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
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. 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
4. 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
5. can help clients shape
(perhaps distort)
external perception of risk.
Merely Clever Lawyers
6. design systems that
balance risk and improve
transparency, helping clients
correctly price risk internally
Great Lawyers
9. -Or-
What is the
Value of Marginal Dollar
invested inside/outside
lawyers
(From the CEO / CFO Perspective)
10. help price risk /
help reduce information asymmetries
transactional =
11. litigation =
characterize (predict) risk/exposure
shift the expected value of a lawsuit
help price risk /
help reduce information asymmetries
transactional =
12. 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 =
13. 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
14.
15. Today we only want to talk
about one thing …
#Arbitrage
36. The Two Major Branches
in #FinTech
removing
socially
meaningless
frictions
(from financial processes)
37. The Two Major Branches
in #FinTech
removing
socially
meaningless
frictions
characterizing
(pricing)
increasingly
exotic
forms of risk(from financial processes)
38. #Fin(Legal)Tech
application of those ideas and
technologies to a wide range of
law related spheres including
litigation, transactional work
and compliance.
43. 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
51. there is often a significant
spread between
Kim Craig @ Seyfarth Lean Consulting
Chicago Legal Technology + Innovation MEETUP
52.
53. 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
80. the path of fin(tech)
has in part followed
developments
in artificial intelligence
81. There has been lots of recent
interest in applying
artificial intelligence to law
82.
83.
84.
85.
86.
87.
88. data driven AI rules based AI
Competing Orientations in
Artificial Intelligence
89. 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
117. Here are just a few
predictions
that we are trying to
accomplish in law
118. #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.
119.
120. There are 3 Known Ways
to Predict Something
fin(tech)Borrowing in part from
127. 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:
155. “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.”
161. (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)
182. 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
183.
184. Given our ability to offer
forecasts of judicial
outcomes, we wondered
if this information could
inform an event driven
trading strategy ?
185. Revise + Resubmit @
http://arxiv.org/abs/1508.05751
available at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2649726
186.
187. lots of litigation decisions
are just a version of this basic idea
law = finance
188. of course the most well known
fin(legal)tech
is current litigation finance industry
204. AIG to Launch Data-
Driven Legal Ops
Business in 2016
https://bol.bna.com/aig-to-
launch-data-driven-legal-
ops-business-in-2016/
205. #fin(legal)tech
tomorrow?
learn from legal ops service
offering to build a commercial
insurance product offering
legal cost insurance ?
other exotic insurance offerings?
206. #fin(legal)tech
In such a world,
Law Firm is *not* interfacing
with client but rather insurance
company regarding fees
213. Meet Bob Bob is about to
engage in yet
another round of
markup on deal terms
lawyer on
a major
corporate
transaction
214. 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
215.
216. how much economic value is
created
by these modifications?
how much delay
will be introduced?
vs.
217. Need a better understanding
of the actual drivers of risk
218. Being able to compute the
change in risk as a function
of a change in deal terms
219.
220. Outside of M+A
Requires Mapping of Deal Terms
to actual substantive outcomes
#legaldata
#legalanalytics
221. this is particularly important
when non-lawyers are
doing the negotiation
(for example your global sales force)
262. horizontal integration
of legal work product in the
broader corporate technology
ecosystem represents a source
of immediate value creation
263. 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
287. focusing every individual
and every organization on the
places where they actually provide
a return on investment (ROI)
288.
289. Associate Professor of Law
IllinoisTech - Chicago Kent
Affiliated Faculty
Stanford CodeX
Center for Legal Informatics
College of Law
Chief Strategy Officer
LexPredict
294. Daniel Martin Katz
@ computational
computationallegalstudies.com
lexpredict.com
danielmartinkatz.com
illinois tech - chicago kent college of law@
thelawlab.com