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law’s future from finance’s past
Fin(Legal)Tech
daniel martin katzdaniel martin katz
blog | ComputationalLegalStudies.com
c...
Three Types of Lawyers
(as described by paul lippe)
play “whack-a-mole”, reacting to
problems by creating fear and
friction within organizations and
the impression that there...
can help clients shape
(perhaps distort)
external perception of risk.
Merely Clever Lawyers
design systems that
balance risk and improve
transparency, helping clients
correctly price risk internally
Great Lawyers
Lawyer VALUE PROPOSITION
(From the Client’s Perspective)
(internal or external client)
-Or-
What is the
Value of Marginal Dollar
invested inside/outside
lawyers
(From the CEO / CFO Perspective)
help price risk /
help reduce information asymmetries
transactional =
litigation =
characterize (predict) risk/exposure
shift the expected value of a lawsuit
help price risk /
help reduce info...
litigation =
characterize (predict) risk/exposure
shift the expected value of a lawsuit
compliance = identify + prevent ro...
litigation =
characterize (predict) risk/exposure
shift the expected value of a lawsuit
compliance = identify + prevent ro...
Today we only want to talk
about one thing …
#Arbitrage
But if we are going
to talk about
#Arbitrage
Then we need to talk about
why we *sometimes* 

miss obvious opportunities
opportunities that have been
right under our noses all along
Must get exposed to new ideas
(most innovation in law started elsewhere)
need to develop a
less law centric view of the world
So in that spirit …
the analogy du jour
law = finance
(insuranceaswell)
law < > finance
many elements in law look like
finance did 25 - 50 years ago
(on the long road from Black-Scholes to algorit...
this is an extension of this prior talk
by mike bommarito
Dominant Model in Law
expert centered pricing of risk
Dominant Model in Law
lots of unintentional self insurance
rarely (if ever) based upon
explicit risk models
Cult of one 

(or very small # of)
person(s) thinking drives
decisions with serious
financial consequences
hard to move
to more rigorous
models given
borderline
pathological
numerophobia
among lawyers
Claim:
fin(tech)
offers
lessons
for many
areas
in law
thesis statement:
the financialization
of the law will be
an important vector
of the next
decade(s) in law
The Two Major Branches
in #FinTech
The Two Major Branches
in #FinTech
removing
socially
meaningless
frictions
(from financial processes)
The Two Major Branches
in #FinTech
removing
socially
meaningless
frictions
characterizing
(pricing)
increasingly 

exotic ...
#Fin(Legal)Tech
application of those ideas and
technologies to a wide range of
law related spheres including
litigation, t...
We recently organized 

this conversation
(Next one in October 2017)
FinLegalTechConference.comNovember 4, 2016
Fin(Legal)Tech Conference
finlegaltechconference.com
@Illinois Tech - Chicago Kent College of Law
Fin(Legal)Tech Conference
finlegaltechconference.com
@Illinois Tech - Chicago Kent College of Law
Fin(Legal)Tech Conference
finlegaltechconference.com
@Illinois Tech - Chicago Kent College of Law
Fin(Legal)Tech Conferenc...
#FinLegalTech
removing
socially
meaningless
frictions
(from legal processes)
across the economy there
are many effort to convert
an artisanal process into
an industrial process
the industrialization
of the artisan
lets focus on these two stages
there is often a significant
spread between
Kim Craig @ Seyfarth Lean Consulting
Chicago Legal Technology + Innovation MEET...
recently met with the general
counsel of a large publicly traded
company who has reduced the
legal expenditures of the com...
Lean, Six Sigma
and other
process improvement methodologies
can help improve almost
every subsector in law
the toyota
production system
lean ideas
lean for
enterprises
(white collar, etc.)
Remove Waste (friction)
Increase predictability
(profitability)
convert high volatility process
convert high volatility process
into a lower volatility process
Not
just
about
efficiency
its also about
excellence
Lean
Process
Mapping
KM
+
Lean
Examples:
http://
www.seyfarth.com/
dir_docs/
publications/
LITDecJan2014LeanS
ixSigma.pdf
http://
www.seyfarth.com/
dir_docs/
publications/
LITDecJan2014LeanS
ixSigma.pdf
The Course that I help
co-teach at
Chicago-Kent
College of Law
#FinLegalTech
the path of fin(tech)
has in part followed
developments
in artificial intelligence
There has been lots of recent
interest in applying
artificial intelligence to law
data driven AI rules based AI
Competing Orientations in
Artificial Intelligence
expert
systems
Computational Law
Data Driven Rules Based
prediction
models
and
methods
network
analytic
methods
natural
la...
