Within organizations, fraud professionals and those responsible for revenue growth have been working against each other for decades. You can stop fraud almost completely of course, by making it nearly impossible for your customers to transact by placing
limits on the transaction or transfer amount, excessive manual reviews, and requesting additional authentication information that leads to abandonment.
Or you can maximize revenue by taking on more risk by minimizing the consumer security steps and security defenses.
But what if we truly knew our customers better, not in a regulatory sense, but knew that their identity or persona changes dynamically depending on whether they are working, traveling, vacationing, or spending their weekend with their family.
Artificial intelligence has evolved to where such platforms can process at a speed and scale that processes billions of real-time signals to confirm a user’s true identity.
The technology now exists that provides enhanced identity verification and threat prevention techniques, allowing a digital organization to reduce friction & improve consumer experience with instant identity verification & onboarding.
Artificial intelligence decisioning platforms can leverage at massive scale behavioral, contextual, personal, and relationship signals. This enables real-time identity verification based on an advanced understanding of how an actor’s behaviors change as
it moves through time and space. Identity has changed in the digital age to be:
- Personas (who we are)
- Relationships (who we know)
- Context (signals around us)
- Behavior (what we do)
From a fraud perspective, gaining a holistic view of customer behavior, pattern recognition, and login history enables businesses to better understand the customer attempting to access their account or perform a transaction. Artificial intelligence ensures
the highest chance of an accurate authentication decision without compromising the customer experience by aggregation of behavioral, contextual, relationship, device, and personal signals as well as 3rd party and proprietary sources. Capturing and analyzing
transaction data in real-time, these types of machine learning platforms score the event leveraging a myriad of data sensors to enhance the accuracy of the score.
From a revenue perspective, artificial intelligence platforms are being used even by banks and financial institutions to predict how to better serve customers. Examples include predicting the customers next product, giving better financial advice, or helping
consumers plan and save for payments. AI can provide a comprehensive solution to enable more compelling consumer experiences. The same platform used for identity and risk can be used to create consumer specific models, integrating digital and legacy data sets
for identifying consumer demographics, consumer sentiment, consumer behaviors, and other applications. Designed correctly, AI enables greater insights into current consumer interactions and provides methods for enhancing those interactions. This creates new
opportunities for truly compelling consumer experiences: hyper-personalized customer processes that facilitate consumer shopping decisions. And, knowing that identity or “persona” also creates a frictionless experience for our true customers which increases
revenue potential while mitigating risk.
Conclusion
Understanding and recognizing your consumer from a 360-degree view, both with respect to risk and fraud mitigation, to their propensity, motivation to buy, and predicting their next product need is now possible using the same technology. That is a complete
departure from the legacy world of viewing the consumer in silos focused uniquely on fraud or revenue, but never truly understanding the customer holistically.
Michael Lynch is the Chief Strategy and Product officer at Deep Labs, Inc.