Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Together - Professor Daniel Martin Katz

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Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Together - Professor Daniel Martin Katz

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Measure Twice, Cut Once - Solving the Legal Profession Biggest Challenges Together - Professor Daniel Martin Katz

  1. 1. measure twice, cut once Solving the Legal Profession's Biggest Problems Together daniel martin katz blog | ComputationalLegalStudies.com corp | LexPredict.com page | DanielMartinKatz.com edu | chicago kent college of law
  2. 2. collectively this industry faces some real challenges …
  3. 3. challenges that have been 
 well documented …
  4. 4. But I believe that we are going to persist
  5. 5. indeed, I think we can thrive…
  6. 6. my resolution is not related to the notion that the world owes us anything
  7. 7. But rather it is related to this group and groups like it
  8. 8. working together help solve 
 the Legal Profession's Biggest Problems
  9. 9. But we have real work to do
  10. 10. So today I would like to focus my comments …
  11. 11. on how we together might build a more perfect supply chain
  12. 12. financially rigorous measurement of the value proposition associated with various legal services centered upon
  13. 13. moving items from the ‘art’ column and to the ‘science’ column …
  14. 14. so today 
 a presentation in five parts …
  15. 15. the economics of law the industrialization of the artisan toward an enterprise data strategy in legal fin (legal) tech Legal Analytics + #MLaaS part 1: part 2: part 3: part 4: part 5:
  16. 16. the economics of law Part One
  17. 17. I would like to take a step back
  18. 18. When we look at the industry…
  19. 19. under alternative conditions its structure might have differed
  20. 20. there are fundamental economic principles which have yielded
  21. 21. the current industrial organization 
 of the legal industry
  22. 22. why do we have lawyers? (in other words what do they solve for …)
  23. 23. help navigate complexity manage enterprise (legal) risk +
  24. 24. Social, Economic and Political Complexity
  25. 25. Which for our purposes manifests in legal complexity
  26. 26. In the face of ever growing legal complexity we have applied greater and greater numbers of human experts to solve the underlying problem
  27. 27. Lawyer as Complexity Engineer
  28. 28. complexity keeps growing ...
  29. 29. and so has total expenditures on legal services
  30. 30. Legal Expenditures as a function of GDP (some disagreement between these plots but they project a similar trend)
  31. 31. Cobb Douglas is the traditional way to describe a production function
  32. 32. LaborCapital Cobb Douglas is the traditional way to describe a production function
  33. 33. Capital Cobb Douglas is the traditional way to describe a production function Labor historically we have turned this dial
  34. 34. Where is are large scale complexity filled opportunities in law?
  35. 35. Where is are large scale complexity filled opportunities in law? BANKS AS CLIENTS (and TECH AS CLIENTS)
  36. 36. manage enterprise (legal) risk
  37. 37. Three Types of Lawyers (as described by paul lippe)
  38. 38. 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
  39. 39. can help clients shape (perhaps distort) external perception of risk. Merely Clever Lawyers
  40. 40. design systems that balance risk and improve transparency, helping clients correctly price risk internally Great Lawyers
  41. 41. when it comes to risk … one challenge with identifying their value proposition is the counterfactual
  42. 42. why do we have law firms? (in other words what do they solve for …)
  43. 43. The Enterprise Consumer (client) always faces the decision of 
 make versus buy
  44. 44. Solving the Peak Load Problem
  45. 45. Provide High Value but Rarely Utilized or Hard to Acquire Expertise
  46. 46. Without sufficient volume it is not sensible to make but rather to buy …
  47. 47. lawyers and law firms provide substantial value
  48. 48. BUT the problem of agency costs always looms
  49. 49. an economic concept concerning the fee to a “principal” (an organization, person or group of persons), when the principal chooses or hires an "agent" to act on its behalf. Because the two parties have different interests and the agent has more information, the principal cannot directly ensure that its agent is always acting in its (the principal's) best interests.
  50. 50. agency costs turn allies (friends) into frenemies
  51. 51. frenemy
  52. 52. agency costs muddy the inside vs outside counsel relationship
  53. 53. (Partially) solving the industry’s requires engineering around these agency issues?