Three Examples
of Rules Based
(Expert Systems)
A.I.
EXAMPLE 1
tax
preparation
software
EXAMPLE 2
A2J AUTHOR
www.a2jauthor.org
PROCESS
Guided
Interview
Completed
Document
LOGIC
DECISION TREE
Used over
3.5
Million
times
2.1 Million
Documents generated
IMPACT
EXAMPLE 3
Rules
Based
A.I.
Decision Trees are a step by step
memorialization of best practices
Among other things Neota
has been used to create
decision trees to support
lawyers / non lawyers
What do I do if there has been
An issue in Human Resources ?
A potential FCPA violation?
A potential data breach?
Expert Systems 

(together with data) 

will eventually
become Chatbots …
we see a decent amount of
rules based AI
in legal industry
that is actually pretty consistent
with path of A.I. in general
lots of issues
with expert systems
and/or
rules based A.I.
(without data or an evolutionary dynamic)
So Folks Often
Move Toward
Data Centric
Approaches
iterative data < > rules
A.I. State of the Art
purely data centric
augment expert forecasts w/ data
fin(tech)
is commercial field where
huge advances have been
made in science of prediction
is an emerging field where
the tools of predictive analytics
are finally being employed
fin(legal)tech
some PUBLIC examples
(many more proprietary examples)
Here are just a few
predictions
that we are trying to
accomplish in law
#Predict Relevant Documents
#Predict Case Outcomes
Data Driven Legal Underwriting
Data Driven EDiscovery/Due Diligence
(Pr...
There are 3 Known Ways
to Predict Something
fin(tech)Borrowing in part from
Experts, Crowds, Algorithms
example from our own work
predicting the decisions of the
Supreme Court of the United States
#SCOTUS
But Same Method
Could Be Applied
to Predict
Transactional Risk
Regulatory Risk
Litigation Risk
Experts
Columbia Law Review
October, 2004
Theodore W. Ruger, Pauline T. Kim,
Andrew D. Martin, Kevin M. Quinn
Legal and Political ...
experts
Case Level Prediction
Justice Level Prediction
67.4% experts
58% experts
From the 68
Included
Cases
for the
2002-2003
Supr...
these experts probably
overfit
they fit to the noise
and
not the signal
if this were
finance this
would be
trading
worse than
S&P500
#NoiseTrading
#BuffetChallenge
#BuffetChallenge
law is full of 

noise predictors …
we need to
evaluate
experts and
somehow
benchmark
their
expertise
from a pure
forecasting
standpoint
the best
known
SCOTUS
predictor is
the law
version of
superforecasting
Crowds
crowds
https://fantasyscotus.lexpredict.com/case/list/
We can
generate
Crowd
Sourced
Predictions
however,
not all
members of
crowd are
made equal
we maintain
a ‘supercrowd’
which is the top n%
of predictors
up to time t-1
the
‘supercrowd’
outperforms
the overall
crowd
(and also the
best single player)
not
enough
crowd
based
decision
making in
institutions
“Software developers were asked on two
separate days to estimate the completion
time for a given task, the hours they
proj...
NOTE:
here
is our
commercial
offering
https://lexsemble.com/
Brief Aside
About the
Power of
Crowd
Sourced
Prediction
#LegalCrowdSourcing
(most pundits did not
identify as a serious
candidate him until
mid-January 2017)
Neil Gorsuch was #1
o n o u r F a n t a ...
#FantasySCOTUS
Algorithms
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174698
Katz DM, Bommarito MJ II, Blackman J (2017), A Ge...
Professor Katz noted
…“We believe the
blend of experts,
crowds, and
algorithms is the
secret sauce for the
whole thing.”
M...
