artificial intelligence: enhancing due diligence ... · ddiq: automated due diligence reports...

Artificial Intelligence: Enhancing Due Diligence &

Prospect Research

David Payne – Client Success & Engagement Manager, Exiger

Wouter Servaas – Prospect Researcher, University of Sheffield

3 Confidential All rights reserved. Copyright © 2017. Not for redistribution

Agenda

Introduction to AI – understanding cognitive computing and natural language processing

How AI can aid prospect researchers in due diligence?

Live demonstration

AI in prospect research – an adopter’s experience

Q&A


Data Protection and Compliance

General Counsel Statement:

Exiger infrastructure and data is co-located in redundant SOC 2 Type II certified, Tier 3 data centers. The data center operators only provide power, cooling, physical security, and carrier network connections, Exiger owns and operates all hardware and software

We are reviewing all data transfer compliance and we believe we are in compliance with GDPR standards now but we definitely will be before implementation in May 2018.

Thus far Canada has been white listed by the EU data authorities as meeting adequate PI protection standards: http://ec.europa.eu/justice/data-protection/international-transfers/adequacy/index_en.htm

http://ec.europa.eu/justice/data-protection/international-transfers/adequacy/index_en.htm







DDIQ –

Automated Due

Diligence


6

DDIQ Use Cases

Major donor research

Donor prospecting

Corporate relationships

Honorary degree nominations


New Paradigm in Due Diligence

2000s 2010s Today

Watchlist/

Development

Data

Aggregation

Automated

Due Diligence


Why Context Matters

Google: Consumer focus, too many false hits, often wrong context, shallow

Cognitive Adjudication Platform: Contextual analysis minimizes noise by navigating through the results as an investigator

Sample CAP-Generated Report Too many results

Wrong Subject

Wrong Subject

again

Right Subject

but not a risk

Wrong Subject

again

Search: Clean


How does DDIQ work?

Investigative &

Enhanced

Due Diligence

Real-World

Examples

Cognitive Computing

Platform

Report


Entity-Based Cognitive Computing Technology

Structured & Unstructured Data Acquisition Constantly growing list of sources

Aggregation of open web and premium sources

Proprietary Auto-Heal data connections

Comprehensive & Accurate Research Structured extracted information

95% of false positive removal

Easy access to source material

Profiles generated in < 5 minutes

DDIQ Cognitive Engine Natural Language Processing learns about the Entity

Assesses content and extracts information

Eliminates false-positives

De-duplicates content

Data Input Subject Information

W W W

Enriched seed data


DDIQ: Investigative Cognitive Computing

Natural language processing allows DDIQ to read, understand and piece together text. It discovers and follows leads found with the text similar to what a human researcher would do.

The Old Way The Way


DDIQ: Automated Due Diligence Reports

IPO

Screen

Rapid

Manual Diligence

Scope

Watchlist / Database

Screening Required for

global

compliance as

well as

operational &

reputational risk

reduction Red Flag Level I Level II

DDIQ

Risk

Profile

~ 10 Minutes 5 Business Days 7-10 Business Days 10-12 Business Days Bespoke

Profile Narrative Report


Cognitive Computing Research

DDIQ’s open web searches present information that may be removed regionally.


Found Hidden Search Results

Warning that the results have been removed due to EU law

Search results related to corruption have been removed

Not all results are available in English

Search results related to corruption are surfaced

Non-English results have been translated

Identified risks are categorized by event type

Google


DDIQ Deployment Models

SaaS Cloud Per User Access

Batch Reporting Automatic Run of DDIQ Profiles

Account Refreshes/Remediations

Enterprise Integrated DDIQ Engine

Embedded in Client Workflow

Available Hosted, On-Premise,

And in Hybrid Deployments

Profile Monitoring Available for All Configurations


DDIQ

DDIQ Demo


DDIQ Client Success Story – University of Sheffield

Pain Points

Spelling of prospective donor names, especially when translated from languages with non-Latin alphabet

Use of aliases, married names, or maiden names

Prospective donors with generic names – how to weed out the false positives

Prospective donor organisations without websites

A world top-100 university, renowned for the excellence, impact and distinctiveness of its research-led learning and teaching, set out its own due diligence process to safeguard its reputational standing.


DDIQ Client Success Story – University of Sheffield

DDIQ Solutions

DDIQ takes into account various spelling options

DDIQ includes aliases, maiden names and married names in the search results

Being able to fill in DOB and alma mater narrowed down the number of search results

DDIQ provides overviews of board members / trustees, enabling a more in-depth due diligence check

A world top-100 university, renowned for the excellence, impact and distinctiveness of its research-led learning and teaching, set out its own due diligence process to safeguard its reputational standing.


Case Study: University of Sheffield

AI helps the University of Sheffield to streamline its due diligence process and increase the turnover rate of due diligence checks

The University was offered a donation by an organisation. The organisation’s name came up in online searches, but little was revealed about its internal structure. A due diligence check was needed to ensure there would be no reputational risk for the University in accepting this donation.

Artificial intelligence helps human researchers in creating the full picture. At the University of Sheffield, DDIQ has proven to be an excellent addition to existing due diligence practices, enabling the University to make fully-informed decisions on whether proposed donations can be accepted or not.

Challenge:

Solution:


Case Study: University of Sheffield


Q&A

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