What is the Future of Finance? or is UBS a top performer?

 

 

“UBS never took enough interest in its risks”, Financial Times, 20.12.2012

 

Let’s start with the bad news – we did not win the Americas UBS Future of Finance challenge 2017. The good news is that we had the opportunity to pitch our RegTech vision and not less important, to get an inside look at UBS’s technology use (or lack of) in this field.

Our pitch was simple: you (UBS) need a regulatory compliance system (much like the one we’re currently offering for world laws – but much more advanced; a Smart system that can track, translate, map, compare and digest new regulatory change in less than an hour – globally. A learning system that will co-evolve with the bank systems and thus prevent future fines and minimize risk.

The justification was strait forward: according to a BCG recent report, the number of individual regulatory changes that banks must track on a global scale has more than tripled since 2011, to an average of 200 revisions per day. This is not a scale humans can handle efficiently. Hence it is no surprise that Banks paid $42 billion in fines in 2016 alone and $321 billion since 2008.

Technically speaking the Americas finals in which we participated were organized to the last detail. Though dietary options were not available (vegan, gluten-free etc.), the bank allocated relevant representatives to meet with each finalist and provide feedback on the pitch. For us these meeting felt like development meetings as the bank people offered great ideas to enhance our vision.

More importantly, it was an indication from a first-hand internal source that the bank (and other banks as well) is light years behind when it comes to RegTech and regulatory compliance. Given the bank spending in this field (in the billions) it is quite amazing and certainly was reassuring going to the pitching competition.

Inconveniently, while the mentoring session was held at the bank’s offices in Manhattan, the finals were held at the offices in New Jersey. This divide forced the candidates to move from one hotel to another and/or struggle with the massive transportation challenges that New York City has to offer.

With no expected diversity, the judges were all IT people. The America’s CEO Tom Natatil gave the opening speech but failed to stay for the actual competition. The judges were provided with feedback from the previous day mentors (ours was excellent) but did not provide any feedback or reasons for their choice of the winning pitch nor the 2nd and 3rd runners-up.

The winner, Authomate, pitched a mobile security system to allow the bank clients to log into the bank’s portal safely. While the technology may be new, this is by no means an innovative concept nor disruptive. Moreover, based on corporate logic, this will probably be the last technology UBS will adopt.

It is too early to say if the bank will be interested in our vision for the future. The same way that it was not clear whether the finalists were supposed to pitch a future venture that can be developed with the bank, or what they already have (Automate) to be used by the bank. Either way one thing was clear, as most big corporations, UBS structure is very fragmented and the chance to capture the attention of the relevant person is extremely challenging.

To summarize the experience, I would like to use the same citation I used at the end of my pitch: “Increasing regulation is here to stay – much like a permanent rise in sea level. In an era of rising regulatory seas, focus on management is mandatory, not optional. Top performers will use the opportunity to incorporate technical innovation” (BCG Report).

Whether UBS is a top performer is yet to be seen.

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Software That Reads Laws: PenaltyAI Search – Global Risk & Compliance Redefined

About a year ago I sat with my CTO in the Manhattan office of one of the world’s largest accounting firms. Their regulatory compliance global team was very impressed from what we’ve done so far with Global-Regulation and wanted to know what more we could do. As usual with large firms, they wanted a system that does everything – from tracking new bills to predicting the future (step 10 instead of step 3).
The ambition to create the ultimate risk and compliance system stuck with us. This ambition came into life when we realized, in one of our internal discussions about our global law search engine that penalties are the kind of information that can be identified with a high degree of certainty by an Artificially Intelligent system.

My story begins in the 2000s when I helped the Israeli court system work with IBM to digitize legal information. I’ve seen the slow evolution of legaltech and listened to the ambitious ideas of tech people. But I’ve also seen the reality of legal technology and wondered: how can we give machines the insight of lawyers?

Fast forward to 2017, after seemingly endless testing, experimenting, coding, consulting (thank you to Kyle Gorman from Google for the words to numbers converter recommendation) and hard work – we are extremely excited to present the PenaltyAI Search – the first and only AI system that identify compliance clauses in legislation on a global scale, extracts the actual penalties amount and serve it all to the user in US dollars.

Now risk and compliance professionals can search and identify risk levels across jurisdictions on a specific topic without even reading the law. Lets say that you are an IBM executive considering global expansion of your Watson services to new markets – with a click of a mouse you can now use the PenaltyAI Search feature of Global-Regulation to learn what would be the risk level of your goal.

Screenshot of PenaltyA Search for "tobacco nicotine"

Combine this with our complexity feature, suggested search ideas and related laws – and a risk & compliance team can feed Governance, Risk and Compliance (GRC) platforms with all the information needed to launch a new business line, in a matter of hours. Before, this would have taken months, require an army of translators and a division of analytics to determine risk and compliance.

