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
Identity Theft
61% of banks lack the ability to fully share client profile data for KYC reviews
There has been a more than 50% increase in identity theft crimes globally year over year
AML
Account Takeover
Between $800 billion (2%) and $2 trillion (5%) of the world’s GDP is laundered globally each year
Account takeover attacks increased 354% year-overyear
Phishing
Mule Accounts
Approximately 3.4 billion phishing emails are sent each day
47% of anti-financial crime professionals surveyed said money mule activity is a major concern
SIM Swap SIM swap fraud reports have increased by 400% in the past five years
KYB AI-based KYB verification boasts an accuracy rate of 98.67%
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
Devices
• Valid social security ID
Application
• N/A
• Extensive credit history on bureau
• Solid social media presence
• AI model suggests low risk of fraud
• 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
Devices
Application
• Valid social security ID
• Android device - up to date
• Limited credit history on bureau
• No email address
• Geo-location suggests phone is in locale of employment address
• Open banking check shows regular income received
• No social media presence • ID documents confirmed following document verification
• AI model suggests medium risk of fraud
• Phone number to name
• Some common links to other frauds
Internal Check
CRA Check
Result: MEDIUM-RISK
Identity Check
Document Check
Fraud Check
Device Check
Fraud Rules
Credit Risk 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.
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
Devices
Application
• Invalid social security ID
• Jailbroken phone
• No credit history on bureau
• Email which has been linked to multiple frauds
• Presence of malware
• Open banking check shows account does not belong to named applicant
• Inability to validate KYC checks
• Geo-location suggests phone is outside country of application
• No social media presence
• Newly registered SIM
• Applicant linked to other frauds
• Phone number to name mis-match
Internal Check
Result: HIGH-RISK
CRA Check
Identity Check
Document Check
• No income received in the account • AI model suggests high risk of fraud
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.
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Application Fraud - Component Parts
DECISION INTELLIGENCE
AI-powered insights to understand and optimize strategy performance.
DYNAMIC DECISIONING
A single hub to analyze & minimize risk and maximize reward.
FRAUD & KYC STRATEGY-FRIENDLY
With low-code UI, business owners can easily review, modify, and simulate.
DATA INTEGRATION
Embed third-party data knowledge and strategic understanding of fraud scenarios.
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