# Presentation: 826051

## DB2 Entity Analytic Solutions

**Answering the Question “Who is Who?”  “Who Knows Who?” and  “Who Knows Who Anonymously?”**

**Darren Baldwin****World Wide Manager Entity Analytics**

**How do we shorten the distance between detect and preempt?**

## Financial Services Companies have some BIG issues!

- ID theft loss = $221 billion
- Growing at 300%
- ($2 Trillion by EOY 05)
*Aberdeen Group*

- Inability to uniquely identify clients is the leading cause for failure of CRM applications.  Oddly enough, almost the same information and analysis is needed for AML and CRM – Name, Address, structure and names of family members and business relationships...
- Gartner Group,
- BCS Banking Analytics Paper

- Money Laundering = $600 billion to $1.8 trillion of the world's annual economic activity
*Gannett News Service *

- Criminal’s are using
- “Front” Business and
- “Ghost” Employees to appear normal
- - IBM BCS

- 42% of all ID theft is credit card fraud
*MasterCard International*** **

- 25% of Americans say it’s okay to defraud insurers Accenture

- Collusion between employeesand 3rd parties accounts for 48% percent of all fraudulent activities.
*American Banker*

**Financial Services Companies have some BIG issues!**

**?**

## What Does the Entity Analytic Solutions Family Do?

**1. Disambiguate and aggregate Identities**

**2.** **Relate Disambiguated Identities,  reveal obvious and non-obvious relationships,  generate real-time alerts.**

**3. “Anonymyze” all identities to protect privacy.**

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## EAS’s Powerful Identity Recognition Platform

## Some of our Clients

-  Ultimate Who’s Who and
- Who Knows Who Systems
- Subject based Analysis – Many Degrees

- Know Your Customer Compliance
- And Customer Disambiguation
- 100’s of systems and 100 Million Records

-  Employee, Vendor and Customer
- Relationship & Collusion Detection

**Notes:**
Andrew Friedrich

## When You Don’t Know “Who is Who?”, You Don’t Have a Complete Picture of an Individual! 

- Identity Resolution detects and identifies a single person from multiple sources even if the data is insufficient, incorrect or fraudulent

-  Profile 4

-  Profile 3

- Profile 2

-  Profile 1

## Enterprise Entity ID Resolution

**Master Consolidated **
**Unique Entity ID/Unique person Identifier**

**Consolidates Multiple Systems Entity/ID’s**
**Generates Unique Entity ID**
**Full Data Attribution Retention & Resolution **
**Enterprise Entity Analytics**
_**Continuous, real-time, validation and correction**_** of ingested data**

**Data**
**Silo 1**

- PII = Personally Identifiable Information

**Data**
**Silo 2**

**Data**
**Silo 3**

## Identity Resolution – Example Data Points

**Identity Resolution – ****Example Data Points**

## The EAS Identity Repository – Identity Folder

| Names | Marc R Smith | A-#70001 |
| --- | --- | --- |
|  | Randal Smith | B-#009102 |
|  | Mark Randy Smith | C-#6251 |
| Address | 123 Main St. | A-#70001 |
|  | 456 First Street | C-#6251 |
| Phones | (713) 730-5769 | A-#70001 |
|  | (713) 731-5577 | B-#009102 |
|  | (713) 731-5577  | C-#6251 |
| SSN | 537-27-6402 | A-#70001 |
| DL | 0001133107 | A-#70001 |
|  | 1133107 | C-#6251 |
| DOB | 06/17/1974 | B-#009103 |

**Entity #144465**

- Mark R Smith

- Randall Smith

- Mark Randy Smith

- Mark Smith

## Introducing, Relationship Resolution

- Identity Deltas and Contagious Data Association

-   Degrees of separation for Investigation

-                  Identify Relationships between Entities

## Step 6: Knowing “Who Knows Who?” Extends Identities Beyond 360°

**Relationship Resolution**

**NAMES**
- Jack Smith
- Jack Black
- Jack B Nimber
- Jack B Quick
- Jack Rabbitt

**ADDRESSES**
- P.O. Box 1227
- Denver, CO 80112
- 415 Lancaster St Worcester, MA 01609

**ADDITIONAL**DOB:	07/03/69
- Phone:(303)778-1210	(508)278-6019
- Work:	U.S. Air Force
- Wife: 	Kendra Clark

- Step 6: Knowing “Who Knows Who?” Extends Identities Beyond 360°

- Identifies links and identity relationships
- Enables powerful case management & analytics
- Built on Entity Analytics platform

## Relationship Resolution and File Folders

- Kate Jones
- Entity #144465

- Account:  200898
- Phone1: (978) 365-5312
- Addr1: 123 Main St.
- Addr2: 456 First Street
- Grantor: Joe Albert