  54. 54. Measurement, Standards, Metrics, #DATA
  55. 55. this allows for a partial solution to the problem
  56. 56. Many at this conference and at conferences like this are working on the problem
  57. 57. there are successes from other sectors of the economy from whom we can learn
  58. 58. where the supply chain thrived in a metrics heavy environment…
  59. 59. the industrialization of the artisan Part Two
  60. 60. across the economy there are many effort to convert an artisanal process into an industrial process
  61. 61. as we move toward a more metrics centered field we want to ensure that we can maintain the artisan elements that DO ADD VALUE
  62. 62. My favorite non-legal example Sal Consiglio - (Sally’s APizza) Domino’s Ad Circa 1990’s ARTISAN INDUSTRIAL
  63. 63. the industrialization of the artisan
  64. 64. lets focus on these two because for now this is where 
 process improvement and 
 data should be directed
  65. 65. so … remaining mindful of the lawyer and law firm value proposition
  66. 66. but with an eye toward reducing the agency costs issues …
  67. 67. every organization in law needs a data strategy
  68. 68. Toward an Enterprise Data Strategy in Legal Part Three
  69. 69. every organization in law needs a data strategy
  70. 70. Capture, Clean, Regularize Data to support a range of tasks
  71. 71. Deploy Data for Specific Enterprise Applications Develop a data roadmap
  72. 72. We want data to help support two major things …
  73. 73. We want data to help support two major things … substantive predictions procedural predictions
  74. 74. there is often a significant spread between Kim Craig @ Seyfarth Lean Consulting Chicago Legal Technology + Innovation MEETUP
  75. 75. so think of the process map as a first order estimation of your actual processes
  76. 76. rich / granular data can help illuminate the actual processes present in various (legal) organizations
  77. 77. for each node in a process we want to be able to render a prediction about things such as duration,cost, etc.
  78. 78. each unit of time linked + logged to a node on the process map
  79. 79. if there is not a node than it can be added and thus the map becomes more reflective of reality
  80. 80. just be careful not to create a #ridiculogram
  81. 81. with predictions about individual nodes
  82. 82. we can then sum to generate predictions about the distributional moments of an overall matter (or phase) (i.e. mean, variance, skewness, kurtosis)
  83. 83. this matter should take … between 9-15 months in 85% of the similar matters (what about the long tail?)
  84. 84. this matter will cost… most common range 275k - 345k but the second mode is 555k - 625k (and that second mode typically is achieved when the following factors are present … )
  85. 85. #LegalData Collaboration Point
  86. 86. transparency as the relationship glue (and trust that comes with transparency)
  87. 87. how could you facilitate data sharing / transparency?
  88. 88. sharing data between customer and client (real time, no filter?)
  89. 89. are law firms AND corporate counsel willing to engage in a 
 two way data exchange ?
  90. 90. We want data to help support two major things … substantive predictions procedural predictions
  91. 91. Here are just a subset of the substantive predictions we are trying to undertake in legal …
  92. 92. #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding) SUBSTANTIVE LEGAL PREDICTIONS
  93. 93. #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Contract Terms/Outcomes Data Driven Transactional Work SUBSTANTIVE LEGAL PREDICTIONS
  94. 94. #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rouge Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work SUBSTANTIVE LEGAL PREDICTIONS
  95. 95. #Predict Relevant Documents #Predict Case Outcomes Data Driven Legal Underwriting Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rouge Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work SUBSTANTIVE LEGAL PREDICTIONS
  96. 96. Data Driven Transactional Work
  97. 97. Meet Bob
  98. 98. Meet Bob lawyer on a major corporate transaction
  99. 99. Meet Bob Bob is about to engage in yet another round of markup on deal terms lawyer on a major corporate transaction
  100. 100. 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
  101. 101. how much value is created by these modifications? how much delay will be introduced? vs.
