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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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