If You Want a Taste of How
the Algorithm Works …
Quantitative Methods for Lawyers
http://www.quantitativemethodsclass.com/Professor Daniel Martin Katz
Intro Class
Legal Analytics
Professor Daniel Martin Katz
Professor Michael J Bommarito II
http://www.legalanalyticscourse.com/Professor Daniel Martin Katz
Professor Michael J. Bommarito II Advanced Class
Experts, Crowds, Algorithms
For most problems ...
ensembles of these streams
outperform any single stream
Humans
+
Machines
Humans
+
Machines
>
Humans
+
Machines
Humans
or
Machines
>
A Visual Depiction of
How to build an
ensemble method in our
judicial prediction example
expert
forecast
crowd
forecast
learning problem is to discover how to blend streams of intelligence
algorithm
forecast
ens...
expert
forecast
crowd
forecast
learning problem is to discover how to blend streams of intelligence
algorithm
forecast
ens...
Given our ability to offer
forecasts of judicial
outcomes, we wondered
if this information could
inform an event driven
tr...
Revise + Resubmit @
http://arxiv.org/abs/1508.05751
available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2649726
lots of litigation decisions
are just a version of this basic idea
law = finance
of course the most well known
fin(legal)tech
is current litigation finance industry
funding sources includes:
institutional
crowdfunding
competitive funding platform
institutional
www.burfordcapital.com/
http://www.gerchenkeller.com/
http://www.fulbrookmanagement.com/
http://www.longford...
competitive funding platform
think of it as
https://mighty.com/
https://www.lexshares.com/crowdfunding
from an asset valuation standpoint
lots of litigation decisions
are actually implicit litigation finance
(or self insurance...
Consider for example …
Reserves Setting Under
FASB ASC 450-20-25
#fin(legal)tech
fin(legal)techpricing as
hourly
rates
alternative
fees
vs.
this is a really
finance/insurance question
Who should bear the cost
associated with an overrun?
Question is how to rigorously
underwrite / predict
costs of matters?
#fin(legal)tech
Corporate Counsel
+
Law Firm
pricing goal
correctly characterize the
distributional properties of
a portfolio of matters
#fin(legal)tech
#self insurance
today this is how you
would run a more
rigorous version of
AIG to Launch Data-
Driven Legal Ops
Business in 2016
https://bol.bna.com/aig-to-
launch-data-driven-legal-
ops-business-i...
#fin(legal)tech
tomorrow?
learn from legal ops service
offering to build a commercial
insurance product offering
legal cost...
#fin(legal)tech
In such a world,
Law Firm is *not* interfacing
with client but rather insurance
company regarding fees
fin(legal)tech
Transactional Work as
we just discussed price of lawyers
now lets think about
transactional value
Meet Bob
Meet Bob
lawyer on
a major
corporate
transaction
Meet Bob Bob is about to
engage in yet
another round of
markup on deal terms
lawyer on
a major
corporate
transaction
Meet Bob Bob is about to
engage in yet
another round of
markup on deal terms
this round is likely to
generate a delay on
t...
how much economic value is
created
by these modifications?
how much delay
will be introduced?
vs.
Need a better understanding
of the actual drivers of risk
Being able to compute the
change in risk as a function
of a change in deal terms
Outside of M+A
Requires Mapping of Deal Terms
to actual substantive outcomes
#legaldata
#legalanalytics
this is particularly important
when non-lawyers are
doing the negotiation
(for example your global sales force)
fin(tech) fin(legal)techvs.
Additional Lessons
fin(tech)
is commercial field where
there have have been huge
advances in working
with unstructured data
80%+ of the world’s data
is unstructured data
in a variety of ways fin(tech)
has already confronted this
Solution is to either let
tech or human process that data
And humans are actually pretty
good pattern detectors
But only for
certain types of problems
Trading (HFT in particular)
is about looking for anomalies
60 Seconds of HFT
Two
Relevant
Examples
of
Anomaly
Detection
in Law
example 1
the discovery + compliance convergence
a hard #bigdata problem in law
(near real time) compliance
FCPA, Product Defect, etc.
the goal is
near real time monitoring
defect w/5 ‘airbag’
version 1.0
backdate w/5 ‘option’
etc.
near real time monitoring of
version 2.0
a massive volume of communications
example 2
we all have a tell
lots of efforts to trade
on sentiment
lessons from fin(tech)
efforts to
trade on the
sentiment
contained
in these
and other
related
documents
https://www.wsj.com/articles/hidden-in-plain-sight-a-powerful-way-to-beat-the-market-1497367597
June 13, 2017
sentiment analysis
sentiment analysis
understanding your opponents or
other key decision makers tells
(and your own)
legal sentiment analysis
a new source of
competitive legal intelligence
combating complexity
through information mgmt.