**Fraud File, OFAC, Bad List &**
**Unauthorized Use Data**

## Slide 13

## Relationship Resolution Pipeline Process

**Relationship Resolution**

**  ****Association Lists & **
**Enterprise Interactions**

**Relationships**

## IBM Entity Analytics:  Banking Example

**IBM Entity Analytics:  Banking Example**

**WHO KNOWS WHO?**

**WHO IS WHO?**

**?**

**?**

**?**

**?**

**Enterprise**
**Multi-Channel**
**Trust **
**Verification**

**Employee**
**Collusion**

**Branch Phone Logs (07/09/04)**
- 978-365-6631
- 614-876-8456
- 376-557-5050
- 949-657-8128
- 513-876-2853
- 217-314-2127

**Fraud Networks**

**Notes:**
We dump all of this information in for analysis.  And so, here is how the technology works:
We have a buyer who purchases electronic equipment
In his employee file he has an emergency contact listed with a corresponding phone number (another state).
That phone number corresponds to a contact number of a manager at a Target store.
That phone corresponds to the contact phone from an online transaction 4 months ago from a Kathy Green
Which is similar to Kate Green – an electronics vendor who sells to our buyer.
Here we have determined who is related to who and who is who...
Are these actually related?

I think of a scenario like this as even being outside of the statistics we have discussed thus far.  If we do not have any knowledge of this potential connection, we continue in the due course of business in purchasing electronics from XYZ without being able to quantify the potential cost of doing business with XYZ.  Maybe other vendors have better prices but due to an unknown and unwanted relationship, the buyer is motivated to work with XYZ.  This is not something we generally go looking for like cash refunds, under-rings, false voids or false payouts.

And, what if we further realize that C Jones who is Charlie Jones is actually Charles Janes, a known money launderer who is listed on an OFAC banned list?

(transition) This is why it is becoming more and more important to who you are doing business with...

## $$$

- Have Dinner at
- Great Restaurant

- Transactions

- $$$$

- However, No one knew that the
- Waiter used a “Skimmer” to steal
- Their credit card information

- He spent a lot with their
- Credit Cards......

**FDIC **
**Member **
**Bank**

## WHERE DID THESE CHARGES 

- $$$

- $$$

- $$$

- $$$

- $$$

- Have Dinner at
- Great Restaurant

- Transactions

- $$$$

- However, No one knew that the
- Waiter used a “Skimmer” to steal
- Their credit card information

- He spent a lot with their
- Credit Cards......

- WHERE DID THESE CHARGES
- COME FROM?!?!?!?!

- Excellent question
- lets find out!!!

**FDIC **
**Member **
**Bank**

## $$$

- Excellent question
- lets find out!!!

- Fraudulent Merchant
- Co-location

- Joint Investigation

- Same Waiter

**FDIC **
**Member **
**Bank**

## Introducing, Anonymous Resolution

- Original/Clear-Text data remains with owner

- Protect individuals PII*

- Share data safely from enterprise-to-enterprise

- *PII - Personally Identifiable Information

## Anonymous Resolution Determines “Who is Who & Who Knows Who... Anonymously”

- DB2 Anonymous Resolution allows multiple parties to safely and securely share & compare “anonymized” information assets.

## The One-Way Hash

**Result:** An irreversible digital code
- (Not encryption)
**Use:** Fundamental building block used in modern cryptography

_**INPUT VALUE**_

_**OUTPUT VALUE**_

- One-Way Hash
- Function
- (e.g., MD-5 or
- SHA-1)

- Mark Smith

## The One-Way Hash – Infinitely Sensitive

_**INPUT VALUE**_

- One-Way Hash Function

- One-Way Hash Function

_**OUTPUT VALUE**_

- Mark Smith

- Mark R Smith

**Notes:**
Hash values are very sensitive. Just a letter affects the hash value. So how do we compensate? Next slide.

## No Identity Data is in the Clear, Nothing is discarded

| A-100031 | First Name | Original | cbd034409c22929518fa494f99dc9964 |
| --- | --- | --- | --- |
| A-100031 | First Name | Standardized | 9269bb3bc60366245144cbd5e960cfd8 |
| A-100031 | Last Name | Original | b835b521c29f399c78124c4b59341691 |
| A-100031 | Date of Birth | Original | 799709b2e5f26f796078fd815bebf724 |
| A-100031 | Date of Birth | Swapped mm/dd | 40ddba83c22acc2acaddff12c66d7adf |
| A-100031 | Date of Birth | Circa DOB | e4310b75f2fa9595f8154411924b19b1 |

**What’s in this **
**Database?**

**Notes:**
We store original first name hashed as well as standardized first name hashed.

## Data Source Anonymization

**Output**

**Anonymizer**

**Bank A**
**Suspects**

**Bank B **
**Suspects**

**Output**

**Output**

**Bank C**
**Suspects**

**Anonymizer**

**Notes:**
Cruise Line cannot legally share PII. If they annonymize they can. If they get an alert they have probably cause. Has is not encryption, pig/sausage.

## Anonymized Output processed by the Resolver

**Output**

**Output**

**Output**

**Bank A, B and C**
**are all suspicious **
**of the same guy!**

**Notes:**
Now we anonymize and ingest our Banned Parties list and alerts come out.

## DB2 Entity Analytic SolutionsSolution Portfolio Overview

***Thoughts?***

**Darren Baldwin****Manager, WW Sales Entity Analytics**