Yves Faguy: You’re listening to Modern Law presented by the Canadian Bar Association’s National Magazine.

Hi. I’m Yves Faguy. In this episode of Modern Law we discuss how technology could reshape litigation. New technology has slowly been making inroads into law for years, from contract review software to e-discovery tools. But with firms inclined to embrace technology more fully these days, they are beginning to explore ways of using historic data, either data that is in the public record or their own internal data, to predict the outcomes of cases or offer more clarity on the costs associated with litigating a case, for example.

As the ability to assess the potential success of litigation gets more sophisticated, the litigation funding market is one that will be well placed to benefit from all these new tools. To help us understand some of what’s going on, we have on our show today Amanda Chaboryk. She is the disputes and litigation data lead at Norton Rose Fulbright’s London office, and she is part of NRF Transform, the firm’s global change and innovation program.

A large part of her job focuses on strategic projects and honing legal technology to improve the delivery of legal services, particularly on complex legal projects. She also has experience in project management, litigation finance and data science. Also, she’ll be working with King’s College London on a course that she’ll be giving titled legal practice technology, and she’ll be discussing this with us in a little bit more detail. Welcome to the show, Amanda.

Amanda Chaboryk: Thank you so much for having me.

Yves Faguy: So, listen, you seem to have carved out a very interesting role for yourself in law. Tell us a little bit about yourself, how you got interested in data and litigation finance.

Amanda Chaboryk: No; of course. For starters, as a Canadian it’s a pleasure to participate in the Modern Law podcast. Originally from Toronto I did my undergrad at U of T and I always wanted to study in Europe, so I completed my law degree at the University of Exeter. And I’ve worked at a series of law firms and a litigation fund in quite a few different roles in legal. I’ve always had a passion for both the law and data, so I feel privileged I have a role that involves both disciplines. They’re quite different areas, so it’s very interesting establishing connections between them both.

So ironically my first job after studying at U of T was at a Canadian market research and polling firm, and that’s pretty much where the data interest sparked, as well as studying a statistics module. This basically helped me not only be privy to massive datasets but see the relationships between how organizations use data and how data brings infinite possibilities to businesses.

In terms of litigation finance, after studying law at Exeter, I worked at a litigation fund in London where I learned a lot about the industry, as well as the importance of data and informing investment decisions. So meaning when a funder decides to invest in a case, and now at present I’m focused on developing and supporting our disputes group use of analytics, litigation finance and legal technology.

Yves Faguy: That sounds like a fascinating job. Perhaps you could give us a bit of a sense of, you know, where we’re at. I mean, you’re at Norton Rose Fulbright. But give us a sense of the lay of the land in the legal industry. Where are we at with actually data analytics and where are we at with litigation funding and where do the two come together?

Amanda Chaboryk: No; yes, of course. I was going to say litigation finance, it’s changed a lot and it’s become more widestream in the past few years. At first I like to start by saying that I personally think it’s young but not that young, precisely. So quite a few litigation funds were actually founded around the 2008 financial crisis. Different jurisdictions, say Canada and England, also have different histories, which I’ll touch on in a moment. But to start what largely stood in the way of funding being permissible are the Doctrines of Maintenance and Champerty.

So these were actually first introduced in Medieval England with the objective of preventing abuses of justice. So it’s interesting to contrast that now to the widely socialized concept of litigation funding providing access to justice.

So, briefly returning to maintenance and champerty. The first pertains to an unconnected third-party assisting to help maintain litigation throughout – so by financial assistance. And the other, champerty, is a more serious form of maintenance and takes place when the maintaining party pays some or all the costs in return for a share of the proceeds.

So an important court of appeal decision in the UK, just over five years old, was Excalibur Ventures LLC versus Texas Keystone. So, this is where the court stated that litigation funding is an accepted and judicially sanctioned activity perceived to be in the public interests. And then the Jackson reforms in the UK also had a major impact on funding, addressing funding and civil litigation procedures and costs as a whole.