lessons from fin(tech)
Information Management
is a significant problem in legal
data that could inform
operations is not collected /
or not regularized
information necessary to
undertake due diligence or
other regulatory exercises is
locked in an antiquated format
(i.e. pdf...
problem is
legal work product is not a
pointable data object
horizontal integration
of legal work product in the
broader corporate technology
ecosystem represents a source
of immediat...
for example -
contracts should be born
(or processed)
as computational objects
to point straight into finance/acct
and othe...
sensor data
+
contracts talking to other systems
This is the
Internet of Legal Things
#InternetofThings
#IOT
#IOT
we are starting a
decade(s) long process of
overhauling the
global financial infrastructure
it is a massive friction
reduction exercise
#fin(tech)
#fin(tech)
#fin(tech)
#fin(tech)
#fin(tech)
#fin(tech)
but blockchain is important
bitcoin is probably not that important
SOME
CONCLUDING
IMPLICATIONS
In sum, I believe …
Over the coming years,
we are going to be able
financialize large elements
of the legal industry
By which I mean —-
apply the tools of finance
and insurance to measure /
predict a wide range of
procedural + substantive
o...
we will help better establish
the value proposition
associated with a wide
range of legal tasks …
As we move items from
the ‘art’ column to the
‘science’ column …
There will be impacts
on the industrial
organization of the
legal industry
But what remains
thereafter will be a
better industry …
focusing every individual
and every organization on the
places where they actually provide
a return on investment (ROI)
Associate Professor of Law
IllinoisTech - Chicago Kent
Affiliated Faculty
Stanford CodeX
Center for Legal Informatics
Colle...
LexPredict.com
ComputationalLegalStudies.com
BLOG
@ computational
TheLawLab.com
Daniel Martin Katz
@ computational
computationallegalstudies.com
lexpredict.com
danielmartinkatz.com
illinois tech - chica...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Dan...
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Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Daniel Martin Katz + Michael J Bommarito

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Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Daniel Martin Katz + Michael J Bommarito

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Fin (Legal) Tech – Law’s Future from Finance’s Past (Some Thoughts About the Financialization of the Law) – Professors Daniel Martin Katz + Michael J Bommarito

  1. 1. law’s future from finance’s past Fin(Legal)Tech daniel martin katzdaniel martin katz blog | ComputationalLegalStudies.com corp | LexPredict.com page | DanielMartinKatz.com edu | illinois tech - chicago kent law lab | theLawLab.com michael j bommarito blog | ComputationalLegalStudies.com corp | LexPredict.com page | bommaritollc.com edu | illinois tech - chicago kent law lab | theLawLab.com
  2. 2. Three Types of Lawyers (as described by paul lippe)
  3. 3. play “whack-a-mole”, reacting to problems by creating fear and friction within organizations and the impression that there is a legal risk around every corner. Mediocre Lawyers
  4. 4. can help clients shape (perhaps distort) external perception of risk. Merely Clever Lawyers
  5. 5. design systems that balance risk and improve transparency, helping clients correctly price risk internally Great Lawyers
  6. 6. Lawyer VALUE PROPOSITION (From the Client’s Perspective) (internal or external client)
  7. 7. -Or- What is the Value of Marginal Dollar invested inside/outside lawyers (From the CEO / CFO Perspective)
  8. 8. help price risk / help reduce information asymmetries transactional =
  9. 9. litigation = characterize (predict) risk/exposure shift the expected value of a lawsuit help price risk / help reduce information asymmetries transactional =
  10. 10. litigation = characterize (predict) risk/exposure shift the expected value of a lawsuit compliance = identify + prevent rogue behavior monitor behavior in (near) real time help price risk / help reduce information asymmetries transactional =
  11. 11. litigation = characterize (predict) risk/exposure shift the expected value of a lawsuit compliance = identify + prevent rogue behavior monitor behavior in (near) real time help price risk / help reduce information asymmetries transactional = regulatory = help identify (predict) the decisions of regulators / law makers and the risk associated with various outcomes
