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Real-time fraud detection (Session + Demo)
Stu Ward, Director of Financial
Services APAC, Confluent
James Gollan, Enterprise Solutions
Engineer ANZ, Confluent
Fraud and Scams major issue for Australians
Which industries are impacted?
What is the cost of
recovery?
Every $1 of fraud now
costs the industry $4.
Why can’t companies stop fraud, despite having made
massive investments in technology?
Organizational design has led to
operational silos.
● Fraud teams and Cyber Security operating
independently.
01
Fraud teams typically work with
structured data from the GL or
databases, watch lists etc.
02
Cyber Security teams typically work
with unstructured data sources
from logs. This data is rich in clues,
but often the fraud teams don’t
have access to it.
03
Many fraud detection systems
focus on individual types of fraud,
e.g. transaction fraud.
04
Fraud attacks come in many forms
Phishing & Smishing Insider threat Scams
Market abuse and
Rogue trading
Invoice and Payroll
fraud
Application fraud
Stolen cards &
Stolen credentials
ATM / IDM fraud
By Now Pay Later
(BNPL) scams
Money laundering Tax fraud
Insurance claims
fraud
Claims
exaggeration
Insurance premiums
application fraud
Benefit fraud …
Cheque fraud Crypto scams Gift-card fraud
Cyber security
breaches & attacks
Transaction fraud
Cross-border
payments fraud
Account takeover Synthetic identities
The impact of fraud goes beyond just financial losses. Damage to reputation and brand hinders a firm’s ability
to attract and retain customers, partners and employees, impacting their bottom line and crippling business
What evidence
do financial
crime-fighters
look for?
Real-time
Two key requirements for detecting fraud
Contextual
Confluent Cloud fraud demo scenario
Website Logs (HTTP)
Login Attempts
IP & Geo-data
Watch lists
Payments & Transfers
Enrich
Route
Filter
Join
Detect
Govern
Super-TRAP
Score
Login & BOT Scores
Payment &
Transfer Scores
Account TRAP* scores
Topic 1
Topic 2
Topic 3
Topic 4
Topic 5
Topic 6
Calculate TRAP A
Calculate TRAP B
Calculate TRAP C
Calculate TRAP D
TRAP SCORE
TRAP SCORE
Source
Continuously
stream data to
Confluent
ADD TO WATCH
LISTS
DOWNSTREAM
ALERTING
INVESTIGATIONS
DASHBOARDS
Track
Event driven
immutable data
Process
Enrich & cleanse,
continuously calculate
risk scores
Assess
Examine temporal
Scores
Share
Outputs and alerts
HTTP LOGS
AUTH LOGS
TRANSACTIONS
WATCH LISTS
*TRAP = Temporal Risk Analysis Probability - (Risk Indicators)
Indicative SQL
90
Indicative SQL
91
High Level Diagram
Fraud
Management
eBook
Thank You
Stream Designer flow for Transfers & Payments
95
Stream Designer flow for Transfers & Payments
96
Transfers & payments lineage
97
Account TRAP scores
Account TRAP Alerts
Afternoon Reception &
Partner Expo
Getting started with Confluent Cloud is easy!
Have your teams access a
free Confluent Cloud
account!
*Free credits worth $400 during the first 30
days for all new sign ups.
All the resources you need to
set your Data in Motion,
in one place.
Revisit today's materials and gain access to
on-demand webinars and toolkits.
Thank You
Lucky Draw
Raffle through your stamped passports!
Real-time fraud detection

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Real-time fraud detection

  • 1. Real-time fraud detection (Session + Demo) Stu Ward, Director of Financial Services APAC, Confluent James Gollan, Enterprise Solutions Engineer ANZ, Confluent
  • 2. Fraud and Scams major issue for Australians
  • 4. What is the cost of recovery? Every $1 of fraud now costs the industry $4.
  • 5. Why can’t companies stop fraud, despite having made massive investments in technology? Organizational design has led to operational silos. ● Fraud teams and Cyber Security operating independently. 01 Fraud teams typically work with structured data from the GL or databases, watch lists etc. 02 Cyber Security teams typically work with unstructured data sources from logs. This data is rich in clues, but often the fraud teams don’t have access to it. 03 Many fraud detection systems focus on individual types of fraud, e.g. transaction fraud. 04
  • 6. Fraud attacks come in many forms Phishing & Smishing Insider threat Scams Market abuse and Rogue trading Invoice and Payroll fraud Application fraud Stolen cards & Stolen credentials ATM / IDM fraud By Now Pay Later (BNPL) scams Money laundering Tax fraud Insurance claims fraud Claims exaggeration Insurance premiums application fraud Benefit fraud … Cheque fraud Crypto scams Gift-card fraud Cyber security breaches & attacks Transaction fraud Cross-border payments fraud Account takeover Synthetic identities The impact of fraud goes beyond just financial losses. Damage to reputation and brand hinders a firm’s ability to attract and retain customers, partners and employees, impacting their bottom line and crippling business
  • 8. Real-time Two key requirements for detecting fraud Contextual
  • 9. Confluent Cloud fraud demo scenario Website Logs (HTTP) Login Attempts IP & Geo-data Watch lists Payments & Transfers Enrich Route Filter Join Detect Govern Super-TRAP Score Login & BOT Scores Payment & Transfer Scores
  • 10. Account TRAP* scores Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Topic 6 Calculate TRAP A Calculate TRAP B Calculate TRAP C Calculate TRAP D TRAP SCORE TRAP SCORE Source Continuously stream data to Confluent ADD TO WATCH LISTS DOWNSTREAM ALERTING INVESTIGATIONS DASHBOARDS Track Event driven immutable data Process Enrich & cleanse, continuously calculate risk scores Assess Examine temporal Scores Share Outputs and alerts HTTP LOGS AUTH LOGS TRANSACTIONS WATCH LISTS *TRAP = Temporal Risk Analysis Probability - (Risk Indicators)
  • 16. Stream Designer flow for Transfers & Payments 95
  • 17. Stream Designer flow for Transfers & Payments 96
  • 18. Transfers & payments lineage 97
  • 22.
  • 23. Getting started with Confluent Cloud is easy! Have your teams access a free Confluent Cloud account! *Free credits worth $400 during the first 30 days for all new sign ups. All the resources you need to set your Data in Motion, in one place. Revisit today's materials and gain access to on-demand webinars and toolkits.
  • 25. Lucky Draw Raffle through your stamped passports!