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Confluent as the Data Streaming
Platform for Financial Services
Peter Pugh-Jones
Director Financial Services, CSG, Confluent
Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Software Is Changing Every Aspect of Life
Evolution ..…
Futurama, created by Matt Groening and developed by Groening and David X. Cohen. Produced by 20th
Television Animation a part of Disney Television Studios, with Rough Draft Studios, Inc.
The Foundational Assumption of Every Database:
Data at Rest
Data at rest
Slow, daily
batch processing
Simple, static
real-time queries
Databases
Context ……
Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. 7
“The key realization for the adoption of event-first
thinking is that an event represents a fact,
something happened; it is immutable and therefore
changes how we think about our domain model”
- Neil Avery
will enable us to revolutionize the value
obtained by the business lines that we serve”
100+ Companies Across the
Financial Services Industry Rely on Confluent
9
10 Use Cases for Real-time Streaming Data
in Financial Services*
*Download the WhitePaper!
Consumer
Banking
Payments
Fraud
Trading
Risk
Management
Insurance
Wealth
Management
FinTech
Finance
Crypto /
Distributed Ledger
Event Streaming Insights - Maturity Curve
Investigation of data
at rest to identify
patterns (post event)
1
2
3
4
6
Basic real-time rules
to score transactions
5
Detecting entity
related patterns using
highly contextual data
Adoption of
context-aware Risk
Indicators, and risk-weighted
assessments (In real-time)
Adoption of ML on data at
rest to significantly
improve success rate for
detection of anomalies
Adoption of ML in Data
Stream Processing to
dynamically improve the
detection of anomalies
Real Time
Data
Payments
Mobile Banking
Security & Fraud
Operations
Lending
A New Paradigm
Continuously Process Streaming Data
Stream Processing
Customer Experience
Clearing & Settlement
“We need to shift our thinking from everything
at rest to everything in motion.” —
12
Data Streaming Lifecycle
A New Paradigm to Set Your Data in Motion:
Confluent Data Streaming Platform
All your data continuously streamed, processed, governed and shared as a product,
making it instantly valuable, usable, and trustworthy everywhere.
PROCESS
GOVERN
CONNECT
STREAM
SHARE
Custom Apps &
Microservices
Data Systems
AI/ML Modeling
Inventory Payments
Personalization
Fraud Supply Chain
Recommendations
From Data Mess To Data Products
To Instant Value
Everywhere
Whole Bank
Regions
Sub Regions
Country
Office
Team
Stream, Connect, Transform, Prepare, Share
Compute, Enrich & Aggregate
Database
SaaS App
Custom Producers
Metadata
Migrating Schemas
Real-time Transactions
Watchlists
Connect Share
Govern Process Build
A New Paradigm to Set Your Data in Motion:
Confluent Data Streaming Platform
All your data continuously streamed, processed, governed and shared as a product,
making it instantly valuable, usable, and trustworthy everywhere.
PROCESS
GOVERN
CONNECT
STREAM
SHARE
Custom Apps &
Microservices
Data Systems
AI/ML Modeling
Inventory Payments
Personalization
Fraud Supply Chain
Recommendations
From Data Mess To Data Products
To Instant Value
Everywhere
16
Data Streaming Lifecycle
Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Hyper-personalised Customer Experiences
17
CRM
General Ledger
Website
Mobile Bank
Branch / ATM
Credit Ratings / Risk
Security
Contact Center
Open Banking
Inbound Data Customer State Vector
Customer Contact Details
Credit History
Recent Transactions
Products of Interest
Customer Locations
Demographic
Geographic
Psychographic
Behavioral
Historical
Advertising
Website Content
Offers
Customer Service
Fraud Prevention
Cyber Ops
Lending
Marketing
Business Consumers
Data is streamed
asynchronously into the state
vector, so it is continuously
updated every time a new data
point arrives.
Data is always 100% up to date
and synchronised across all
locations.
Business units consume data
that is recent, accurate, and
relevant.
Mobile banking, Branch banking, Phone banking,
Open banking: Many places, one experience.