  102. 102. Need a better understanding of the actual drivers of risk
  103. 103. Being able to compute the change in risk as a function of a change in deal terms
  104. 104. Requires Mapping of Deal Terms to actual substantive outcomes #legaldata #legalanalytics
  105. 105. this is particularly important when non-lawyers are doing the negotiation (for example your global sales force)
  106. 106. Data Driven Compliance
  107. 107. 80%+ of the world’s data is unstructured data
  108. 108. Solution is to either let tech or human process that data
  109. 109. And humans are actually pretty good pattern detectors
  110. 110. But only for certain types of problems
  111. 111. Trading (HFT in particular) is about looking for anomalies
  112. 112. the discovery + compliance convergence
  113. 113. a hard #bigdata problem in law (near real time) compliance FCPA, Product Defect, etc.
  114. 114. the goal is near real time monitoring
  115. 115. defect w/5 ‘airbag’ version 1.0 backdate w/5 ‘option’ etc.
  116. 116. near real time monitoring of version 2.0 a massive volume of communications
  117. 117. Corp Security Beginning to mirror today’s NSA
  118. 118. Behavior will change (i.e. rogue action will be done offline) Corp Security Beginning to mirror today’s NSA
  119. 119. Behavior will change But Behavior Change will lag (i.e. rogue action will be done offline) (i.e. folks will craft incriminating communications at least for a while) Corp Security Beginning to mirror today’s NSA
  120. 120. thus, discovery (in part) becomes compliance and some (only some) litigation is avoided legal standards will still shift real time monitoring will generate lots of false positives
  121. 121. #Predict Case Outcomes Data Driven Legal Underwriting
  122. 122. A Deeper Dive on Predicting Predicting Case Outcomes (other problems can be solved using similar methods)
  123. 123. Supreme Court of United States #PredictSCOTUS
  124. 124. There are only 3 ways 
 to predict something Experts Crowds Algorithms
  125. 125. Experts
  126. 126. 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:
  127. 127. experts
  128. 128. Case Level Prediction Justice Level Prediction 67.4% experts 58% experts From the 68 Included Cases for the 2002-2003 Supreme Court Term
  129. 129. these experts probably overfit
  130. 130. they fit to the noise and not the signal
  131. 131. we need to evaluate experts and somehow benchmark their expertise
  132. 132. from a pure forecasting standpoint
  133. 133. the best known SCOTUS predictor is
  134. 134. the law version of superforecasting
  135. 135. Crowds
  136. 136. crowds
  137. 137. https://fantasyscotus.lexpredict.com/case/list/ We can generate Crowd Sourced Predictions
  138. 138. however, not all members of crowd are made equal
  139. 139. we maintain a ‘supercrowd’ which is the top n% of predictors up to time t
  140. 140. the ‘supercrowd’ outperforms the overall crowd (and the best single player)
  141. 141. not enough crowd based decision making in institutions
  142. 142. “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.”
  143. 143. in law here is a commercial offering
  144. 144. design to unlock untapped expertise in organizations
  145. 145. Allowing for Frictionless Crowdsourcing #ManualUnderwriting
  146. 146. https://lexsemble.com/
  147. 147. https://lexsemble.com/
  148. 148. Algorithms
  149. 149. Black Reed Frankfurter Douglas Jackson Burton Clark Minton Warren Harlan Brennan Whittaker Stewart White Goldberg Fortas Marshall Burger Blackmun Powell Rehnquist Stevens OConnor Scalia Kennedy Souter Thomas Ginsburg Breyer Roberts Alito Sotomayor Kagan 1953 1963 1973 1983 1993 2003 2013 9-0 Reverse 8-1, 7-2, 6-3 19 19 19 19 19 20 20 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 - Reverse 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 - 8-1, 7-2, 6-3 9-0 19 19 19 19 19 20 20 algorithms
  150. 150. we have developed an algorithm that we call {Marshall}+ random forest
