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Financial Services


Two of the biggest problems facing the financial industry today are data security and fraud. Breaches are happening left and right, and millions of real people have their identities compromised with each leak. It’s nearly impossible to close all the backdoors that exist, and hackers keep getting craftier with their attacks. What if, in addition to doubling down on closing those backdoors, financial institutions could create honeypots of artificial, synthetic data to fool would-be hackers into stealing data that looks eerily real, but isn’t? This would create a deterrent dividend – potential hackers would be afraid of detection.

Better yet, what if an institution could synthesize all of its data in such a way that the important information was retained without containing personally identifiable information, and this could be done prior to sharing it internally? This data becomes much less valuable to a hacker, and simultaneously becomes much less of a risk for the institution to handle on less secure channels.

With the average cost of fraud rising year over year, companies are scrambling to find methods that allow them to detect questionable activity earlier and more effectively. Fraud comes in many forms – money laundering, credit card applications, loan applications. Financial institutions process thousands of transactions every day, which is far too many for a human to look over and identify fraudulent activity by eye.

Not to mention that fraudsters are getting more creative. Anomalies are very rarely obvious enough to notice by eye anymore. Most perpetrators are getting sneaky and making their transactions look similar to other, nonfraudulent ones. Without the presence of an obvious outlier, how can financial institutions know that a transaction is suspicious?

Learn how Diveplane’s GeminAI™ solves this problem »
Diveplane AI for Finance



One of the biggest challenges in the healthcare industry is the lack of ability to share patient data. Medical data is mired heavily in regulation, and analysts are often unable to directly access data due to its highly sensitive nature. Because of this, most electronic medical records exist in incredibly segregated silos and are inaccessible to almost everyone except for the practitioners in direct contact with the patient in question.

One solution would be to reliably synthesize this data to remove all real records of personally identifying information while creating new, artificial entities that mirror the original data. This would allow a greater number of analysts across disciplines and institutions to collaboratively reveal the insights hidden in the troves of unanalyzable medical data that exists today.

Learn how Diveplane’s GeminAI™ solves this problem »

Another problem plaguing the healthcare industry is that of insurance fraud. Some healthcare insurance providers process billions of claims per day, sometimes with multiple lines per claim, with complex denial, resubmission, and approval processes. The amount of information is too much for a human to handle visually, and even most AI systems aren’t sophisticated enough to find the worst anomalies.

The most common form of fraud is not the obvious, outrageous claim that is obviously overpriced. Healthcare insurance fraud is more insidious than that – taking place primarily in the form of duplicate claim submissions, resubmissions of denied claims with slight tweaks, and a cumulative sum of money built on small overcharges over the course of millions of claims submissions.

Learn how Diveplane AI solves this problem »
Diveplane AI for Healthcare


Artificial Intelligence (AI) should always be accountable to humans, especially to defense policymakers, commanders, and soldiers when the application of lethal force is possible or probable. These are the most consequential scenarios for AI – machine learning predictions or decisions that are matters of human life and death.

Because of the critical significance of military operations, AI-powered predictions or decisions should always provide transparent and auditable answers about why a certain prediction or decision was made, what the important factors were in making that prediction or decision, and how confident the model is in that prediction or decision.

Diveplane’s patented technology is capable of answering these questions and more, which is critical in helping keep powerful AI accountable to human authority.

Furthermore, with human oversight, Diveplane AI applications can continue to learn after the platform is deployed, comparing its predictions against real-world results that military staffs and/or remote sensors feed back into the platform. Diveplane AI learns from any discrepancies between the two to become even more accurate over time.

Learn how Diveplane AI solves this problem »
Diveplane AI for Defense