Yves Faguy: What are the Jackson reforms?

Amanda Chaboryk: OK. So, they are reforms compiled looking at civil litigation procedure and costs. And what they also did is they made all these high-level evaluations and ways to not just improve access to justice but reforms and reforms to civil procedure in the UK.

Yves Faguy: OK.

Amanda Chaboryk: So a key point of this was the recoverability of success fees. So CFAs and DBAs, which I’ll touch on in a moment, but in general the last decade and beyond has seen a lot of changes in the law and increasingly innovative forms of alternative pricing arrangements and insurance products. So in the UK, for example, success fee arrangements include conditional fee arrangements and damage-based agreements, which are of course subject to certain conditions. And just as a whole the insurance market has very much also developed with some insurers offering dispute resolution, and after-the-event insurance solution for both litigation and arbitration.

Yves Faguy: OK.

Amanda Chaboryk: I would say that the increase of risk transfer insurance and the sophistication of the market has just been huge for litigation funding and insuring cases as a whole.

Yves Faguy: And why do you think – so what prompted this? You said a lot of this came after 2008, and you think a lot of this was brought out or accelerated by the financial crisis?

Amanda Chaboryk: Yeah. I would say, like, increasing amounts of litigation and organizations having shrinking litigation spend – having to do more with less. In terms of the insurance market, for example, a lot of insurers are now offering dispute resolution and ATE – so after-the-event – insurance solutions for both litigation and arbitration. Examples of this include security of costs, which helps to address security for cost order. So this is an order that requires a party, often the claimant, to pay money into the court to serve as security for the opponent’s cost of litigation.

Another one is contingency fee, insurance – so CFA insurance, insurance for arbitration, proceedings award default insurance, after-the-event insurance. So, this is the insurance liability for the opponent’s cost following an adverse cost order.

So in Canada, actually, the courts can also similarly exercise their discretion to allow a claimant or a funder to provide security for costs. So ATE insurance is also permitted in Canada, which also follows an increase in the legal expense market.

Yves Faguy: And so how are the markets different, you know, in all of this between what you’re seeing in the UK and what you see back home here in Canada?

Amanda Chaboryk: So one I think what’s quite important to mention is in the UK it’s a loser pay system, for lack of better words. So it’s beneficial for both the claimant and the funder to have an ATE policy that provides coverage for adverse cost risks. So some funders will also fund these costs as premiums can be quite high. I would say, though, that the UK, in terms of sophistication, there’s a larger volume of funders in the UK but a lot of funders, for example, have expanded internationally and some will have offices in Toronto or different jurisdictions.

Yves Faguy: OK. Yeah.

Amanda Chaboryk: In terms of where the market is now, in the UK the market has rapidly grown and the courts have progressively accepted third-party funding arrangements, particularly, as I mentioned, following the endorsement of the Lord Justice Jackson and his thorough review of civil litigation costs. There’s quite a few active funders in the UK now funding cases globally, including international arbitration cases.

In terms of growth markets for funding, jurisdictions that have leading reputations for international arbitration, I’d say they’re likely to experience growth. And a lot of firms have also notably – sorry; law firms – joined forces with funders. So over the past few years you can see in the legal press there’ve been quite a few announcements of strategic partnerships with funders and law firms. I’d say the advantage of these relationships is, while having a funding facility and being able to go to clients and inform clients of the funding facility, as well as the process and expediting the process for due diligence, sometimes applications will be done on a single-case basis. But if there’s a strategic relationship this can surely be expedited.

Yves Faguy: What kind of litigation are we seeing being funded, from where you’re standing? What are the types of cases that we’re seeing? This is where I’m asking. Like, how does the market break down?

Amanda Chaboryk: In a general basis, the main cases that are funded, I’d say, are high court litigation and arbitration. So when since funding is non-recourse, so if the case isn’t successful the funder loses their investments, funders want cases with strong prospects of success that have either assets that can be immediately liquidized or strong monetary damages.