  151. 151. Benchmarking since 1953 + Using only data available prior to the decision Mean Court Direction [FE] Mean Court Direction 10 [FE] Mean Court Direction Issue [FE] Mean Court Direction Issue 10 [FE] Mean Court Direction Petitioner [FE] Mean Court Direction Petitioner 10 [FE] Mean Court Direction Respondent [FE] Mean Court Direction Respondent 10 [FE] Mean Court Direction Circuit Origin [FE] Mean Court Direction Circuit Origin 10 [FE] Mean Court Direction Circuit Source [FE] Mean Court Direction Circuit Source 10 [FE] Difference Justice Court Direction [FE] Abs. Difference Justice Court Direction [FE] Difference Justice Court Direction Issue [FE] Abs. Difference Justice Court Direction Issue [FE] Z Score Difference Justice Court Direction Issue [FE] Difference Justice Court Direction Petitioner [FE] Abs. Difference Justice Court Direction Petitioner [FE] Difference Justice Court Direction Respondent [FE] Abs. Difference Justice Court Direction Respondent [FE] Z Score Justice Court Direction Difference [FE] Justice Lower Court Direction Difference [FE] Justice Lower Court Direction Abs. Difference [FE] Justice Lower Court Direction Z Score [FE] Z Score Justice Lower Court Direction Difference [FE] Agreement of Justice with Majority [FE] Agreement of Justice with Majority 10 [FE] Difference Court and Lower Ct Direction [FE] Abs. Difference Court and Lower Ct Direction [FE] Z-Score Difference Court and Lower Ct Direction [FE] Z-Score Abs. Difference Court and Lower Ct Direction [FE] Justice [S] Justice Gender [FE] Is Chief [FE] Party President [FE] Natural Court [S] Segal Cover Score [SC] Year of Birth [FE] Mean Lower Court Direction Circuit Source [FE] Mean Lower Court Direction Circuit Source 10 [FE] Mean Lower Court Direction Issue [FE] Mean Lower Court Direction Issue 10 [FE] Mean Lower Court Direction Petitioner [FE] Mean Lower Court Direction Petitioner 10 [FE] Mean Lower Court Direction Respondent [FE] Mean Lower Court Direction Respondent 10 [FE] Mean Justice Direction [FE] Mean Justice Direction 10 [FE] Mean Justice Direction Z Score [FE] Mean Justice Direction Petitioner [FE] Mean Justice Direction Petitioner 10 [FE] Mean Justice Direction Respondent [FE] Mean Justice Direction Respondent 10 [FE] Mean Justice Direction for Circuit Origin [FE] Mean Justice Direction for Circuit Origin 10 [FE] Mean Justice Direction for Circuit Source [FE] Mean Justice Direction for Circuit Source 10 [FE] Mean Justice Direction by Issue [FE] Mean Justice Direction by Issue 10 [FE] Mean Justice Direction by Issue Z Score [FE] Admin Action [S] Case Origin [S] Case Origin Circuit [S] Case Source [S] Case Source Circuit [S] Law Type [S] Lower Court Disposition Direction [S] Lower Court Disposition [S] Lower Court Disagreement [S] Issue [S] Issue Area [S] Jurisdiction Manner [S] Month Argument [FE] Month Decision [FE] Petitioner [S] Petitioner Binned [FE] Respondent [S] Respondent Binned [FE] Cert Reason [S] Mean Agreement Level of Current Court [FE] Std. Dev. of Agreement Level of Current Court [FE] Mean Current Court Direction Circuit Origin [FE] Std. Dev. Current Court Direction Circuit Origin [FE] Mean Current Court Direction Circuit Source [FE] Std. Dev. Current Court Direction Circuit Source [FE] Mean Current Court Direction Issue [FE] Z-Score Current Court Direction Issue [FE] Std. Dev. Current Court Direction Issue [FE] Mean Current Court Direction [FE] Std. Dev. Current Court Direction [FE] Mean Current Court Direction Petitioner [FE] Std. Dev. Current Court Direction Petitioner [FE] Mean Current Court Direction Respondent [FE] Std. Dev. Current Court Direction Respondent [FE] 0.00781 0.00205 0.00283 0.00604 0.00764 0.00971 0.00793 TOTAL 0.04403 Justice and Court Background Information Case Information 0.00978 0.00971 0.00845 0.00953 0.01015 0.01370 0.01190 0.01125 0.00706 0.01541 0.01469 0.00595 0.02014 0.01349 0.01406 0.01199 0.01490 0.01179 0.01408 TOTAL 0.22814 Overall Historic Supreme Court Trends 0.00988 0.01997 0.01546 0.00938 0.00863 0.00904 0.00875 0.00925 0.00791 0.00864 0.00951 0.01017 TOTAL 0.12663 Lower Court Trends 0.00962 0.01017 0.01334 0.00933 0.00949 0.00874 0.00973 0.00900 TOTAL 0.07946 0.00955 0.00936 0.00789 0.00850 0.00945 0.01021 0.01469 0.00832 0.01266 0.00918 0.00942 0.00863 0.00894 0.00882 0.00888 Current Supreme Court Trends TOTAL 0.14456 Individual Supreme Court Justice Trends 0.01248 0.01530 0.00826 0.00732 0.01027 0.00724 0.01030 0.00792 0.00945 0.00891 0.00970 0.01881 0.00950 0.00771 TOTAL 0.14323 0.01210 0.00929 0.01167 0.00968 0.01055 0.00705 0.00708 0.00690 0.00699 0.01280 0.01922 0.02494 0.01126 0.00992 0.00866 0.01483 0.01522 0.01199 0.01217 0.01150 TOTAL 0.23391 Differences in Trends
