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Fin (Legal) Tech - Law’s Future from Finance’s Past - Professors Daniel Martin Katz + Michael J Bommarito

Fin (Legal) Tech - Law’s Future from Finance’s Past - Professors Daniel Martin Katz + Michael J Bommarito

Fin (Legal) Tech - Law’s Future from Finance’s Past - Professors Daniel Martin Katz + Michael J Bommarito

Daniel Martin Katz

February 13, 2017
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  1. daniel martin katz blog | ComputationalLegalStudies.com corp | LexPredict.com law’s

    future from finance’s past page | DanielMartinKatz.com michael j bommarito blog | ComputationalLegalStudies.com corp | LexPredict.com page | bommaritollc.com Fin(Legal)Tech edu | chicago kent college of law edu | university of michigan cscs
  2. see what the world is understand what it might become

    #arbitrage the precursor to invention
  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. litigation = characterize (predict) risk/exposure shift the expected value of

    a lawsuit help price risk / help reduce information asymmetries transactional =
  5. 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 =
  6. 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
  7. focusing every individual and every organization on the places where

    they actually provide a return on investment (ROI)
  8. law < > finance many elements in law look like

    finance did 25 - 50 years ago (on the long road from Black-Scholes to algorithmic trading)
  9. Dominant Model in Law lots of unintentional self insurance rarely

    (if ever) based upon explicit risk models
  10. Cult of one 
 (or very small # of) person(s)

    thinking drives decisions with serious financial consequences
  11. thesis statement: the financialization of the law will be an

    important vector of the next decade(s) in law
  12. The Two Major Branches in #FinTech removing socially meaningless frictions

    characterizing (pricing) increasingly 
 exotic 
 forms of risk (from financial processes)
  13. #Fin(Legal)Tech application of those ideas and technologies to a wide

    range of law related spheres including litigation, transactional work and compliance.
  14. 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
  15. lots of issues with expert systems and/or rules based A.I.

    (without data or an evolutionary dynamic)
  16. Ultimately we are trying to learn the rules / dynamics

    that underlie some class of activity
  17. rules based A.I. data driven A.I. 1980’s, 1990’s, Early 2000’s

    rules based A.I. data driven A.I. 2005 - Present < > ~
  18. iterative data < > rules A.I. State of the Art

    purely data centric augment expert forecasts w/ data
  19. is an emerging field where the tools of predictive analytics

    are finally being employed fin(legal)tech
  20. #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 #Predict Regulatory Outcomes Data Driven Lobbying, etc.
  21. 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:
  22. Case Level Prediction Justice Level Prediction 67.4% experts 58% experts

    From the 68 Included Cases for the 2002-2003 Supreme Court Term
  23. “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.”
  24. “FantasyJustice predicted the long-shot appointment of Neil Gorsuch for the

    U.S. Supreme Court! “On November 20th, less than two weeks after the election, FantasyJustice predicted the Gorsuch appointment,” wrote our colleague, Michael Bommarito on the LexPredict blog. “And except for a few brief hours on November 23rd, Gorsuch never fell from that lead.
  25. 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:
  26. Random forest is an approach to aggregate weak learners into

    collective strong learners (think of it as statistical crowd sourcing)
  27. Random Forest: Group of Decision Trees Outperforms and is more

    Robust (less likely to overfit) than a Single Decision Tree
  28. 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
  29. 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
  30. A Visual Depiction of How to build an ensemble method

    in our judicial prediction example
  31. expert forecast crowd forecast learning problem is to discover how

    to blend streams of intelligence algorithm forecast ensemble method ensemble model
  32. 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
  33. Given our ability to offer forecasts of judicial outcomes, we

    wondered if this information could inform an event driven trading strategy ?
  34. AIG to Launch Data- Driven Legal Ops Business in 2016

    https://bol.bna.com/aig-to- launch-data-driven-legal- ops-business-in-2016/
  35. #fin(legal)tech tomorrow? learn from legal ops service offering to build

    a commercial insurance product offering legal cost insurance ? other exotic insurance offerings?
  36. #fin(legal)tech In such a world, Law Firm is *not* interfacing

    with client but rather insurance company regarding fees
  37. Meet Bob Bob is about to engage in yet another

    round of markup on deal terms lawyer on a major corporate transaction
  38. 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
  39. Being able to compute the change in risk as a

    function of a change in deal terms
  40. Outside of M+A Requires Mapping of Deal Terms to actual

    substantive outcomes #legaldata #legalanalytics
  41. fin(tech) is commercial field where there have have been huge

    advances in working with unstructured data
  42. 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
  43. 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
  44. information necessary to undertake due diligence or other regulatory exercises

    is locked in an antiquated format (i.e. pdf, word, tif file)
  45. “The living will is effectively a roadmap and simulation of

    the largest possible series of transactions in a bank’s lifetime, the type of analytical exercise that is common in electronic systems design or software testing, but unprecedented in law.”
  46. Ideal RRP is a ‘War Game’ whereby a SIFI demonstrates

    it is robust to failure of various counterparties
  47. horizontal integration of legal work product in the broader corporate

    technology ecosystem represents a source of immediate value creation
  48. “Watson [and related machine learning technologies] will catalyze better organization

    of legal information and legal data, forcing organizations to better manage their current data and delivering substantial returns from this information management step alone....”
  49. 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
  50. #InternetofContracts which is a special case of the #InternetofLegalThings This

    is the which is a special case of the #InternetofThings #IOT
  51. Big 4 vs. Big Law who will get to drive

    this agenda? (i will bet on the big 4)
  52. 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
  53. Associate Professor of Law Illinois Tech - Chicago Kent Affiliated

    Faculty Stanford CodeX Center for Legal Informatics College of Law Chief Strategy Officer LexPredict
  54. Fellow Stanford CodeX Center for Legal Informatics Adjunct Professor University

    of Michigan Center for Study of Complex Systems Chief Executive Officer LexPredict