So on a general basis if I had to, I’d say, divide the case types into the most common, I would say meritorious claims for damages, whether through court or contract. Some will be brought by single claimant; some will be brought through group actions.

The scope is very large, so I’ve even seen now some funders funding defamation, divorce and personal injury claims. Years ago I would have honestly never thought that this is possible. But in terms of returning to the general categories and those that are most common, I’d say commercial litigation, competition claims, international arbitration, insolvency and group and class actions – especially in Australia, class actions and group actions are very popular.

Yves Faguy: You know, so obviously you mentioned how, you know, they are, you know, the funders are looking at success outcomes, or hopefully they’re banking on success outcomes. So how do we measure – you know, how challenging is it for them to measure the odds of success?

Amanda Chaboryk: Yeah. So I would say this is definitely one of the most complex aspects, and some funders will instruct, for example, QCs, barristers or senior lawyers, which they often form the kind of investment committees or the due diligence to look thoroughly into the claim. This is why when – or, like, claimants or law firms or applications for funding and insurance are being made, quite a few data points are collected and a lot of information is asked about the claims such as what’s the claim type, what’s it about, how does it arise. When will limitation become barred? Are there any limitation issues, as well as key dates such as a letter of claim, the date of the letter of response? So that’s to do with the timeline. Because as you can imagine, the longer litigation proceeds the more funding will be needed.

Other questions such as a security of cost application envisaged. What needs to be established to succeed on both quantum and liability for the different parts of the claim? Details of any parallel actions. This is just to name a few. So funders and insurers will very carefully review the strength of the claim and of course request evidentiary support to predict the chances of the claim being successful. So in general a funder’s investment in claims that are seeking a monetary outcome from a defendant, and they want a defendant that has the ability to pay.

So that’s why on some application forms some funders or insurers will ask for the number of opponents and their legal status. As mentioned, some will ask for the council’s opinion depending on the stage of litigation to determine the prospects of success on quantum and merits. If the claim is at a very early stage, some funders will even fund for the council’s opinion to be obtained.

So, as a whole, the extent of the funder’s review will very much depend on the specific case type and its status.

Another very important part is the litigation budget. So, this involves what, like, somewhat of a fixed commitment of capital in order to pay for the fees and associated costs of litigation. So this is the part where I think the data element comes in and is really important. So in order to produce a litigation budget, you need to, or a solicitor or whoever is making the application has to, provide somewhat of a draft of the different how much is it going to cost for witness statements, experts, pre-action in the different stages. And this can be – especially when this involves how much funding is going to be deployed, this amount is very important. But it’s quite hard to determine how – like, a key question is how long how much is it going to cost to take my case to trial? Are there going to be interim applications? A lot of this is unknown until a case materializes.

So I think it’s really important, for example, for organizations to carefully look back and, for example, track information such as historical costs. From a data perspective, litigation funds, they will most likely have really unique data on timings as well as the costs based on the litigation budgets they’ve received compared to the actual costs accrued for the different litigation stages.

Yves Faguy: Do you mean that they’re using their own internal data of, like, you know, past projects, past cases, or –

Amanda Chaboryk: I’m not sure if funders use their historical data, but on a general basis a lot of funders have very useful data on the claims that they invest in, and also the claims that are brought to them that they might not invest in, because they’ll – and they’ll likely have structured datasets, for example, on, for example, what claims do they have the most applications for in, say, X year. Let’s say 2018, for example. How many arbitration claims did they fund and where are these arbitration claims seated?

Yves Faguy: These are some of the data points that they’re looking at. Are they beginning to rely on more sophisticated levels of data analytics to look at this? Are law firms beginning to look at data analytics to look at these data points? Are we beginning to see the incorporation of, you know, machine learning? We talk about artificial intelligence, sometimes a little bit loosely. Where are we at at incorporating some of this technology into the analysis of data of these of these possible files?

Amanda Chaboryk: Yeah. I would say litigation analytics or dispute analytics is a new field in its own right. In terms of litigation funds, though, I don’t believe AI has widely yet been honed but I think it is slowly and surely being honed. Some vendors have structured data that courts and tribunals have made public. A really good example is Lex Machina. So it’s a legal analytics company owned by LexisNexis. So in terms of who uses Lex Machina and how it works, so with the data available from Lex Machina, it uses a series of different technologies – so natural language processing and machine learning – that are obtained from court documents. So it basically, in a sense, scrapes the data from federal US docket and entries using natural language processing and machine learning.

So it’s a big data solution that basically harvests large amounts of legal data from thousands and thousands of documents and some law firms or lawyers can use it to predict the behaviours and outcomes that different legal strategies might produce. Some of them might use it for pitching and to land new clients or to win new work. On the Lex Machina you can type in entities and you can see, well, there are – you can see their existing litigation. You also have insight into the timings.

In the UK, for example, there’s a solution called Solomonic, which is a litigation analytics platform that is somewhat of a similar solution. So every month, for example, hundreds of new claims are listed and there’s hundreds of active firms in the courts. It’s of course really hard to keep track or to know when a court has published a claim against a client, so what Solomonic does is they can they sift through all the claims and the court activity on a daily basis, so in order to help whether it’s law firms or individuals keep track of the latest claims and also keep an eye on recent decisions.

One limitation, though, however, in terms of the data available, is that settlements are confidential and a very large amount of claims do settle. So, this is an area where it would be, for example, hard, because settlements are confidential to have an overview of all the market activity.

Yves Faguy: Well, yeah, and that’s why I was asking if they’re – if, you know, some of these funders are looking at their own internal data because they know when there’s a settlement but we don’t necessarily know that if you’re scraping the entire court system.

Amanda Chaboryk: Yeah. So I would say funders and insurers have very interesting points that probably that they likely use to inform their investments.

So, just immediate examples that come to mind are claim type, action date such as when the, like – if, for example, if I was a litigation fund, I would create a structured dataset of different actions. I’d have a live data model, for example, of the percentage of claims that are actually – that are invested in versus the queries that come in, organized by claim types such as arbitration. I would also assemble a timeline such as when the claim became statute bars. The key dates like when the document was served. When the claim form was sealed, for example.

I would make not just a timeline but I would also have an analysis between what was the amount originally on the claim form. So the origin – the damages envisaged versus what was actually paid out; I think that would be a really interesting data point. So that would be, in a sense, stress-testing the economic scene. This is the amount that was envisaged at X time. This is what was actually paid out.

Yves Faguy: And so what are some of the other barriers that they’re having in terms of, you know, in terms of – you know, again, measuring the risks involved in investing in a litigation piece? What kind of data barriers do they face?

Amanda Chaboryk: So in terms of data barriers, so let’s say in determining causation, remoteness or contributory negligence or even a counter claim, some – or let’s say liability. So it’s quite – it can be quite difficult to determine the likelihood of succeeding in establishing liability on the different heads of the claim, as well as the quantum. I’d say the quantum bit is quite tricky because it determines how much a fund is going to invest in the litigation, and sometimes you can – a litigation funding agreement could stipulate the amount of funding whether on a monthly basis or however the arrangement provides for. But it can be quite tricky to predict the cost of litigation, and since a lot of litigation actions are quite unique, even creating structured datasets to, for example, compare apples for oranges can be quite challenging, and every organization has very different data practices. 

Yves Faguy: Yeah. So, yeah. So agreeing on the datasets has always been a challenge, and it seems to be a particular challenge in the legal industry in particular. What about, you know, what about the use of data analytics and, you know, maybe machine learning techniques in litigation in general? How much would you say that’s penetrated into the legal industry? On the litigation side in particular. And it could be at the law firm, inside law firms, or, you know, at litigation funders. 