  152. 152. Total Cases Predicted Total Votes Predicted 7,700 68,964
  153. 153. Justice Prediction Case Prediction 70.9% accuracy 69.6% accuracy From 1953 - 2014
  154. 154. Our algorithm is a special version of random forest Black Reed Frankfurter Douglas Jackson Burton Clark Minton Warren Harlan Brennan Whittaker Stewart White Goldberg Fortas Marshall Burger Blackmun Powell Rehnquist Stevens OConnor Scalia Kennedy Souter Thomas Ginsburg Breyer Roberts Alito Sotomayor Kagan 1953 1963 1973 1983 1993 2003 2013 9-0 Reverse 8-1, 7-2, 6-3 19 19 19 19 19 20 20 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 - Reverse 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 - 8-1, 7-2, 6-3 9-0 19 19 19 19 19 20 20 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244 http://arxiv.org/abs/1407.6333 available at Revise and Resubmit @ PloS One
  155. 155. Experts, Crowds, Algorithms
  156. 156. For most problems ... ensembles of these streams outperform any single stream
  157. 157. Humans + Machines
  158. 158. Humans + Machines >
  159. 159. Humans + Machines Humans or Machines >
  160. 160. Ensembles come in various forms
  161. 161. Here is a well known example
  162. 162. Poll Aggregation is one form of ensemble where the learning question is to determine how much weight (if any) to assign to each individual poll
  163. 163. poll weighting
  164. 164. A Visual Depiction of How to build an ensemble method in our judicial prediction example
  165. 165. expert crowd algorithm ensemble method learning problem is to discover when to use a given stream of intelligence
  166. 166. expert crowd algorithm via back testing we can learn the weights to apply for particular problems ensemble method learning problem is to discover when to use a given stream of intelligence
  167. 167. Legal Analytics + #MLaaS Act Four
  168. 168. Given large fixed costs infrastructure + human capital (data scientists) Historically speaking
  169. 169. harder to successfully deploy high quality enterprise applications for relatively narrow (sub)verticals
  170. 170. Law is a relatively small vertical and there is lots of diversity among tasks lawyers undertake …
  171. 171. in addition there is a borderline pathological numerophobia among lawyers
  172. 172. Analytics / Quant Legal Prediction has come to law Notwithstanding these head winds—
  173. 173. I predict some very interesting economic forces will impact the #legalanalytics space
  174. 174. And applications are about to get far cheaper to develop
  175. 175. Emerging Business Model - Machine Learning as a Service #MLaaS
  176. 176. The Cloud Wars
  177. 177. Commercial Examples
  178. 178. Machine Learning as a Service #MLaaS
  179. 179. Machine Learning as a Service #MLaaS
  180. 180. Machine Learning as a Service #MLaaS
  181. 181. Machine Learning as a Service #MLaaS
  182. 182. historically one needed to build the full stack (i.e end to end) for an application
  183. 183. Standing on 
 the Shoulders of Giants
  184. 184. The (Emerging) Last Mile Problem in (Legal) Analytics
  185. 185. Off the Shelf #MLaaS, etc. (perhaps with some configuration and/or customization) Unique Domain Specific Offering
  186. 186. MLaas + Open Source Decreases Cost of Production Lowers the Cost of Protoyping
  187. 187. Fin (Legal) Tech Part Five
  188. 188. Today I have encouraged collaboration
  189. 189. and one reason is that both sides (firms / clients) can unlock more enterprise value by working together
  190. 190. because past is merely prelude
  191. 191. because the biggest change in legal not robots
  192. 192. because the biggest change in legal financialization not robots
  193. 193. developing a data strategy
  194. 194. developing a data strategy leveraging #MLaaS
  195. 195. developing a data strategy leveraging #MLaaS we get better at predicting
  196. 196. developing a data strategy leveraging #MLaaS we get better at predicting which opens the door for… #FinTech
  197. 197. #FinTech removing socially meaningless frictions characterizing (pricing) increasingly exotic forms of risk
  198. 198. #Fin(Legal)Tech application of those ideas and technology to a wide range of law related spheres including litigation, transactional work and compliance.
