Fraud prevention is an ever-changing, rapidly moving industry that demands innovation occurs at a staggering pace. Founded in January of 2016, Precognitive addresses a gap that was quickly expanding in the fraud detection and prevention industry. The technology being built for malicious purposes was evolving faster than technology being built to prevent fraudulent behavior. Precognitive was created to thwart this trend and to stop online fraud before it happens.

The actual threat posed by online fraud is far from the popular stereotype of dark-hooded hackers stealing ATM numbers and cracking passwords. The current driver of the online fraud economy is actually organized crime, and they’re operating fraud rings as if they are Fortune 500 companies. They have the infrastructure necessary to build and deploy products at the same pace and scale of most agile start-ups.

The criminal target: the entire online economy of over $2.5 trillion per year.

These fraudsters are increasingly sophisticated, making it difficult for current solutions to catch them. This situation invariably makes companies set up stringent transactional guidelines in order to prevent fraud, which leads to higher decline rates where legitimate consumers are rejected. In today’s omni-channel world, a negative impact on the customer journey has real consequences that extend far beyond a single transaction. Rejecting good customers will likely send them straight to a competitive site, or worse, could end up causing social media rants, extending the impact of a negative experience to an entire community.

Current fraud solutions often rely solely on the transactional data entered by the user at the time of an event to detect fraudulent activity. For example, a popular solution is to verify inputs such as matching the billing and shipping address to the card information and the IP address. These same data inputs are readily available to fraud rings, and enable an easy bypass to the entire fraud detection solution. This one-dimensional approach marketed as a solution lacks the sophistication necessary to stop even the most novice cybercriminals. While transactional data is important, it’s not enough.

Precognitive takes a multi-dimensional approach to fraud detection and prevention. Precognitive combines device intelligence, advanced behavioral analytics, and a real-time decision engine to accurately detect and prevent fraud for online businesses. This holistic methodology enables organizations to detect and prevent fraud at the time of each transaction, while enabling a smooth customer journey for legitimate consumers.

For the last 18 months, we have been meticulously building our fraud detection and prevention tools as easy-to-deploy b2b SaaS solutions. We’re happy to announce we have closed $1.25 million in seed funding led by Corazon Capital with participation from Flybridge Capital Partners, Hyde Park Venture Partners and Jeff Liesendahl, the co-founder and former CEO of Accertify.

We’re looking forward to continuing to build our modular tool set to further empower companies to solve a wide range of fraud and authentication challenges. We’re also focusing on facilitating the easy adoption of Precognitive’s agile platform to continuously outpace and outsmart fraud from every angle.

Fraudsters accessing your site aren’t thinking about these behaviors either. This gives us an advantage. By aggregating and analyzing each user’s subconscious habits, we can identify and prevent fraudulent activity before it happens.

Online criminals are becoming more tech-savvy with readily available tools on the black market aiding them. These tools help them keep their activity hidden, but behavioral traits are the weakness that they can’t mask. Perpetrators often act quickly, with their own unique behavioral traits and patterns. By leveraging behavioral analytics, we can effectively identify and track these online behaviors, which clearly differ from legitimate consumer patterns.

Behavioral analytics is a distinctive tracking and identification system for fraud prevention. It analyzes behavioral traits that are unique to each consumer or potential fraudster, including usage patterns and physical factors that are virtually impossible to emulate.