Amanda Chaboryk: Yeah. So I would say what I’ve noticed over the past few years is some law firms having advertising data analytics capabilities and, for example, our organization has data scientists who do incredible work using different technologies who are well skilled in coding and creating different solutions and client products. I’d say all law firms or all organizations have different – are in different journeys with their data and that data has become, I’d say, more trendy over the next few years. But in terms of litigation, so e-discovery, so TAR 1, TAR 2 and the different technologies used for assisted reviews have massively developed over the past few years. 

And eDiscovery products – so let’s say – what’s a common example? There’s quite a few. There’s Recommind, Relativity, and OpenText. Some of the platforms now are leveraging the technology used in e-discovery to have other capabilities, for example, such as analytics using the same technology. But what I think is very important to mention is that all these tools such as machine learning and artificial intelligence, a lot of these tools can only be deployed if there’s large, structured and clean datasets. And for an organization to have structured datasets and clean data, there needs to be individuals responsible for those datasets. And the data needs to be cleaned; it needs to be accessible.

So I’d like to I don’t want to say argue but say that a lot of these amazing tools and solutions are only available if the data is available as well as if investment is put into training the data models as well as, for example, programming logic and basically providing what the tools need in order in order to work effectively.

Yves Faguy: Maybe – I hope I’m clear with this question but, like, where does that – you know, where does that effort have to come from? Does it come from the law firms, for example? Or does it come from the litigation funders? Because, I mean, obviously the client’s data is involved as well. 

So, yeah. How difficult is it for law firms to take on the challenge of cleaning data? Because, you know, data’s coming in from all sides, obviously. You know? Is it even conceivable that they can manage possibility of having a clean dataset, eventually?

Amanda Chaboryk: So on the topic on what is law firm data, one of the key challenges that law firms and all legal professionals have to face is navigating ever-increasing volumes of data at massively growing rates. So a lot of roles in law firms, for example, involve seeking and synthesizing applicable and pertinent information quite quickly. So it’s also of note the issue of time reporting – that if, for example, if people are on the clock you have to be very careful how much time you spend doing research. And you want to demonstrate the value of research and how you’re optimally using resources. So, this presents a bit of a challenge and that – while clients want very cost-effective and efficient services, but the pool of information is expanding.

And even though there’s quite a few technology solutions available, a key – but that needs to be addressed is first understanding the relationship between data and information and structured and unstructured data. So this talks about how conceivable it is for organizations to organize and just to make use of their data.

So I would say that while every organization is on a different journey, it’s very important to have data stewards or individuals assigned to discrete groups of data as well as having an understanding of relational databases and, for example, which pieces of an internal system link together, where the data is going, as well as the importance of having live data models. So not just, for example, reactively producing a report but having a live data model. 

So, for example, let’s say I’m a food vendor. Instead of, for example, researching the amount of items I sold, having a live data model that says this is the amount sold in this location, these are the top 10 most purchased products. But I think it’s really hard for organizations that have so much data to have live data models and to do reporting that is proactive rather than reactive.

Yves Faguy: And so you’re – I mean, presumably you’re trying to instill this culture at your own firm. But, you know, speaking about the legal industry in general, are law firms and litigation funders, are they actually beginning to buy into this idea that they need to hire and nurture these data stewards? And who are the data stewards anyway?

Amanda Chaboryk: In terms of the data stewards, so I’ve noticed that over the past few years sometimes law firms will hire consultants or people with different skillsets as well as data skillsets. Especially – if you think about kind of the sources that – so the different kind of sources of data that law firms have. They’ll have precedent documents; they’ll have reviewed so many legal decisions. Well, this just – I think what a really good example that immediately comes to mind is looking at a judgment or a decision, think of all the data points that you can get. You can have the causes of action, you can have all the cases that are cited, you can have the trial date, the length of trial, the parties on both sides, the decision, what kind of industry. 

So there’s a lot of really interesting publicly available data, and that’s why these tools like Lex Machina and Solomonic have been transformational and finally being able to capture this data and provide analytics functionalities.

