended to get tested for COVID-19. The test results returned negative in several instances, but the patient would still suffer from the disease. Such a scenario meant that the affected patients could not receive treatment and recover on time as their test results were negative. Not only were the patients carrying the disease, but because of the highly infective nature of the SARS-CoV-2 virus, they would spread the disease among their family and the immediate neighborhood.

False negatives in cyber security refer to instances of threats undetected by the security system, either due to ill-equipped security measures or the level of sophistication involved in the attack. As in the case of banking and financial companies, false negatives impact cyber security along similar lines. Massive data breaches, loss of intellectual property, ransomware infections, and so on are some of the consequences caused by false negatives.  


The proliferation of mobile internet and broadband connectivity has boosted online shopping, and e-commerce marketplaces have emerged as the go-to destination for tech-savvy shoppers. Unfortunately, the convenience delivered by online shopping has given rise to the problem of false negatives or true positives. These occur when online merchants fail to detect or flag fraudulent transactions, and these bypass the merchants' fraud detection systems. This is the opposite of false positives, where merchants decline transactions as they suspect fraud. In both instances, the merchants suffer due to loss in brand reputation, monetary losses, and lost customers.

The Helping Hand of Analytics

As mentioned in the previous section, false negatives can result in misidentifying and misdiagnosing ailments and diseases among patients, loss of reputation among banks and financial institutions, and exposure to security systems and cybercrime attacks. In such a scenario, analytics is critical in identifying and eliminating false negatives. Analytics is all about discovering and communicating meaningful patterns in data. It helps businesses make informed decisions on how the data can be used for deriving benefits, increasing sales, and reducing costs. Let us examine a few use cases for analytics in managing false negatives.


Time is of the essence for banks and financial institutions as analytics reduces the need for manual reviews, enhances the speed and accuracy of processes, and lowers the overall cost of operations. Analytics can handle a large volume of data accurately. Banks and financial institutions are inundated with a vast amount of data daily, which they need help processing. When these institutions rely on traditional data analysis methods, they often become time-consuming and error-prone. With its algorithms and data models, analytics can handle a significant portion of data and identify patterns or anomalies undetected by humans. In addition, Analytics is cost-effective and scalable in identifying false negatives.

The healthcare sector can benefit from analytics as it identifies abnormalities and changes, which could indicate the presence of a severe condition or a disease. With analytics, healthcare professionals can monitor vital signs among patients, which helps them identify the underlying causes and respond accordingly. Furthermore, analytics can eliminate the threat of false negatives in cyber security by examining network traffic, log files, user behavior, etc. It can also provide real-time insights into emerging threats and help quickly eliminate them. Additionally, analytical models can be constantly updated and enhanced based on feedback and data, which allows them to fine-tune their responses to changing circumstances.

Analytics can help e-commerce marketplace in identifying false negatives. Machine learning models with fraud detection tools and with sufficient rules inbuilt can easily classify and detect them. Another popular method in machine learning to deal with false negatives is implementing a decision tree algorithm. This enables merchants to deliver a warning to the customers, suspend them, or de-platform them permanently for indulging in fraudulent transactions. However, the biggest challenge in a decision tree is the changing and evolving fraud patterns. As a result, despite stringent thresholds, merchants may penalize at least 5% of the "good" buyers from their marketplaces.


Leading the Way

Businesses rely on vast amounts of data regularly. In such instances, the chances of false negatives are significantly high. Suppose left undetected or overlooked, false negatives impact business transactions and disease transmission, resulting in data breaches and loss of reputation. Analytics is a valuable tool for identifying and eliminating false negatives and is essential for effective decision-making.

By Anup Gunaseelan, Director, Delivery Head (Tech Vertical), LatentView Analytics