  199. 199. if we can predict we can develop insurance
  200. 200. if we can predict we can develop insurance if we can predict we can develop trading strategies
  201. 201. if we can predict we can develop insurance if we can predict we can develop trading strategies if we can predict we have assets under mgmt.
  202. 202. Just a Few Examples of Fin(Legal)Tech
  203. 203. #fin(legal)tech pricing
  204. 204. it is *not* predicting cost of this particular matter where n=1
  205. 205. correctly characterize the distributional properties of a portfolio of matters
  206. 206. both + and - including identification of outliers
  207. 207. apply portfolio theory
  208. 208. to take n=1 and scale to n=many #fin(legal)tech
  209. 209. #self insurance today this is how you would run a more rigorous version of
  210. 210. tomorrow? learn from legal ops service offering to build a commercial insurance product offering legal cost insurance ? other exotic insurance offerings?
  211. 211. AIG to Launch Data- Driven Legal Ops Business in 2016 https://bol.bna.com/aig-to- launch-data-driven-legal- ops-business-in-2016/
  212. 212. #fin(legal)tech In such a world, Law Firm is *not* interfacing with client but rather insurance company regarding fees
  213. 213. Earlier I discussed the application of experts, crowds + algorithms
  214. 214. as applied to predicting case outcomes
  215. 215. that was an example of manual underwriting
  216. 216. Given our ability to offer forecasts of judicial outcomes, we wondered if this information could inform an event driven trading strategy ?
  217. 217. Paper Released August 24, 2015 http://arxiv.org/abs/1508.05751 available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2649726
  218. 218. lots of litigation decisions are just a version of this basic idea law = finance
  219. 219. lots of litigation decisions are actually implicit litigation finance (or self insurance) #fin(legal)tech
  220. 220. But most implicit litigation finance is not based upon 
 rigorous underwriting … law =! finance (but it will)
  221. 221. http://www.slideshare.net/ Danielkatz/fin-legal-tech-laws- future-from-finances-past- professors-daniel-martin-katz- michael-j-bommarito-ii
  222. 222. TheLawLab.com
  223. 223. FinLegalTechConference.comNovember 4, 2016
  224. 224. In sum, I believe …
  225. 225. Over the coming years, we are going to be able financialize large elements of the legal industry
  226. 226. By which I mean —- apply the tools of finance and insurance to measure / predict a wide range of procedural + substantive outcomes
  227. 227. it will help better establish the value proposition associated with a wide range of legal tasks …
  228. 228. As we move items from the ‘art’ column to the ‘science’ column …
  229. 229. There will be impacts on the industrial organization of the legal industry
  230. 230. But what remains there after will be a better industry …
  231. 231. it will help focus every individual and every organization on the places where they actually provide a return on investment (ROI)
  232. 232. Daniel Martin Katz @ computational computationallegalstudies.com lexpredict.com danielmartinkatz.com illinois tech - chicago kent college of law@

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