As I mentioned, it’s a whole – the whole other element to existing, the whole other element of having an overview of the market as a whole, is that a lot of settlements or all settlements are confidential. 

Yves Faguy: Yeah. So how do you see the whole industry evolving generally over the next few years? How do you see, you know, if you were to guess a little bit, how quickly will we be folding in machine learning analysis into predicting outcomes and litigation? How vastly deployed do you think it will be throughout the legal industry using these techniques? How necessary will it be for law firms and litigation funders to, I guess, embrace this kind of technology?

Amanda Chaboryk: Yeah. So a lot of what’s happening in the legal space related to artificial intelligence has been connected to machine learning. And there’s also quite a lot of ambiguity about these terms. So in order to kind of best explain the nuances it’s helpful to view artificial intelligence as a far-reaching category where machines carry out, let’s say, intelligent tasks that are commonly associated with [just not? 00:29:09] human decision-making. And then machine learning is, I’d say, a sub-category or a division of AI which involves a powerful application of AI technology, basically where machines are exposed to large volumes of data. For example, they become progressively more intelligent, done through continuous training.

I’d say another example, just some examples of legal tools are Kira and Clocktimizer. So, Clocktimizer uses natural language processing; it can read through narratives and categorize them. On the topic of AI again, there are quite a few other applications in the field. I guess the key examples are document automation, as I mentioned legal analytics, prediction technology, due diligence, electric billing. Going back to the example of Kira, often which is sometimes deployed for due diligence, it basically has a functionality of performing more accurate due diligence contract review through searching, highlighting and extracting the relevant content needed for analysis. 

Yves Faguy: Essentially, I mean, so we’ve seen a lot of the deployment of, you know, machine learning technology, particularly in the contractual field, I’d say. Is that fair enough to say?

Amanda Chaboryk: Yes. Yes. Definitely. 

Yves Faguy: So on the litigation side, I mean, how – I mean, where do you see it going? I mean, are litigation funders and law firms going to be investing in this more heavily in the coming years? Do they see value in trying to measure their risks a little bit more, with a little bit more sophistication? And, you know, what is it going to take for them to kind of get to the next level in terms of measuring, again, the risks and possible outcomes of the files that they choose to back?

Amanda Chaboryk: So I would say that I can see funders or insurers building their own data models based on historical investments in order to forecast outcomes. Let’s say – I think a good example would be, for an example, an insurer. Let’s say an insurer has provided travel insurance for thousands and thousands of claims. It would naturally have data on the amount of times it pays out, as well as individual factors that go into informing the coverage levels.

Similarly I would say that funds have, or in the future will start, making use of data analytics or hiring data scientists to do a deep dive into their data, especially looking at the economics and the financials behind cases. And then going back to the legal analytics tools available such as Lex Machina and Solomonic, a really interesting point that this covers is there’s often a gap between issue and serving. 

So, for example, the use of Solomonic, you can be alerted in advance. So basically you can tell a client that they’re being served before they’re they’ve actually been served. It can also build a richer picture of what’s going on with the claim, especially as a lot of claims settle. 

Other benefits to legal litigation analytics technologies, for example, is real-time tracking, data and documents. So some of these technologies now do real-time tracking. They collect information from cause lists. Westlaw and Valley, they have the functionality to extract [deep data type? 00:32:46] claims. So deploying data cleansing, collecting different data points, making use of statistical analysis, and then putting all these elements onto a web platform and providing links and document requests for court documents, links to Westlaw, for example. So quite unique datasets. So, like, the datasets around claims, datasets around judgments. For example, it would take ages to read through hundreds of judgments related to a particular action, but now with some web scraping and the technologies, this is finally feasible.

Yves Faguy: What are you going to be teaching at King’s College?

Amanda Chaboryk: So, with the new master’s program that’s taking place, so I’m part of – I’m a contributor to the Professional Law Institute. And so the PLI leads the law school strategy on the development of new professional education courses and new programs. Sorry – the new master’s program. And it complements the law school’s undergraduate and graduate level through the creation of professional education programs, which is designed to prepare students for the demands of modern legal practice.

