The document discusses the importance of semantic knowledge graphs in addressing today's data management challenges in financial services. It highlights the benefits of using a data fabric for self-service access, advanced analytics, and automation of data-driven operations, asserting that starting with small, impactful use cases can lead to significant improvements. Key takeaways emphasize the necessity for a modern approach to data and the gradual implementation of these solutions.

1. Discovering Insights in your Data
Industry Case Studies
Marty Loughlin, Vice President, Financial Services Sales

2. ©2018 Cambridge Semantics Inc. All rights reserved.
Financial Services Data Fabric
Trading
CRM
Loan
…
AML
Fraud Detection
Insider Threats
Recommendation
KYC/Customer 360
Regulatory ReportingKnowledge Graph
(Canonical Model/FIBO)
Line of Business
(Curated Data Catalog)
Enterprise
(Data Lake, Source Catalog)
3rd party
data
services
Finding Alpha
Logs
Digital Assistants
‘Graphy’Usecases

3. ©2018 Cambridge Semantics Inc. All rights reserved.
The Data Fabric Address Today’s Information Management Challenges
Answer unanticipated questions on
complex data across many sources
Self-service data access and
advanced analytics
? !
1
Achieve strategic data objectives
faster vs. stitching many tools
together
Semantic layer on Cloud
data lakes and Hadoop2
Accelerate automation of data-
driven operations with machine
learning and AI
Accelerate data science with
automated feature engineering
and selection
Data management and
prep for NLP, AI, and ML
NLP AI
ML
3

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Alternative Data
American Airlines: Stock Price vs # of Delayed Flights Honolulu Airport Information
Jetblue 360 View

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Risk Analytics

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Trade Surveillance

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Trade Surveillance

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Fraud Analytics
As-Is Data Graph
(Transliterated to Graph)
Auto-ETL
Dashboard
Profiling
Graph Mart
Schema & Data
Conceptual Model
ETLMap
ELT
ELT
Most Active Real Estate Communities

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Investment Research

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Feature Engineering & Selection
• Web-based interface uses the
Knowledge Graph with linked
data sets to easily create &
select features for machine
learning frameworks
• Anzo’s Excel-like formula
language makes it easy to
denormalize, aggregate, query,
& bucket data from
multidimensional models
• Set up rules to clean & transform
data
• Transformations are kept
discrete and granular 10

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Data Harmonization & Migration
Data Feed On-Boarding
Before
• Multiple SMEs
• 6-12 months to add new feed
• $500K - $1M
After
• Single SME
• 1-3 months to add new feed
• $50K - $100K
LoanIQ
Hive

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Key Takeaways
• Today’s data challenges need a new approach
• Semantic knowledge graphs are a powerful option
• Enterprise class platforms are available today
• Transformation takes time, start now
• Start small, select impactful use cases