We see this as a great achievement on several levels:

  1. an AI system that can really read legal text and produce useful meaning; and,
  2. enabling risk and compliance professionals to explore real and relevant data on a global scale, in English; and,
  3. allowing governments and businesses to assess and enhance their compliance efforts; and finally,
  4. for researchers to compare and contrast risk and compliance data globally.

Thank you big accounting firm for teaching us that even seemingly unsuccessful business meetings can bring great results. Thank you Microsoft Canada for your help in connecting us with the Microsoft Translator team. Thank you LegalX (now LawMade). Thank you Ken Thompson for UNIX and regular expressions. Thank you to my wife and children for your daily inspiration.

If you’d like to know more about how the system works technically, my CTO has written a blog post on building PenaltyAI Search.

Computers can now tell us about penalties for world laws.

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Graphing the World’s Laws: Visualization of 1.55 Million Laws + Our PenaltyAI Search

The graph above is the first time that penalties for non-compliance with the world’s laws has been visualized. It was made possible by the culmination of Global-Regulation Inc.’s R&D efforts over the last year to create an automated AI method for reading penalty provisions from civil laws – see the system here.

Our system (that we’re calling “PenaltyAI Search”) is now able to extract penalties from legislation (statutes and regulations) and present them in US dollars, along with the original text. This is a multi-phase process that starts with an AI based algorithm that identifies the penalty clauses. The next step is to extract the penalty amount from the penalty clause. This step includes complex linguistics mechanism that can convert amount in words into numbers like “one hundred thousand” to 100,000, and Indian English notation like “lakh” and “crore”. The next step is to convert different notation systems into a standardized decimal format (e.g. “560,99” to 560.99).The final step is converting all the world’s currency’s into USD to enable comparison on a global scale (which is done on an ongoing basis to account for currency fluctuations).

As for the graph at the top of this page, it was created by applying PenaltyAI Search to all of the laws in the Global-Regulation.com database (currently around 1.55 million laws from 79 countries) and then excluding countries with only a small number of laws available or too few penalties to make any useful statistical inferences. We’re making available the Excel file for the graph here: World Penalties – Feb 9 2017. We’ve excluded any penalties other than those within the top twenty most frequent for each country in order to eliminate outliers.If you make any use of this data please link back to this blog post and let us know by pinging us on Twitter @globeregulation.

The PenaltyAI Search system has been implemented into the Global-Regulation.com search engine and soon (within the next week) the user will be able to search, explore and drill down for a given topic, across jurisdictions or filtered by country. As usual, these features will be accompanied by our innovative visualization display.

We see this system as a ground breaking event in the field of extracting valuable information from legal text using algorthmic methods. On the theoretical level this is proof that the text of legislation can be mined for insights, and on the practical level, this is a celebratory milestone for compliance and GRC professionals that will be able to use our system to simplify their work.

Congratulations to our technical team that enabled us to go to where no legal tech product has gone before.

More updates will be available in the next edition of our newsletter and will be rolled out to subscribers shortly thereafter.

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Search Ideas – Interaction with the Search Engine

What if you could discuss your search query with the search engine? well, now you can. Our new feature suggest search ideas based on the user’s query. These search ideas are extracted from our world’s laws database itself.

Here’s how it works:
1. We take the text of every law in the world and extract the most frequently mentioned word pairs, on a per-law basis. This way we create a new database of word pairs.
2. When someone does a search we check the database of word pairs and take the word pairs that occur most frequently in association with the word or word pair that the user is searching for. So a search for “coffee” will return keyword suggestions for words that appear in laws that mention “coffee” most commonly.
3. We then filter the words and take the best matches and display those to the user. These are the search ideas.

You can click on the search ideas in yellow at the top and it will be updated according to your recent search. For example, lets say you started with Coffee –> then you choose ‘Coffee Agreement’

And then choose ‘system certificates’. This is endless.

This new feature actually enable you to interact with the search engine and follow the trail that is based on the database of word pairs we created from our gigantic database of the world’s laws.

 

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The First Law Chatbot: GRBOT

Screenshot of chatbot on iOS
We are very excited to announce the launch of the first ever legislation chatbot: GRBOT, using the Kik mobile platform. The GRBOT enables the users to type a request: e.g., “search for United States drone laws” or “Show me EU laws about organic farming”, and the GRBOT will send the most relevant laws to the users’ mobile device, with a link to see more.

You can see the bot in the Kik Bot Shop here: https://bots.kik.com/#/grbot.

The only thing needed to connect to the GRBOT is to download the Kik app from the app store and friend the GRBOT account. When you first start chatting a message is displayed that shows a few examples of how to interact with the bot.

We think we’re the first ever legislation chatbot but there have been other legal bots created, both on chat platforms and accessible through other services.

Other legal bots already around are a lawbot.info – built by Cambridge students to advise sexual assault victims and lawbot.co, designed to analyze contracts. Others include DoNotPay, created by an 18-year old British coder, that quickly handles ticket appeals through a Q&A chat; and of course ROSS (but see skeptical voices). See also Lexi – “You can chat to Lexi to generate a free Privacy Policy or Non-Disclosure Agreement”. See also Fastcase bad law bot.

Screenshot of Kik Bot Shop

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Machine Learning – Text Analytics Comparison

As part of our work engaging Artificial Intelligence and especially Machine learning into Global-Regulation‘s system, we’ve conducted a comparison between the big four providers of ML Text Analytics: Microsoft, Google, IBM and Amazon. This post is a follow up of a previous post regarding AI assisted compliance system.

MicrosoftMS ML studio allow some options of text analytics.

screen-shot-2016-11-11-at-9-50-29-am

Although not particularly helpful for the purpose of identifying segments within legislation, MS ML studio

dn781358-mccaffreymls_fig1_hiresja-jpmsdn-10 is the most friendly system among the ML tools in this comparison. It is so friendly that even a user with minimal background in programming and ML can use it (with some patience and strong will 🙂

In MS ML There is a link to new text analytics models but unfortunately it is a broken link.

GoogleTensorflow offers some text analytics features. This is not a friendly tool and the text analytics options it does offer are vague. However, the vector representation of words may be useful when analyzing legal text and training a model to identify segments within legislation. This is a different approach than the structured text analytics offered by MS and IBM – see below.

screen-shot-2016-11-11-at-10-08-39-am

In the context of a previous post about AI assisted compliance system, Tensorflow vector representation may be the solution for the first part of the challenge, i.e., manually identifying compliance clauses and training the model with these clauses. Nonetheless, new challenges arises in the implementation stage since the system will be able to identify laws that includes compliance clauses but not the specific clauses within the law.

Overcoming this challenge will require an additional stage in which the laws may be broken into chunks of text before running the model to identify the clauses. As laws are not always (and usually not) machine friendly, this process creates its own challenges.

IBM – Now offered through AlchemyLanguage, IBM now have one text analytics feature analyzing entities and relevance. Before migrating the text analytics features in July 2016, IBM offered few options of text analytics that are not available now.screen-shot-2016-11-11-at-10-20-11-am

This system analyze factor as ‘Fear’, ‘Anger’ and ‘Joy’ – not exactly what one would need to analyze legal text. In addition, IBM’s costumer service does not really work. Attempts to get access to their system failed even after stubborn emails.

Finally, it should be mentioned that Amazon’s ML platform  does not provide any text analytics options.

Conclusion

One would expect that the first step in analyzing legal text would be to use ML text analytics options. This seems like the short way towards identifying segments within legislation and the best way to ride the advancements in this field. However, upon testing these ML text analytics abilities, it becomes clear that this is not the answer and that in their present state of development, ML text analytics is not capable of doing much serious work, rather than classifying text as ‘Joy’ or ‘Anger’.

The more ‘simplified’ approach taken by Tensorflow vector representation is much more relevant for the purpose of analyzing legal text and identifying segments in big data even though it is far from the ‘Watson Dream’ where you ‘work with Watson’ and get your text analyzed with the click of the mouse.

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Upgrade to MySQL Cluster

We upgraded our MySQL-based law database system a couple weeks ago. We’re now running a cluster with a writer and reader so that the failure of one server doesn’t result in downtime for our users. This new cluster-based system is approximately 15x faster than the old one due to a much higher amount of RAM (necessary to accommodate our rapidly growing index of laws and translations). Although we’ve updated many other parts of the system, we had been running on the same HDD-based MySQL system since October of last year. Our new system is SSD-based and has far higher throughput.

Users will primarily see the difference in how much quicker the “Related Laws” feature is. We’ve measured the performance at about 15x the previous MySQL server system. Thanks to our cloud-based infrastructure and standardized components it was quite easy to make the transition. Within about two days we went from experimenting to a full transition (and no reported downtime from users).

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Designing an AI assisted Compliance System – A Theoretical Framework

This post offers a theoretical framework for the design of AI assisted compliance system that would be able to identify compliance clauses and fines within legislation, thus enabling enhanced compliance by identifying the regulatory risk, especially to the corporation, according to the risk magnitude and its consequences for the corporation.

AI is the theory and development of computer systems able to perform tasks that normally require human intelligence. Examples include tasks such as visual perception, speech recognition, decision making under uncertainty, learning, and translation between languages.

There are two aspects of Artificial Intelligence (AI) that are relevant to the legal field: the use of AI to benefit the legal profession and the second is the implications of AI on our lives and the role of law in this respect.

Venture capital investments in companies developing and commercializing AI-related products and technology exceeded $2 billion since 2011. Leading players like IBM, