Business Challenge
Cumbersome Experiences Lead to Abandonment
Financial institutions with long
account opening processes and a
poor customer experience fall
short of expectations – resulting in
abandonment rates as high as
95%
Application Fraud is at an All- time High
Data breaches make it easy for
fraudsters to open accounts with
stolen information. Without the
ability to detect fraudulently
identities in real time, Financial
Institutions will continue to incur
losses
Compliance Requires the Right Tools
Financial Institutions that cannot
validate the identity of remote
applicants and prove that they
followed a compliant process risk
fines, reputational damage, and
other impacts on the business
Solution opt in
Quickly Verify Identities Remotely
Detect manufactured or synthetic identities with
multi-layered digital identity verification
methods.
Ensure the Authentic Applicant is Present
Use facial comparison to determine whether a
remote user is authentic and present.
Capture Customers’ Consent Electronically
Automate signing workflows and capture
consent with legally binding e-signatures.
Protect Against Mobile Fraud
Thwart unauthorized applications for new
credit products and stop automated malware or
bot activity.
Leverage Digital Audit Trails
Capture a record of everything the applicant saw and did during the process, for compliance purposes.
How it Works
1. The customer initiates a financial transaction
Once a customer initiates a transaction, Risk Analytics collects and analyzes data from a variety of different data sources, including:
• Devices –Endpoint–centric data monitoring at the device level
• Behavior –Analyzes interactions with the device as well as session navigation behavior such as the speed and time of browsing, in order
to identify suspicious activity
• Historical –Analysis of user and account activity in a digital channel, on a historical basis
• Multi–channel –Analysis of user behavior across multiple channels, devices, and applications
• Business applications –Analysis of financial and third–party application data.
2. Additional customer account data is sent for contextual analysis
Financial institution sends additional customer account information to Risk Analytics for contextual analysis.
3. Analyzes and scores user, device, and transaction data across multiple digital channels in real–time.
To determine the risk associated with each financial transaction, Risk Analytics leverages machine learning and data modeling to analyze and
score user, device, and transaction data points across multiple digital channels in real–time.
4. Based on the risk score, appropriate action it taken.
Based on the risk score, Risk Analytics automatically takes appropriate action:
• Allow: Low risk score –Allows the financial transaction to continue
• Review: Medium risk score –Creates an activity case for review; more customer validation is required
• Block: High risk score –Blocks the transaction and creates an activity case for review.
5. Transaction risk score is low. Funds are allowed to be transferred.
The transactional risk score is low, Risk Analytics allows the funds to be transferred.