18
Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Combining Security and Fraud operations
19
Kafka Topics
Logs, Metrics,
Traces (in context)
Immutable log
Audit Trail
Syslog/OS Logs
DNS + Network Data
Application Logs
Internet/Mobile Banking
Transactions and Website Data
All Payments
Open Banking API Requests/
Transactions/Usage Data
General Ledger/Transactions
Money Laundering/
Sanctions Watchlists
Email/Messaging Data
Fraud Watchlists
Authentication Data
HTTP Logs
Technical/Performance
Data Sources
Application Traces
Performance Metrics
Microservices and Cloud Metrics
Security Data Sources
Stream
Processing
Filtered,
Optimised Data
Stream
Processing
Raw Data
Confluent Security Operations
Fraud and Financial Crime Operations
IT Operations
Splunk / Elastic / Other SIEM
Tools
Fraud Detection Tools
Operational Resilience Tools
Datadog / New Relic /
Observability Tools
S3 / Cheap Storage
Improving Customer Satisfaction
through Real-Time Engagement
Challenge: Assess suspicious account with accuracy and
at the moment when it matters most with real-time
fraud detection.
Solution: Deploy Confluent to enable an event-driven
architecture that underpins real-time fraud detection
service that combines real-time account data with
historical context.
Results:
Save $150/customer and greatly increased customer
satisfaction by identifying activity in question and
triggering real-time, actionable alerts with Eno.
“We look at events as running our business. Business
people within our organization want to be able to react
to events—and oftentimes it's a combination of events.”
— VP of Streaming Data Engineering
Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. 21
Largest bank in the United States,
the world's largest bank by
market capitalization, and the
fifth largest bank in the world in
terms of total assets, supporting
more than 31 Billion payments a
year, with a value of over $1
Trillion, on Confluent Platform
and on a journey to Confluent
Cloud.
Largest Bank In America
Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Existing Payment Rails
A platform for payments
22
Business applications leveraging Confluent data platform
Confluent Real-Time Payments Platform: A Consolidated view of all payments
Open Banking
APIs
PCI
Compliance
Compliance/
Internal Audit
Customer
Vectors
APT Based
Payments
Gateways
ISO 20022
Adoption /
Integration
Fraud
Detection/AML
Security
Operations
Adaptors/
APIs
Core Banking System /
Mainframe
Ops/SLA
Monitoring
Adaptors/
APIs
Existing Payment Rails
Visa/Mastercard
Amex/Discover
Swift
BACS
CHAPS
ACH
Faster Payments
SEPA
Inbound
Payments
Data Store
Outbound
Payments
Data Store
Visa/Mastercard
Amex/Discover
Swift
BACS
CHAPS
ACH
Faster Payments
SEPA
Challenge: Modernizing legacy systems for traditional
banks to enable them to innovate faster, deliver
hyper-personalized customer experiences, and compete
with digital- native banks.
Solution: Deliver a cloud-native SaaS solution— powered
by Confluent Cloud’s real-time data streaming platform.
Results:
● Reduced costs with increased agility and faster time to
market for traditional banks
● Achieved better hyper-personalized experiences for
banking customers
● Delivered a resilient and highly available platform
● Enhanced enterprise-grade security
● Reduced TCO with simplified management
“Our mission is to make banking 10x better for banks, for
customers, and society. To do that, we need a
cloud-native data streaming platform that is also 10x
more reliable, 10x more performant than Apache Kafka.”
– Mark Holt, Chief Product and Engineering Officer at 10x Banking
Link to video also here
Challenge: Develop a new trading platform for markets across
multiple European countries that supports high-volume,
high-speed trading and provides clients with access to
real-time data.
Solution: Use Confluent Platform to implement a reliable,
scalable persistence layer for market orders that supports
millisecond latencies and billions of messages per day.
Results:
● Reliable 24/5 operations achieved and maintained
● Stringent performance requirements exceeded
● Dedicated, expert support received
“We have been very satisfied with Confluent Platform as the
backbone of our persistence engine. The platform has been
super reliable. We have stringent requirements for real-time
performance and reliability, and we have confirmed – from
proof-of-concept to deployment of a cutting-edge production
trading platform – that we made the right decision.”
