Maximize Value Across the Entire Customer Lifecycle
Stop Fraudsters in Their Tracks
How an AI-Powered Decisioning Platform Can Optimize Your Fraud Data Orchestration
As fraud threats evolve, how can financial services organizations keep up? One key is how you orchestrate and integrate your data. There is no shortage of fraud data providers available but using them wisely is trickier. Are you able to easily consolidate disparate sources into a single stream of usable data to integrate into your decision-making process? Because the better you get at optimizing your data and preventing fraud, the more confident you can be in your decisions - and enable sustainable business growth.
KYC 61% of banks lack the ability to fully share client profile data for KYC reviews AML Between $800 billion (2%) and $2 trillion (5%) of the world’s GDP is laundered globally each year Mule Accounts 47% of anti-financial crime professionals surveyed said money mule activity is a major concern KYB AI-based KYB verification boasts an accuracy rate of 98.67%
Identity Theft There has been a more than 50% increase in identity theft crimes globally year over year Account Takeover Account takeover attacks increased 354% year-over- year Phishing Approximately 3.4 billion phishing emails are sent each day SIM Swap SIM swap fraud reports have increased by 400% in
the past five years Synthetic ID
Synthetic identity fraud was the fastest growing form of fraud in 2024
Can you identify the bad actors?
Name: Sanjay Occupation: Chef Address History: 8 years Credit History via Bureau: Valid Social Security ID: Valid KYC Check: Valid Social Media: Valid/normal presence First-Party Fraud Check: Low-risk of not paying
Identity • Valid social security ID
Devices
Application • Extensive credit history on bureau • AI model suggests low risk of fraud
• N/A
• Solid social media presence • KYC checks confirm Idenity • No links to other frauds
Internal Check
CRA Check
Identity Check
Document Check
Fraud Check
Device Check
Fraud Rules
Credit Risk Rules
All checks indicate Sanjay is a genuine person, and first-party AI fraud models show no indicators of him not intending to pay. Application approved without any more required information.
Result: LOW-RISK
Name: Elize Age: 50s Time in Country: 15 months Credit History: Limited Device Check: No suspicious indicators;
phone number matches name/ address
Social Media/Email: None found
Flag: Address and similar name
previously associated with fraud
Identity • Valid social security ID • No email address • No social media presence • ID documents confirmed following document verification • Some common links to other frauds
Devices • Android device - up to date • Geo-location suggests phone is in locale of employment address • Phone number to name
Application • Limited credit history on bureau • Open banking check shows regular income received • AI model suggests medium risk of fraud
Credit Risk Rules
Internal Check
CRA Check
Identity Check
Document Check
Fraud Check
Device Check
Fraud Rules
Using an AI model, the application was scored with additional info as being a medium risk for fraud, and borderline from a credit risk perspective. After manual review and a personal phone call, the bank approved her application.
Result: MEDIUM-RISK
Name: Mr. T. Liefe Occupation: Career Criminal Credit History: None found Email Check: Email address newly created, used in high velocity Social Media: Minimal presence Mobile/Device Data: Phone located in different
country, SIM recently registered to someone else; jailbroken phone running malware
Identity • Invalid social security ID • Email which has been linked to multiple frauds • Inability to validate KYC checks • No social media presence • Applicant linked to other frauds
Devices • Jailbroken phone • Presence of malware • Geo-location suggests phone is outside country of application
Application • No credit history on bureau • Open banking check shows account does not belong to named applicant • No income received in the account • AI model suggests high risk of fraud
• Newly registered SIM • Phone number to name mis-match
Internal Check
CRA Check
Identity Check
Document Check
Fraud Check
Device Check
Fraud Rules
Credit Risk Rules
Given the number of red flags, his bank declined his application without undertaking any further checks, avoiding the need for manual case review.
Result: HIGH-RISK
Application Fraud - Component Parts
AI-powered insights to understand and optimize strategy performance.
DECISION INTELLIGENCE
DYNAMIC DECISIONING
A single hub to analyze & minimize risk and maximize reward.
With low-code UI, business owners can easily review, modify, and simulate.
FRAUD & KYC STRATEGY-FRIENDLY
Embed third-party data knowledge and strategic understanding of fraud scenarios.
DATA INTEGRATION
Optimize your data orchestration and fraud detection/prevention with a holistic, end-to-end fraud risk decisioning platform that allows you to integrate a variety of data sources, continually improve your fraud risk models, and optimize fraud decisions as threats evolve – all alongside your risk decisioning for the elimination of siloed environments and enabling maximum flexibility and agility in your risk decisioning. Discover more accurate fraud risk detection with a more holistic, comprehensive view of your customers.
Get the Data Sheet
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