Infographic
Published April 1, 2025
Banks Credit Unions
Loan loss analysis is an effective way for lending institutions to drive smarter business decisions by identifying loans that have been lost, where they’ve been booked, their key characteristics, and how they’ve performed.
With loan loss analysis, you can gain visibility into:
- What type of financial institution did that applicant book with (bank, credit union, finance company, etc.)?
- What was the average loan amount, and were others offering more or less?
- What was the estimated interest rate, and how did it compare to yours?
- What was the loan term length?
- What was the average risk score?
Accessing this information can help your organization validate your lending policies or find out where you can make adjustments to remain competitive in the market and win more business.
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The imposter in the session: Limited structure, limitless scalability
Dive into a large-scale account takeover (ATO) attack where loosely coordinated fraudsters use low-touch logins to validate credentials and harvest data across 100+ accounts. Learn how unstructured fraud rings achieve rapid scale, why behavior is the hardest signal to fake, and how multidimensional analysis exposes hidden connections to stop attacks early.
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The imposter in the session: The power of one
See how a single attacker leveraged fraud ring tactics to compromise over 50 accounts in under 30 days—and how to detect and stop solo account takeover attacks at scale.
Key insights:
- Lone fraudsters can replicate fraud ring tactics using playbooks, tools and stolen credentials
- A single device can target dozens of accounts rapidly with consistent, repeatable actions
- Behavioral, device and location signals help distinguish imposters from legitimate users
- Stopping high-risk devices early prevents attacks from scaling across multiple accounts
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The imposter in the session: The hub and the hive
Uncover a coordinated account takeover fraud ring in action and learn how it was stopped using behavioral, device and network intelligence.
Key insights:
- Fraud rings operate with structured roles to efficiently execute account takeover attacks
- Compromised accounts can be connected across devices, revealing coordinated activity
- Behavioral, device and network intelligence expose fraud signals traditional tools miss
- Stopping ATO requires identifying and dismantling the full fraud ring, not just individual logins
Case Study
How one lender achieved a 5 month payback
Education Credit Union modernized underwriting with Experian’s Advanced Decisioning—driving faster decisions, stronger approvals, and rapid ROI in a competitive market.
- 5-month payback period
- 4.4% lift in performance & funding
- $4M+ incremental originations