— Alain Courbebaisse, Chief Information Officer, Euronext
Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Large Financial Assets Manager
Modernized their platform to the
Confluent data streaming
platform from their legacy
solution for streaming called
Streambase.
“In the past our clickstream
analysis would take anything
from 48 to 72 hours. With the
implementation of Confluent we
got that down to 3 seconds.”
”We now believe with our
adoption of Confluent, Event
streaming is truly mainstream”.
Building ML/AI Pipelines
for immediate action and insights
Deliver disparate data
sets in real time at scale
Feature
Data Input
Model Building /
Scoring
Production ML App
Training
Data
Model
Params
Params,
Features
Output
Train and bring models
to production faster
Drive critical insights in real time for
immediate action & decision making
App
DB
App
DB
Applications
App
App
App
App
Unlocking Mainframe Data
● Publish to Kafka to improve data reliability, accessibility, and out to cloud services
● No changes to the existing mainframe applications
● Greatly reduce MQ related MIPS to move data off the Mainframe
27
zLinux
CICS
VSAM
Legacy
Apps
IMS
zIIP
MQ
Connector
On-Prem
Cluster
Confluent
Cloud
Augment SIEM with Confluent to Help Reduce Data Indexing, Analysis and Storage Costs
Application Logs
Network Logs
Database logs
OS Logs
Collect all data sources into
Confluent
Filter events streams and only send
priority events to SIEM
Shorten SIEM retention window; store
in cheaper storage for long-term
retention
Offload fast query
and search
Send high
priority data
to SIEM
Send all data to
S3/HDFS for
cold storage
Open up data access
to new use cases
Preserve data in low-cost storage
Tier and route data to cloud storage
infrastructure for long term retention
with pre-built connectors
Save on ingest and indexing costs
Moderate ingest and index volume by
aggregating, and filtering events to
reduce license spend
Lower overall operating expenses
Reduce the need for proprietary
forwarders like Splunk Heavy
Forwarder with Splunk S2S connector
29
Click above image to watch the webinar recording featuring
Ryan James, CDO, Vitality
Resources
ppughjones@confluent.io
https://uk.linkedin.com/in/peterpughjonesjnr
@rubricsinger
http://confluent.cloud
http://confluent.io
Thank you !

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Confluent & GSI Webinars series: Session 2

  • 1. Confluent as the Data Streaming Platform for Financial Services Peter Pugh-Jones Director Financial Services, CSG, Confluent
  • 2. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. Software Is Changing Every Aspect of Life
  • 3. Evolution ..… Futurama, created by Matt Groening and developed by Groening and David X. Cohen. Produced by 20th Television Animation a part of Disney Television Studios, with Rough Draft Studios, Inc.