  12. 12. Today we only want to talk about one thing … #Arbitrage
  13. 13. But if we are going to talk about #Arbitrage
  14. 14. Then we need to talk about why we *sometimes* 
 miss obvious opportunities
  15. 15. opportunities that have been right under our noses all along
  16. 16. Must get exposed to new ideas (most innovation in law started elsewhere)
  17. 17. need to develop a
  18. 18. less law centric view of the world
  19. 19. So in that spirit …
  20. 20. the analogy du jour
  21. 21. law = finance (insuranceaswell)
  22. 22. law < > finance many elements in law look like finance did 25 - 50 years ago (on the long road from Black-Scholes to algorithmic trading)
  23. 23. this is an extension of this prior talk by mike bommarito
  24. 24. Dominant Model in Law expert centered pricing of risk
  25. 25. Dominant Model in Law lots of unintentional self insurance rarely (if ever) based upon explicit risk models
  26. 26. Cult of one 
 (or very small # of) person(s) thinking drives decisions with serious financial consequences
  27. 27. hard to move to more rigorous models given borderline pathological numerophobia among lawyers
  28. 28. Claim: fin(tech) offers lessons for many areas in law
  29. 29. thesis statement: the financialization of the law will be an important vector of the next decade(s) in law
  30. 30. The Two Major Branches in #FinTech
  31. 31. The Two Major Branches in #FinTech removing socially meaningless frictions (from financial processes)
  32. 32. The Two Major Branches in #FinTech removing socially meaningless frictions characterizing (pricing) increasingly 
 exotic 
 forms of risk(from financial processes)
  33. 33. #Fin(Legal)Tech application of those ideas and technologies to a wide range of law related spheres including litigation, transactional work and compliance.
  34. 34. We recently organized 
 this conversation (Next one in October 2017)
  35. 35. FinLegalTechConference.comNovember 4, 2016
  36. 36. Fin(Legal)Tech Conference finlegaltechconference.com @Illinois Tech - Chicago Kent College of Law
  37. 37. Fin(Legal)Tech Conference finlegaltechconference.com @Illinois Tech - Chicago Kent College of Law
  38. 38. Fin(Legal)Tech Conference finlegaltechconference.com @Illinois Tech - Chicago Kent College of Law Fin(Legal)Tech Conference finlegaltechconference.com @Illinois Tech - Chicago Kent College of Law Fin(Legal)Tech Conference finlegaltechconference.com @Illinois Tech - Chicago Kent College of Law
  39. 39. #FinLegalTech
  40. 40. removing socially meaningless frictions (from legal processes)
  41. 41. across the economy there are many effort to convert an artisanal process into an industrial process
  42. 42. the industrialization of the artisan
  43. 43. lets focus on these two stages
  44. 44. there is often a significant spread between Kim Craig @ Seyfarth Lean Consulting Chicago Legal Technology + Innovation MEETUP
  45. 45. recently met with the general counsel of a large publicly traded company who has reduced the legal expenditures of the company by nearly 50% using the lean methodology over past decade
  46. 46. Lean, Six Sigma and other process improvement methodologies
  47. 47. can help improve almost every subsector in law
  48. 48. the toyota production system lean ideas lean for enterprises (white collar, etc.)
  49. 49. Remove Waste (friction) Increase predictability (profitability)
  50. 50. convert high volatility process
  51. 51. convert high volatility process into a lower volatility process
  52. 52. Not just about efficiency its also about excellence
  53. 53. Lean Process Mapping
  54. 54. KM + Lean
  55. 55. Examples:
  56. 56. http:// www.seyfarth.com/ dir_docs/ publications/ LITDecJan2014LeanS ixSigma.pdf
  57. 57. http:// www.seyfarth.com/ dir_docs/ publications/ LITDecJan2014LeanS ixSigma.pdf
  58. 58. The Course that I help co-teach at Chicago-Kent College of Law
  59. 59. #FinLegalTech
  60. 60. the path of fin(tech) has in part followed developments in artificial intelligence
  61. 61. There has been lots of recent interest in applying artificial intelligence to law
  62. 62. data driven AI rules based AI Competing Orientations in Artificial Intelligence
  63. 63. expert systems Computational Law Data Driven Rules Based prediction models and methods network analytic methods natural language processing self executing law visual law computable codes
  64. 64. Three Examples of Rules Based (Expert Systems) A.I.
