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

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 is a
legal risk around every corner.
Mediocre Lawyers

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 /
transactional =

litigation =
compliance = identify + prevent rogue behavior
monitor behavior in (near) real time
help price risk /
transactional =

litigation =
compliance = identify + prevent rogue behavior
monitor behavior in (near) real time
help price risk /
transactional =
regulatory =
help identify (predict) the decisions
of regulators / law makers and the risk
associated with various outcomes

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)

less law centric view of the world

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 algorithmic trading)

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

in #FinTech
removing
socially
meaningless
frictions
(from ﬁnancial processes)

in #FinTech
removing
socially
meaningless
frictions
characterizing
(pricing)
increasingly  
exotic  
forms of risk(from ﬁnancial processes)

#Fin(Legal)Tech
application of those ideas and
technologies to a wide range of
law related spheres including
litigation, transactional work
and compliance.

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

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 MEETUP

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

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

http://
www.seyfarth.com/
dir_docs/
publications/
LITDecJan2014LeanS
ixSigma.pdf

The Course that I help
co-teach at
Chicago-Kent
College of Law

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
language
processing
self
executing
law
visual
law
computable
codes

Three Examples
of Rules Based
(Expert Systems)
A.I.

PROCESS
Guided
Interview
Completed
Document

Used over
3.5
Million
times
2.1 Million
Documents generated
IMPACT

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
(Predictive Coding)
#Predict Rogue Behavior
Data Driven Compliance
#Predict Contract Terms/Outcomes
Data Driven Transactional Work
#Predict Regulatory Outcomes
Data Driven Lobbying, etc.

There are 3 Known Ways
to Predict Something
ﬁn(tech)Borrowing in part from

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

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:

Case Level Prediction
Justice Level Prediction
67.4% experts
58% experts
From the 68
Included
Cases
for the
2002-2003
Supreme
Court Term

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

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

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
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.”

NOTE:
here
is our
commercial
offering

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 s y
Platform 12 Days after
Donald Trump was
elected President
(i.e Nov 20)

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.

Professor Katz noted
…“We believe the
blend of experts,
crowds, and
algorithms is the
secret sauce for the
whole thing.”
May 2nd 2017

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

For most problems ...
ensembles of these streams
outperform any single stream

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
ensemble method
ensemble model

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

Given our ability to offer
forecasts of judicial
outcomes, we wondered
if this information could
inform an event driven
trading strategy ?

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.longfordcapital.com/
http://www.benthamimf.com/

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)
#ﬁn(legal)tech

Consider for example …
Reserves Setting Under
FASB ASC 450-20-25
#ﬁn(legal)tech

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-in-2016/

#ﬁn(legal)tech
tomorrow?
learn from legal ops service
offering to build a commercial
insurance product offering
legal cost insurance ?
other exotic insurance offerings?

#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
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
the expected
close of the deal
lawyer on
a major
corporate
transaction

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

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

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

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, word, tif ﬁle)

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 immediate value creation

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

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

but blockchain is important
bitcoin is probably not that important

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
outcomes

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
College of Law
Chief Strategy Ofﬁcer
LexPredict

ComputationalLegalStudies.com
BLOG

Daniel Martin Katz
@ computational
computationallegalstudies.com
lexpredict.com
danielmartinkatz.com
illinois tech - chicago kent college of law@
thelawlab.com

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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