  • 4. The Foundational Assumption of Every Database: Data at Rest Data at rest Slow, daily batch processing Simple, static real-time queries Databases
  • 6. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
  • 7. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. 7 “The key realization for the adoption of event-first thinking is that an event represents a fact, something happened; it is immutable and therefore changes how we think about our domain model” - Neil Avery will enable us to revolutionize the value obtained by the business lines that we serve”
  • 8. 100+ Companies Across the Financial Services Industry Rely on Confluent
  • 9. 9 10 Use Cases for Real-time Streaming Data in Financial Services* *Download the WhitePaper! Consumer Banking Payments Fraud Trading Risk Management Insurance Wealth Management FinTech Finance Crypto / Distributed Ledger
  • 10. Event Streaming Insights - Maturity Curve Investigation of data at rest to identify patterns (post event) 1 2 3 4 6 Basic real-time rules to score transactions 5 Detecting entity related patterns using highly contextual data Adoption of context-aware Risk Indicators, and risk-weighted assessments (In real-time) Adoption of ML on data at rest to significantly improve success rate for detection of anomalies Adoption of ML in Data Stream Processing to dynamically improve the detection of anomalies
  • 11. Real Time Data Payments Mobile Banking Security & Fraud Operations Lending A New Paradigm Continuously Process Streaming Data Stream Processing Customer Experience Clearing & Settlement “We need to shift our thinking from everything at rest to everything in motion.” —
  • 13. A New Paradigm to Set Your Data in Motion: Confluent Data Streaming Platform All your data continuously streamed, processed, governed and shared as a product, making it instantly valuable, usable, and trustworthy everywhere. PROCESS GOVERN CONNECT STREAM SHARE Custom Apps & Microservices Data Systems AI/ML Modeling Inventory Payments Personalization Fraud Supply Chain Recommendations From Data Mess To Data Products To Instant Value Everywhere
  • 14. Whole Bank Regions Sub Regions Country Office Team Stream, Connect, Transform, Prepare, Share Compute, Enrich & Aggregate Database SaaS App Custom Producers Metadata Migrating Schemas Real-time Transactions Watchlists Connect Share Govern Process Build
  • 15. A New Paradigm to Set Your Data in Motion: Confluent Data Streaming Platform All your data continuously streamed, processed, governed and shared as a product, making it instantly valuable, usable, and trustworthy everywhere. PROCESS GOVERN CONNECT STREAM SHARE Custom Apps & Microservices Data Systems AI/ML Modeling Inventory Payments Personalization Fraud Supply Chain Recommendations From Data Mess To Data Products To Instant Value Everywhere
  • 17. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. Hyper-personalised Customer Experiences 17 CRM General Ledger Website Mobile Bank Branch / ATM Credit Ratings / Risk Security Contact Center Open Banking Inbound Data Customer State Vector Customer Contact Details Credit History Recent Transactions Products of Interest Customer Locations Demographic Geographic Psychographic Behavioral Historical Advertising Website Content Offers Customer Service Fraud Prevention Cyber Ops Lending Marketing Business Consumers Data is streamed asynchronously into the state vector, so it is continuously updated every time a new data point arrives. Data is always 100% up to date and synchronised across all locations. Business units consume data that is recent, accurate, and relevant.
  • 18. Mobile banking, Branch banking, Phone banking, Open banking: Many places, one experience. 18
  • 19. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. Combining Security and Fraud operations 19 Kafka Topics Logs, Metrics, Traces (in context) Immutable log Audit Trail Syslog/OS Logs DNS + Network Data Application Logs Internet/Mobile Banking Transactions and Website Data All Payments Open Banking API Requests/ Transactions/Usage Data General Ledger/Transactions Money Laundering/ Sanctions Watchlists Email/Messaging Data Fraud Watchlists Authentication Data HTTP Logs Technical/Performance Data Sources Application Traces Performance Metrics Microservices and Cloud Metrics Security Data Sources Stream Processing Filtered, Optimised Data Stream Processing Raw Data Confluent Security Operations Fraud and Financial Crime Operations IT Operations Splunk / Elastic / Other SIEM Tools Fraud Detection Tools Operational Resilience Tools Datadog / New Relic / Observability Tools S3 / Cheap Storage
  • 20. Improving Customer Satisfaction through Real-Time Engagement Challenge: Assess suspicious account with accuracy and at the moment when it matters most with real-time fraud detection. Solution: Deploy Confluent to enable an event-driven architecture that underpins real-time fraud detection service that combines real-time account data with historical context. Results: Save $150/customer and greatly increased customer satisfaction by identifying activity in question and triggering real-time, actionable alerts with Eno. “We look at events as running our business. Business people within our organization want to be able to react to events—and oftentimes it's a combination of events.” — VP of Streaming Data Engineering
