FStech - Keeping up with fraud: How are financial institutions using technology to crack down on fraud risk?

1. What are the greatest fraud threats to your organisation? [select top three]

Authorised Push Payment (APP) scams
Customer first party fraud
Payments fraud (including emerging payments)
Insider / employee fraud
ID theft (Stolen Identity or Synthetic Identity Fraud)
Money mules
Card fraud
Account takeover fraud

2. What are the key challenges to effectively addressing fraud in your organisation? [select all that apply]

Legacy technologies
Lack of effective transaction fraud monitoring technology
Lack of effective onboarding controls
Lack of automation
Cost / budget constraints
Lack of data
Conflicting priorities across the organisation
Skill gaps / time to train

3. Which of the following processes or technologies are you currently using to tackle fraud? [select all that apply]

Identity verification
Biometric authentication (e.g. fingerprint facial recognition etc.)
Behavioural biometrics (e.g. how end user interacts with session)
Advanced data / behaviour analytics
Real time transaction monitoring
New account fraud detection (application fraud)
Dark web intelligence
Authentication management
Machine learning models
Network analytics and identity resolution
Email / Mobile / Device Intelligence

4. Which data sources are currently a priority for your anti-fraud operational teams? [select all that apply]

Internal customer data
Behavioural analytics data
Known fraud / watchlist data (includes industry databases)
Data breach / fake ID information
Email Intelligence
Mobile Intelligence
Device Intelligence
IP addresses / Geolocation Intelligence

5. Which authentication technologies or methods is your organisation currently using? [select all that apply]

Behavioural biometrics (e.g. how end user interacts with session)
Hardware / software tokens
Multifactor authentication
Voice biometrics
Document & facial verification
Fingerprint biometrics
FIDO2 (e.g. password-less authentication)
Onetime passcodes (SMS / Email)

6. Which of the following Authorised Push Payment (APP) / Authorised fraud typologies are a priority for your organisation? [select top three]

Purchase scams
Impersonation scams
Romance scams
Investment / Cryptocurrency scams
Advanced Fee scams
Invoice & Mandate scams
CEO Fraud scams

7. To what extent is your organisation using Artificial Intelligence (AI) and Machine Learning (ML) in its fraud strategy? [select one option]

We have no current plans to leverage AI and ML
We have started planning to use AI and ML
We are in the early stages of using AI and ML
We have made progress introducing AI and ML
We use AI and ML extensively

8. Which of the following emerging technologies are you planning to invest in? [select all that apply]

Biometric authentication (e.g. fingerprint facial recognition etc.)
Behavioural biometrics (e.g. how end user interacts with session)
Advanced data / behaviour analytics
Predictive money mule models
Dark web intelligence
Machine Learning models and Artificial Intelligence (AI)
Network graph analytics and entity resolution

9. How far advanced is your organisation in its cloud journey within fraud management? [select one option]

We have not started planning a cloud strategy
We are currently planning a cloud strategy
We are in the early stages of implementing a cloud strategy
We have started our cloud transformation strategy, but there is still work to be done
We have fully completed our cloud transformation strategy and have a cloud-first approach

10. What methods are your organisation using to address detection of money mules in fraud? [Select all that apply]

None currently
AML or Financial Crime team responsibility
Rule-based transaction monitoring with or without scorecards
Machine learning / AI models purpose built for mule detection at account opening
Machine learning / AI models purpose built for mule detection during transaction fraud monitoring
Third party / consortium hot lists / watch lists of known or suspected money mule accounts
Network graph analytics and entity resolution

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