Presentation pack for Decision Point AI, Europe and USA. Focus, will the government approve a merger? and will the combined M&A firms deliver good value?

1. Decision Point
natural intelligence merged with artificial intelligence
Augmented Intelligence

2. Big Data + AI = risk of wrong interpretations
Analysis + Time = risk of missing market opportunities
Limited Data + AI = risk of wrong interpretations
Poor Quality Data + AI = risk of wrong interpretations
Human Bias + AI = risk of wrong interpretations
Poor Data = risk of making wrong decisions
Wrong Data = risk of making wrong decisions
Subjective Logic + Realtime Data Feeds
Opportunity Predictions
Predictions, Opportunity, Risks and Issues
Percentile Predictions
No Bias
Decision Point Consulting
Decision Point Consulting
Standard tools give standard results Build subject matter expertise into AI
2

3. 3

4. Provide transparency
Provide traceability
Operational passport
Algorithmic black box
Provide proof of rational for action
AI regulations not set yet
Point-in-time reporting all aspects built in
Assume they will be
4

5. First, we define the problem – the key question
we wish to answer:
What are your Questions?
Strategic, Tactical,
Operational?

7. First, we define the problem – the key question
we wish to answer:
Will the government
approve a merger?
Issues and Results

8. The knowledge tree was developed for an anti trust issue for a
legal corporate advisory team
The AI combines
Subjective Logic
through Conditional
Logic with Bayesian
Probability to deliver
‘Intelfuze’‘Intelifuze’ is highly effective in measuring
real-world scenario’s with astounding accuracy.
Subject Matter Experts and selected external experts
can provide input into the scenario to achieve the
most informed and accurate outcomes Considerations to be assessed
For the M&A knowledge tree
The process is intuitive to human thinking. Our
cognitive limitations however allow us to only
consider up to five variables at a given time.
‘Intelfuze’ can balance millions of variables
M&A Government Approval: Issues
8

9. Decision Point methodology means we can factor anti-trust
regulations into the scenario as well as past use cases from
the regulatory body.
‘Intelfuze’ can plug into a diverse range of sources,
platforms and API’s to ensure decision makers have a
dynamic and up-to-date real-time model.
Better oversight. Better advice.
An auditable, fully trackable process with our Operational
Passport feature.
A prescriptive actionable outcome for decision makers.
In this instance, there is a strong likelihood of the merger
being blocked by the regulatory body
A knowledge tree is
relatively easy to build,
highly repeatable and swiftly
adaptable for other anti-
trust scenario’s.Tested, active and fully operational within the intelligence
services. Counter terrorism strategy and intelligence
gathering
M&A Government Approval: Results
9

10. First, we define the problem – the key question
we wish to answer:
Will the combined firms
deliver good value?
All the steps

11. Listing the issues that might provide insight
to their combined performance, such as the
Strategic Logic, Organizational Behaviour,
and Financial Position.
Pre M&A Outcomes: Issues
Possible outcomes
11

12. Pre M&A Outcomes: Models (Strategic Logic)
Strategic Logic sub-issues, drivers
and indicators
12

13. Pre M&A Outcomes: Relational Models
Structuring models that hold the
relationships between the issues
In this M&A example, we use several layers of reasoning
from base information that is measurable in the real
world, to aggregated ones, such as Production Operation
Similarity.
13

14. Pre M&A Outcomes: Conditions
In the example above, in the real world, we would
anticipate it is very likely that IF combined firms have
good strategic logic (Combining firms has good strategic
logic) THEN the combined firms would deliver good
value (Combined firms’ performance delivers good
value).
The relationship between the issues is
expressed in terms of conditional probability
14

15. Pre M&A Outcomes: Objects
Almost all objects in the system can be annotated with
external links to web pages, PDF, images, spreadsheets,
etc.
15

16. Pre M&A Outcomes: Data or API
Information/data/opinions are represented by two
sources of information in this example.
• Sources can be databases, spreadsheets, people.
• Automated data ingestion links via a Web
Service/Restful API.
• A Virtual Analyst performs and stores the
assessments.
The Mergers and Acquisitions
model is inherently repeatable
and reusable for other M&A
transactions.
16

17. Pre M&A Outcomes: Questionnaire Validations
The data and opinions for this transaction were derived
from the Questionnaire sent to the corporate advisors.
This questionnaire and model are hosted in AWS.
17

18. Pre M&A Outcomes: Questionnaire Content
Above shows the observations made with respect to all
of the issues contained in the questionnaire. Where
there is colour there is information. More colour = more
certainty. No colour = no data. 18

19. Pre M&A Outcomes: Result
The assessment produced a single result – showing the
likelihood of the combined firms producing (or not)
‘good’ value.
The amount of colour radiating from the centre of the
circle indicates the certainty
(confidence) of the assessment.
Each of the opinions can be expanded to show the
reasoning and data behind each step in the calculation,
down to the information source.
19

20. What are your
Questions?

21. Website: www.decision-point.info
UK Email: uk@decision-point.info
EU Email: eu@decision-point.info
USA Email: usa@decision-point.info
London: +44 20 8133 7315
New York City: + 1 646 593 7849
Seattle: +1 206 905 1128
USA +1 800 650 2487
© May 2019 Decision Point a Veriluma Limited business unit Version 1.0