  65. 65. EXAMPLE 1
  66. 66. tax preparation software
  67. 67. EXAMPLE 2
  68. 68. A2J AUTHOR www.a2jauthor.org
  69. 69. PROCESS Guided Interview Completed Document
  70. 70. LOGIC
  71. 71. DECISION TREE
  72. 72. Used over 3.5 Million times 2.1 Million Documents generated IMPACT
  73. 73. EXAMPLE 3
  74. 74. Rules Based A.I.
  75. 75. Decision Trees are a step by step memorialization of best practices
  76. 76. Among other things Neota has been used to create decision trees to support lawyers / non lawyers
  77. 77. What do I do if there has been An issue in Human Resources ? A potential FCPA violation? A potential data breach?
  78. 78. Expert Systems 
 (together with data) 
 will eventually become Chatbots …
  79. 79. we see a decent amount of rules based AI in legal industry
  80. 80. that is actually pretty consistent with path of A.I. in general
  81. 81. lots of issues with expert systems and/or rules based A.I. (without data or an evolutionary dynamic)
  82. 82. So Folks Often Move Toward Data Centric Approaches
  83. 83. iterative data < > rules A.I. State of the Art purely data centric augment expert forecasts w/ data
  84. 84. fin(tech) is commercial field where huge advances have been made in science of prediction
  85. 85. is an emerging field where the tools of predictive analytics are finally being employed fin(legal)tech
  86. 86. some PUBLIC examples (many more proprietary examples)
  87. 87. Here are just a few predictions that we are trying to accomplish in law
  88. 88. #Predict Relevant Documents #Predict Case Outcomes Data Driven Legal Underwriting Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rogue Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work #Predict Regulatory Outcomes Data Driven Lobbying, etc.
  89. 89. There are 3 Known Ways to Predict Something fin(tech)Borrowing in part from
  90. 90. Experts, Crowds, Algorithms
  91. 91. example from our own work
  92. 92. predicting the decisions of the Supreme Court of the United States #SCOTUS
  93. 93. But Same Method Could Be Applied to Predict Transactional Risk Regulatory Risk Litigation Risk
  94. 94. Experts
  95. 95. Columbia Law Review October, 2004 Theodore W. Ruger, Pauline T. Kim, Andrew D. Martin, Kevin M. Quinn Legal and Political Science Approaches to Predicting Supreme Court Decision Making The Supreme Court Forecasting Project:
  96. 96. experts
  97. 97. Case Level Prediction Justice Level Prediction 67.4% experts 58% experts From the 68 Included Cases for the 2002-2003 Supreme Court Term
  98. 98. these experts probably overfit
  99. 99. they fit to the noise and not the signal
  100. 100. if this were finance this would be trading worse than S&P500
  101. 101. #NoiseTrading
  102. 102. #BuffetChallenge
  103. 103. #BuffetChallenge
  104. 104. law is full of 
 noise predictors …
  105. 105. we need to evaluate experts and somehow benchmark their expertise
  106. 106. from a pure forecasting standpoint
  107. 107. the best known SCOTUS predictor is
  108. 108. the law version of superforecasting
  109. 109. Crowds
  110. 110. crowds
  111. 111. https://fantasyscotus.lexpredict.com/case/list/ We can generate Crowd Sourced Predictions
  112. 112. however, not all members of crowd are made equal
  113. 113. we maintain a ‘supercrowd’ which is the top n% of predictors up to time t-1
  114. 114. the ‘supercrowd’ outperforms the overall crowd (and also the best single player)
  115. 115. not enough crowd based decision making in institutions
  116. 116. “Software developers were asked on two separate days to estimate the completion time for a given task, the hours they projected differed by 71%, on average. When pathologists made two assessments of the severity of biopsy results, the correlation between their ratings was only .61 (out of a perfect 1.0), indicating that they made inconsistent diagnoses quite frequently. Judgments made by different people are even more likely to diverge.”