  • 21. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. 21 Largest bank in the United States, the world's largest bank by market capitalization, and the fifth largest bank in the world in terms of total assets, supporting more than 31 Billion payments a year, with a value of over $1 Trillion, on Confluent Platform and on a journey to Confluent Cloud. Largest Bank In America
  • 22. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. Existing Payment Rails A platform for payments 22 Business applications leveraging Confluent data platform Confluent Real-Time Payments Platform: A Consolidated view of all payments Open Banking APIs PCI Compliance Compliance/ Internal Audit Customer Vectors APT Based Payments Gateways ISO 20022 Adoption / Integration Fraud Detection/AML Security Operations Adaptors/ APIs Core Banking System / Mainframe Ops/SLA Monitoring Adaptors/ APIs Existing Payment Rails Visa/Mastercard Amex/Discover Swift BACS CHAPS ACH Faster Payments SEPA Inbound Payments Data Store Outbound Payments Data Store Visa/Mastercard Amex/Discover Swift BACS CHAPS ACH Faster Payments SEPA
  • 23. Challenge: Modernizing legacy systems for traditional banks to enable them to innovate faster, deliver hyper-personalized customer experiences, and compete with digital- native banks. Solution: Deliver a cloud-native SaaS solution— powered by Confluent Cloud’s real-time data streaming platform. Results: ● Reduced costs with increased agility and faster time to market for traditional banks ● Achieved better hyper-personalized experiences for banking customers ● Delivered a resilient and highly available platform ● Enhanced enterprise-grade security ● Reduced TCO with simplified management “Our mission is to make banking 10x better for banks, for customers, and society. To do that, we need a cloud-native data streaming platform that is also 10x more reliable, 10x more performant than Apache Kafka.” – Mark Holt, Chief Product and Engineering Officer at 10x Banking Link to video also here
  • 24. Challenge: Develop a new trading platform for markets across multiple European countries that supports high-volume, high-speed trading and provides clients with access to real-time data. Solution: Use Confluent Platform to implement a reliable, scalable persistence layer for market orders that supports millisecond latencies and billions of messages per day. Results: ● Reliable 24/5 operations achieved and maintained ● Stringent performance requirements exceeded ● Dedicated, expert support received “We have been very satisfied with Confluent Platform as the backbone of our persistence engine. The platform has been super reliable. We have stringent requirements for real-time performance and reliability, and we have confirmed – from proof-of-concept to deployment of a cutting-edge production trading platform – that we made the right decision.” — Alain Courbebaisse, Chief Information Officer, Euronext
  • 25. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. Large Financial Assets Manager Modernized their platform to the Confluent data streaming platform from their legacy solution for streaming called Streambase. “In the past our clickstream analysis would take anything from 48 to 72 hours. With the implementation of Confluent we got that down to 3 seconds.” ”We now believe with our adoption of Confluent, Event streaming is truly mainstream”.
  • 26. Building ML/AI Pipelines for immediate action and insights Deliver disparate data sets in real time at scale Feature Data Input Model Building / Scoring Production ML App Training Data Model Params Params, Features Output Train and bring models to production faster Drive critical insights in real time for immediate action & decision making App DB App DB Applications App App App App
  • 27. Unlocking Mainframe Data ● Publish to Kafka to improve data reliability, accessibility, and out to cloud services ● No changes to the existing mainframe applications ● Greatly reduce MQ related MIPS to move data off the Mainframe 27 zLinux CICS VSAM Legacy Apps IMS zIIP MQ Connector On-Prem Cluster Confluent Cloud
  • 28. Augment SIEM with Confluent to Help Reduce Data Indexing, Analysis and Storage Costs Application Logs Network Logs Database logs OS Logs Collect all data sources into Confluent Filter events streams and only send priority events to SIEM Shorten SIEM retention window; store in cheaper storage for long-term retention Offload fast query and search Send high priority data to SIEM Send all data to S3/HDFS for cold storage Open up data access to new use cases Preserve data in low-cost storage Tier and route data to cloud storage infrastructure for long term retention with pre-built connectors Save on ingest and indexing costs Moderate ingest and index volume by aggregating, and filtering events to reduce license spend Lower overall operating expenses Reduce the need for proprietary forwarders like Splunk Heavy Forwarder with Splunk S2S connector
  • 29. 29 Click above image to watch the webinar recording featuring Ryan James, CDO, Vitality