So I’m going to be helping with the development of a legal practice technology module. So intended to equip students with relevant knowledge and encourage an innovative growth mindset around the use of relevant technology and law.

Yves Faguy: Well, I mean, I do want to talk about law schools a little bit because we’re, you know, can I ask, you know, what is it that law schools need to be thinking about in terms of – and I’m talking about generally and you went to law school, I went to law school. I certainly didn’t get a data analytics course when I was there. You know what is it that they need to be thinking about when they’re preparing students for this kind of practice where, you know, perhaps we will be using, five, ten years down the line, you know, information technology, data analytics, whatever other techniques there are, to analyze what are just like these massive flows of information? How should law schools generally be preparing for this?

Amanda Chaboryk: Yeah. I would say that law schools – in law school there’s so much focus on academics, memorization and problem questions, which I think is very important. So you can spend so much time reading judgments and learning decisions and memorizing the facts, but it’s all about application. But I would say a very, very important element and what I’d say has very much changed is the larger focus on technology, especially in clients being aware of the tools at our disposal, and as well as the resources. I would say that law schools need to need to provide their students with – I don’t want to say a knowledge of computer science but almost a foundational knowledge of data science and the importance of, even at statistics at a very basic level, understanding the importance of structured, unstructured data, the value that can be obtained from publicly available sources of data, which is often realized when doing essays, for example, and having to go through large – having to look at related cases or cases that – or other similar cases that support a legal argument, and the ability to mine through large amounts of data and immediately look at large amounts of information and understand what’s information, what’s data. 

And inactionable insights. I’d say that’s quite important saying, OK, what are the important takeaway levels of this judgment? It’s so easy to provide so many paragraphs and key points, but being able to disseminate information and also use the tools available. Some tools available or legal research provide high level overviews of cases. So, this can vary – or abstracts of cases, or legislation. This can provide tons of time if someone’s working on a substantial research paper, understanding not just data literacy but understanding the importance of costs. 

And how is a piece of legal work priced? What are the different fee arrangements? Having an appreciation for time recording systems and just the psychology behind time recording. And having to jump between multiple cases and being able to accurately account for your time and having good practices with closing your time at the end of the day. 

I would also teach about new technologies. I’d inform students about Blockchain, the importance of GDPR or data protection, and how data protection in so many jurisdictions has become more sophisticated over the past few years.

I’m sure there’s – you can do even a master’s in data protection now. But I remember in law school, I don’t recall data protection, for example, or being part of the core curriculum; it would be, like, an optional module, but I would personally make it one of the – especially with cybersecurity being such a major concern. I would – if I can go back I would have picked a data protection module, which is often an IP module but I would have made a knowledge of the relevant data protection acts in that jurisdiction as well as a knowledge of cyber risks, I would have incorporated that into the curriculum. 

Yves Faguy: Yeah. I mean, these are – it’s funny because these are actually all topics that we discuss on the show and, you know, from everything from data protection to, you know, the impact that Blockchain is going to going to have on the legal practice and all that. And it’s sort of astounding that, you know, in a lot of law school settings, certainly I guess Blockchain wasn’t around when I was going to law school. But, you know, in a lot of law school settings, you know, we don’t have these very key conversations. And, you know, just, you know, timekeeping management sounds like a pretty core skillset for the practice of law.

Amanda, I want to thank you. This is very helpful to get some insight into the use of data analytics in the practice of law and, you know, where the intersections lie with third-party litigation funding and other developments on the litigation side. So, this is a pretty exciting area. It sounds like you have a very exciting job, a very interesting job, and you have a great front-row view of all these issues. So I want to thank you for taking the time to speak with us.

Amanda Chaboryk: Thank you for having me.

Yves Faguy: We’ve been speaking with Amanda Chaboryk of Norton Rose Fulbright in London. Thank you very much. 

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