  117. 117. NOTE: here is our commercial offering
  118. 118. https://lexsemble.com/
  119. 119. Brief Aside About the Power of Crowd Sourced Prediction #LegalCrowdSourcing
  120. 120. (most pundits did not identify as a serious candidate him until mid-January 2017) Neil Gorsuch was #1 o n o u r F a n t a s y Platform 12 Days after Donald Trump was elected President (i.e Nov 20)
  121. 121. #FantasySCOTUS
  122. 122. Algorithms
  123. 123. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174698 Katz DM, Bommarito MJ II, Blackman J (2017), A General Approach for Predicting the Behavior of the Supreme Court of the United States. PLoS ONE 12(4): e0174698.
  124. 124. Professor Katz noted …“We believe the blend of experts, crowds, and algorithms is the secret sauce for the whole thing.” May 2nd 2017
  125. 125. If You Want a Taste of How the Algorithm Works …
  126. 126. Quantitative Methods for Lawyers
  127. 127. http://www.quantitativemethodsclass.com/Professor Daniel Martin Katz Intro Class
  128. 128. Legal Analytics Professor Daniel Martin Katz Professor Michael J Bommarito II
  129. 129. http://www.legalanalyticscourse.com/Professor Daniel Martin Katz Professor Michael J. Bommarito II Advanced Class
  130. 130. Experts, Crowds, Algorithms
  131. 131. For most problems ... ensembles of these streams outperform any single stream
  132. 132. Humans + Machines
  133. 133. Humans + Machines >
  134. 134. Humans + Machines Humans or Machines >
  135. 135. A Visual Depiction of How to build an ensemble method in our judicial prediction example
  136. 136. expert forecast crowd forecast learning problem is to discover how to blend streams of intelligence algorithm forecast ensemble method ensemble model
  137. 137. expert forecast crowd forecast learning problem is to discover how to blend streams of intelligence algorithm forecast ensemble method ensemble model via back testing we can learn the weights to apply for particular problems
  138. 138. Given our ability to offer forecasts of judicial outcomes, we wondered if this information could inform an event driven trading strategy ?
  139. 139. Revise + Resubmit @ http://arxiv.org/abs/1508.05751 available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2649726
  140. 140. lots of litigation decisions are just a version of this basic idea law = finance
  141. 141. of course the most well known fin(legal)tech is current litigation finance industry
  142. 142. funding sources includes: institutional crowdfunding competitive funding platform
  143. 143. institutional www.burfordcapital.com/ http://www.gerchenkeller.com/ http://www.fulbrookmanagement.com/ http://www.longfordcapital.com/ http://www.benthamimf.com/
  144. 144. competitive funding platform think of it as https://mighty.com/
  145. 145. https://www.lexshares.com/crowdfunding
  146. 146. from an asset valuation standpoint lots of litigation decisions are actually implicit litigation finance (or self insurance) #fin(legal)tech
  147. 147. Consider for example … Reserves Setting Under FASB ASC 450-20-25 #fin(legal)tech
  148. 148. fin(legal)techpricing as
  149. 149. hourly rates alternative fees vs.
  150. 150. this is a really finance/insurance question
  151. 151. Who should bear the cost associated with an overrun?
  152. 152. Question is how to rigorously underwrite / predict costs of matters?
  153. 153. #fin(legal)tech Corporate Counsel + Law Firm pricing goal
  154. 154. correctly characterize the distributional properties of a portfolio of matters
  155. 155. #fin(legal)tech #self insurance today this is how you would run a more rigorous version of
  156. 156. AIG to Launch Data- Driven Legal Ops Business in 2016 https://bol.bna.com/aig-to- launch-data-driven-legal- ops-business-in-2016/
  157. 157. #fin(legal)tech tomorrow? learn from legal ops service offering to build a commercial insurance product offering legal cost insurance ? other exotic insurance offerings?
  158. 158. #fin(legal)tech In such a world, Law Firm is *not* interfacing with client but rather insurance company regarding fees
  159. 159. fin(legal)tech Transactional Work as
  160. 160. we just discussed price of lawyers
  161. 161. now lets think about transactional value
  162. 162. Meet Bob
  163. 163. Meet Bob lawyer on a major corporate transaction
  164. 164. Meet Bob Bob is about to engage in yet another round of markup on deal terms lawyer on a major corporate transaction
  165. 165. Meet Bob Bob is about to engage in yet another round of markup on deal terms this round is likely to generate a delay on the expected close of the deal lawyer on a major corporate transaction
  166. 166. how much economic value is created by these modifications? how much delay will be introduced? vs.
