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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

Supply Chain


Distribution and Supply Chain Management is full of who, what, when, where, and why – forecasting and estimates are at the heart of an industry where margins are tight and mistakes can prove costly.  Market tension between manufacturers, wholesalers, and retailers  through the ongoing growth and dominance of E-commerce has only served to amplify the complexity of investment decisions. Perhaps the greatest challenge currently facing the industry is delivering higher rates of customer service and maintaining  loyalty while also keeping up with customer demand.

Today’s customer has a ‘click-buy-arrive’ mentality and whilst this is usually more associated with the retail consumer, this level of expectation has now become as much part of the B2B marketplace.  High availability of inventory, quick reaction time and the ability differentiate on service are key ingredients to success, but this aspiration can be expensive if not executed well.  There is also a growing consolidation trend with businesses choosing the competitive acquisition path to increase market share and support geographical expansion.

The Diveplane solutions provide businesses with next generation business intelligence with our AI/ML solutions that can provide the mix of real time tactical decision making with advanced strategic planning.

Learn how Diveplane AI solves this problem »
Diveplane AI for Supply Chain

Commercial Real Estate


Data availability is at an all-time high, but gathering the necessary resources to build comprehensive, sophisticated forecast models is still exceedingly difficult. Investors are always seeking to maximize their returns, but many commercial real estate firms simply lack the human capital to pore over dozens of datasets, wrangle the relevant data, clean it up, and make a prediction model out of it. In the time it takes to do that, all your investment opportunities have passed you by.

Ultimately, this analytic complexity leads firms to lean on current practices that favor familiar investment decisions. Today, the challenge is finding the right technology to exploit the vast quantities of industry related data and build confident recommendations, but still provide the opportunity to leverage the years of experience of the individual brokers and investors.

Learn how ALLUVION solves this problem »
Diveplane AI for Commercial Real Estate


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