Best Practices forManaging Customer Information

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Location

Relationships

SentimentInfluence

Buying Propensity

Golden Profiles Require Real-Time Knowledge of the Customer’s Context

Products

3

Evolution of the Customer Information Management Practice

Structure Knowledge Graphs

Insights Contextualized

Profiles

DeliveryExtreme

Processing

4

Knowledge Graphs

Next Generation Approach

5

• Rigid data models tied to RDBMS

• Limited views

• Adaptive data modeling enabled via Graph Data Structures

• Multi-dimensional views enabled via complex relationships & hierarchy management

Knowledge Graphs: Intuitive & Agile

6

Domain Silos of Traditional Approaches

Location/Site Hub Product Hub

Customer Hub

7

What Traditional Approaches Don’t ‘See’

8

Extended Network of a Customer

9

Discover Non-Obvious Relationships

10

Determine Sphere of Influence

11

Multi-dimensional views:Financial Services – Case In Point

Payment Graph (e.g. Fraud Detection, Credit Risk, Analysis, Chargebacks…)

Spend Graph (e.g. Org Drill thru, Product Reco’s, Mobile Payments)

Asset Graph (e.g. Portfolio Analytics, Risk Management, Market & Sentiment)

Master Data Graph (e.g. Enterprise Collaboration, Corporate Hierarchy, Data Governance)

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Poor Data Management Blinded Chase to Madoff Fraud: WSJ by Penny Crosman

JAN 8, 2014

Data locked in silos and the lack of a common customer identifier that could link accounts were to blame for JPMorgan Chase's failure to identify Bernard Madoff's massive fraud, according to an article in Wednesday's Wall Street Journal.

(Madoff, who was arrested in 2008, stole about $18 billion from clients, sending them fake monthly statements reflecting fake trades, assuring customers they were getting high returns when in fact their money was gone.)

Madoff Investment Securities maintained several linked checking and brokerage accounts at JPMorgan Chase, its primary bank, for 22 years. The bank structured and sold investment vehicles tied to the firm's purported returns. The bank has agreed to pay $2.7 billion in fines to the federal government for failing to report warning signs of Madoff's scheme.

"Despite recognizing suspicious activity in its U.K. unit in 2008 — and notifying U.K. regulators that Mr. Madoff's returns were 'too good to be true' — the bank didn't notify its own U.S.-based AML staff or American authorities. AML experts say that JPMorgan's anti-fraud systems should have automatically flagged Madoff accounts across the company," the paper reports. In one of the terms of the bank's settlement, JPMorgan has agreed to continue reforms of its Bank Secrecy Act/Anti-Money Laundering compliance program.

Customer data that's strewn across a company and not linked has been a problem that has plagued large banks for many years. A London division of a bank could have no idea of the activity of a customer in New York, for example, creating fraud as well as customer service issues. Shortly before the financial crisis, several large banks appointed C-level data management chiefs (called chief data officers) and had them start creating unified customer data warehouses in which all accounts, transactions and other activity related to a customer could be gathered in one place. Bank of the West recently completed such a project.

During the financial crisis, these large, multi-year projects with an elusive ROI were put aside. Recently, with the dust settling, a few banks have been turning their attention again to customer data management.

But software can only do so much. The other side to this is that in Manhattan U.S. Attorney Preet Bharara's criminal charges against JPMorgan Chase, a pattern of willful ignorance is described. Time and time again, according to the U.S. Attorney's office, the bank had strong reason to

Poor Data Management Blinded Chase to Madoff Fraud: WSJ by Penny Crosman

JAN 8, 2014

Data locked in silos and the lack of a common customer identifier that could link accounts were to blame for JPMorgan Chase's failure to identify Bernard Madoff's massive fraud, according to an article in Wednesday's Wall Street Journal.

(Madoff, who was arrested in 2008, stole about $18 billion from clients, sending them fake monthly statements reflecting fake trades, assuring customers they were getting high returns when in fact their money was gone.)

Madoff Investment Securities maintained several linked checking and brokerage accounts at JPMorgan Chase, its primary bank, for 22 years. The bank structured and sold investment vehicles tied to the firm's purported returns. The bank has agreed to pay $2.7 billion in fines to the federal government for failing to report warning signs of Madoff's scheme.

"Despite recognizing suspicious activity in its U.K. unit in 2008 — and notifying U.K. regulators that Mr. Madoff's returns were 'too good to be true' — the bank didn't notify its own U.S.-based AML staff or American authorities. AML experts say that JPMorgan's anti-fraud systems should have automatically flagged Madoff accounts across the company," the paper reports. In one of the terms of the bank's settlement, JPMorgan has agreed to continue reforms of its Bank Secrecy Act/Anti-Money Laundering compliance program.

Customer data that's strewn across a company and not linked has been a problem that has plagued large banks for many years. A London division of a bank could have no idea of the activity of a customer in New York, for example, creating fraud as well as customer service issues. Shortly before the financial crisis, several large banks appointed C-level data management chiefs (called chief data officers) and had them start creating unified customer data warehouses in which all accounts, transactions and other activity related to a customer could be gathered in one place. Bank of the West recently completed such a project.

During the financial crisis, these large, multi-year projects with an elusive ROI were put aside. Recently, with the dust settling, a few banks have been turning their attention again to customer data management.

But software can only do so much. The other side to this is that in Manhattan U.S. Attorney Preet Bharara's criminal charges against JPMorgan Chase, a pattern of willful ignorance is described. Time and time again, according to the U.S. Attorney's office, the bank had strong reason to

The Case for Data Governance

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• Limited to non-existent support for roles, responsibilities, and processes between the business and IT

• KPIs tied to process• Monitor trends over-time• Enable business stewardship• Embedded workflows/exception

mgmt.• PII data anonymized

Data Governance: In Service of the Business Process

14

Entity Analytics

Spatial Analytics

Data Visualization:Encourage Data Discovery and Exploration

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Data Visualization:Encourage Data Discovery and Exploration

Timeline ViewsPattern Views

Heat Map Views Bar Chart Views

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Combination of Traditional, Social Network & Spatial Analytics: Robust Context to the Knowledge Graph

• Who is a high spender?

• What is their propensity to buy?

• Is the customer within my pre-defined Geo-fence?

• How does it influence my marketing offers?

• Who is both influential in their community & a high spender?

• Which products would customers prefer that others “like” them have purchased?

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Customer Information Management Best Practices

Use Knowledge Graphs as the intuitive & agile way to organize complex customer data

Establish process-centric data governance

Look beyond traditional analytics to build robust contextual profiles

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