  167. 167. Need a better understanding of the actual drivers of risk
  168. 168. Being able to compute the change in risk as a function of a change in deal terms
  169. 169. Outside of M+A Requires Mapping of Deal Terms to actual substantive outcomes #legaldata #legalanalytics
  170. 170. this is particularly important when non-lawyers are doing the negotiation (for example your global sales force)
  171. 171. fin(tech) fin(legal)techvs. Additional Lessons
  172. 172. fin(tech) is commercial field where there have have been huge advances in working with unstructured data
  173. 173. 80%+ of the world’s data is unstructured data
  174. 174. in a variety of ways fin(tech) has already confronted this
  175. 175. Solution is to either let tech or human process that data
  176. 176. And humans are actually pretty good pattern detectors
  177. 177. But only for certain types of problems
  178. 178. Trading (HFT in particular) is about looking for anomalies 60 Seconds of HFT
  179. 179. Two Relevant Examples of Anomaly Detection in Law
  180. 180. example 1
  181. 181. the discovery + compliance convergence
  182. 182. a hard #bigdata problem in law (near real time) compliance FCPA, Product Defect, etc.
  183. 183. the goal is near real time monitoring
  184. 184. defect w/5 ‘airbag’ version 1.0 backdate w/5 ‘option’ etc.
  185. 185. near real time monitoring of version 2.0 a massive volume of communications
  186. 186. example 2
  187. 187. we all have a tell
  188. 188. lots of efforts to trade on sentiment lessons from fin(tech)
  189. 189. efforts to trade on the sentiment contained in these and other related documents
  190. 190. https://www.wsj.com/articles/hidden-in-plain-sight-a-powerful-way-to-beat-the-market-1497367597 June 13, 2017
  191. 191. sentiment analysis
  192. 192. sentiment analysis
  193. 193. understanding your opponents or other key decision makers tells (and your own)
  194. 194. legal sentiment analysis a new source of competitive legal intelligence
  195. 195. combating complexity through information mgmt. lessons from fin(tech)
  196. 196. Information Management is a significant problem in legal
  197. 197. data that could inform operations is not collected / or not regularized
  198. 198. information necessary to undertake due diligence or other regulatory exercises is locked in an antiquated format (i.e. pdf, word, tif file)
  199. 199. problem is legal work product is not a pointable data object
  200. 200. horizontal integration of legal work product in the broader corporate technology ecosystem represents a source of immediate value creation
  201. 201. for example - contracts should be born (or processed) as computational objects to point straight into finance/acct and other relevant IT systems stored legal work product
  202. 202. sensor data + contracts talking to other systems
  203. 203. This is the Internet of Legal Things #InternetofThings #IOT #IOT
  204. 204. we are starting a decade(s) long process of overhauling the global financial infrastructure
  205. 205. it is a massive friction reduction exercise
  206. 206. #fin(tech)
  207. 207. #fin(tech)
  208. 208. #fin(tech)
  209. 209. #fin(tech)
  210. 210. #fin(tech)
  211. 211. #fin(tech)
  212. 212. but blockchain is important bitcoin is probably not that important
  213. 213. SOME CONCLUDING IMPLICATIONS
  214. 214. In sum, I believe …
  215. 215. Over the coming years, we are going to be able financialize large elements of the legal industry
  216. 216. By which I mean —- apply the tools of finance and insurance to measure / predict a wide range of procedural + substantive outcomes
  217. 217. we will help better establish the value proposition associated with a wide range of legal tasks …
  218. 218. As we move items from the ‘art’ column to the ‘science’ column …
  219. 219. There will be impacts on the industrial organization of the legal industry
  220. 220. But what remains thereafter will be a better industry …
  221. 221. focusing every individual and every organization on the places where they actually provide a return on investment (ROI)
  222. 222. Associate Professor of Law IllinoisTech - Chicago Kent Affiliated Faculty Stanford CodeX Center for Legal Informatics College of Law Chief Strategy Officer LexPredict
  223. 223. LexPredict.com
  224. 224. ComputationalLegalStudies.com BLOG
  225. 225. @ computational
  226. 226. TheLawLab.com
  227. 227. Daniel Martin Katz @ computational computationallegalstudies.com lexpredict.com danielmartinkatz.com illinois tech - chicago kent college of law@ thelawlab.com

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