LexisNexis Risk Solutions’ latest self-calibrating, AI-powered risk scoring solution is helping identify more actual fraud and reducing the volume of low-risk transactions being flagged as fraud.
In testing, the fully AI-model based architecture analysed email, IP, phone, address and other signals, capturing around 90% of frauds amongst the highest risk transactions while reducing false positives by more than 80%.
Emailage Adaptive
According to the company, Emailage Adaptive is a fully AI-enabled solution that continuously learns from each organisation’s unique set of transaction data, fraud patterns and industry context.
It responds by building iterative tailored fraud prevention models that automatically recalibrate with no manual intervention, to generate more precise risk scores and support better predictive accuracy.
LexisNexis explained that the solution has been designed to operate across a range of industries and high-risk scenarios, such as account opening, account management and payments.
With consumers having 7.9 billion email accounts globally and one third keeping their email address for more than 10 years, analysing history and patterns of usage can provide powerful and unique insights when used alongside other risk intelligence.
“Calibrate less, act faster and approve more”
Jeremy Cole, director, business operations, Dell Technologies explained: “The self-calibrating AI model has helped us modernise fraud prevention by minimising reliance on static rules and manual policy adjustments.
“Our fraud team can now calibrate less, act faster and approve more legitimate transactions with confidence.”
Launching the solution
The company highlighted that the latest launch comes at a time when fraud costs are on the rise.
One in every 11 new account creations globally is a fraudulent attempt, according to LexisNexis Risk Solutions Cybercrime Report 2026.
Built firmly on principles of explainability, the AI model-based architecture of Emailage Adaptive relies on a regular flow of customer transaction data, industry trends and emerging fraud patterns to drive a continuous improvement loop.
This reliable data allows models to refine over time and generate explainable risk scores supported by clear reason codes, added LexisNexis.
“Continually enhance predictive accuracy”
Kimberly Sutherland global head of fraud and identity at LexisNexis Risk Solutions commented: “As generative AI scales, fraud attacks are now happening at a faster rate than most manual prevention strategies can keep pace with, overwhelming systems and exposing businesses to ever greater risk.
“By continuously learning from fraud outcomes, backed by cross industry risk intelligence, a self-calibrating fraud model can adjust instantly to subtle changes in fraud patterns, removing the lag between when patterns emerge and when defences calibrate.
“This allows fraud teams to continually enhance predictive accuracy while reducing manual workload,” she concluded.
Notable features
According to the company, the notable features of the solution include:
- Tailored AI models utilise global transaction data and substantiated customer feedback, automatically recalibrating as new patterns are confirmed
- Transparent scoring provides clearreason codes indicating whether a specific factor positively or negatively influenced the model’s output
- Responsible AI by design supports explainability, helps mitigate bias and helps protect data privacy, drawing on reliable data sources
Fraud Prevention Award
Emailage Adaptive earned the 2025 Platinum Award for AI in Fraud Prevention Innovation and the 2026 Platinum Award for Payments Fraud Prevention Innovation.
According to LexisNexis, both awards from Juniper Research, recognise the solution for its adaptive AI capabilities and ability to deliver precise risk assessments alongside seamless customer experiences.