Predictive Analytics and Litigation Funding
Predictive Analytics and Litigation Funding
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​I recently took a course in funding commercial litigation.   The funding of litigation cases is as much of a finance issue as it is an ethical consideration.   This is because funding normally happens prior to the disposition of a case.   However, at the time of funding the status of the case at disposition is an unknown. Therefore, it becomes an ethical issue regarding how much to have funded, how much of that funding to spend prior to disposition in order to run the firm, and whether to use the single case method, portfolio method, or corporate method to determine funding amounts. The good news is that analytics allows a predictive platform for generating a more accurate estimate of how much the case will settle for, the cost to settle, and how much could be immediately paid back to the funding company upon disposition. Analytics can even predict an average time it will take from the initial intake of a case to the final disposition.

This last predictive inference is an easy metric but could be complicated to achieve.     What it entails is the ability to enlist good tracking software into working mode at the law firm.  It also requires that employees be well trained in how to use case tracking software.   Further, the software needs to be coded accurately with the right kind of time sequence to properly chart full case timelines.   This means being able to predict the time from intake to disposition for a case that settles prior to litigation as well as for a case that settles after reaching litigation.   Further, to track litigation cases that dispose before a court hearing verses after trial, and how many go from trial to appeals, including that timeline.  Also, the tracking of how long it takes from the time a case settles to the time that the law firm can recover expenses and cut the settlement check to the client.  Each step in the process needs an individual timeline, plus there needs to be an overall timeline that tracks the entire processing of the case; i.e. how long for this step verses how that for the entire case, what portion of the entire case active time does this step take in the process.   This will help predict the beginning to end statistic based on the case type, which will be discussed below.

The tracking software used should be able to decipher case types by area of law, typical opponent company or type, features of the individual case,  such as injury type, impact level, length of marriage, proper history, e.t.c,  and amount of assets or insurances involved.   There are many sub factors that can be tracked as a coding inference or as an extra effort during data extraction, but this blog is only spaced to suggest main points.   The individual features, focal opponents, and case type should be cross referenced with time to disposition, as well as case status at disposition to accurately predict the timeline for each case.  Accurate cross comparison will help to more accurately predict hours the case will settle and can be cross compared to the case status of pre-lit, lit, meditation, trial or appeals, etc.  These features can also be cross compared to assets/ insurance amounts accrued prior to starting a case to more accurately predict settlement amounts.  Remember this will be a full assessment of previous case type, features, timelines, and assets against previous settlement amounts.   Usually being able to produce a chart of historical case statistics after generating a confidentiality agreement will help the funding company determine the true merit of case predictions.

Once predictions are complete and accurate, there are two other steps that could increase the ability to fund - benchmarking and continuous improvement if processes.  Benchmarking entails a look at the regular time span per case type for the entire market that the law firm works in.  This could be a comparison of the immediate region or it could span to state courts, federal courts, or a cross comparison of multiple regions.   The most basic benchmarking study entails time from filling to disposition with settled pretrial, during trial, or within a higher court.   However, there are ways to drive data about individual case features, opposing party specifics, settlement amounts, and client demographics.   I have heard of comparing legal practice tactics with opposing party practices to decipher what characteristics make an opposing party more likely to settle out of court.  Also, predictions can be skewed based upon the opposing counsel statistics.   Once benchmarking displays a vulnerability, law forms can use analytics to find new practices within their organization to strengthen the weakness.  Displaying evidence of these two tasks will increase the chances of getting approved for funding, and at greater amounts.

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