Across all industries, fraud and financial crimes are on the rise, causing losses that collectively reach into the trillions of dollars each year. Legacy fraud prevention platforms have limitations that result in too many false positive alerts, loopholes that allow the fraudulent activity to go undetected, and determinations of fraudulent activity after the fact when it’s too late to prevent the loss.
A key factor vexing legacy fraud management systems is the emergence of so many different channels of potentially relevant data that reside in completely different systems and formats. A hacker could be creating fraudulent accounts and transactions on one system, and they can’t be correlated with activities or behaviors on the other systems because the fraud platform has no way to link and associate the data that resides in incompatible file systems and formats.
- Leverage a range of link analysis algorithms
to associate transactions across channels and multiple accounts to an identity like customer ID or user
- Get a 360-degree visibility into all accounts
instruments and transactions performed by the user
- Identify, prioritize, and alert on high-risk activity
- Build a cross-channel behavior profile
with advanced machine learning algorithms to detect any deviation based on real-time activities like abnormal SWIFT transfers, excessive funds transfers, unusual debit card usage and card not present transactions
- Drive corrective or response actions
in other systems based on the value of the risk score
Combine access and transaction data from multiple channels into data lakes where both machine learning and advanced analytics can be applied to derive meaningful relationships in real or near real time.
Centralize monitoring across multiple channels to detect and prevent fraudulent activities that may appear benign in isolation.
Detect automated-account (i.e., bots) cross-channel fraud.
Support the businesses without slowing down the customer experience.
Gurucul leverages a range of modern technologies from Big Data to machine learning to analyze millions of datapoints from a variety of siloed, cross-channel sources, such as ticketing systems, phone systems, core banking systems (CBS), and even public databases – all linked to a user identity. By ingesting and linking vast amounts of data from these disparate sources, our platform can identify anomalous behavior in real time, so that companies can intervene before a financial loss occurs.
TOP USE CASES
Account Takeover & Login Fraud
Stopping malicious actors before an account takeover breach requires advanced fraud analytics capabilities including detection of suspicious web and mobile devices, identification of logins from high-risk unusual locations and networks, ID reconnaissance, and unusual password resets or potential brute-force attacks.
Credit Card Fraud
Detect outlier risky behavior indicative of credit card fraud such as unusual account or profile changes, abnormal high value transactions, geo-location anomaly, unusual device usage, and suspicious charges from merchant.
Healthcare Provider Fraud
Identify billing for services not provided, billing for non-covered services, falsifying service data, abnormal waiving of deductibles and/or co-payments, incorrect reporting of diagnoses or procedures, unnecessary drug prescriptions and more.
Healthcare Consumer Fraud
Detect medical identity theft, false insurance claims, unusual claims submissions from numerous geolocations/accounts, and HSA Account Takeovers.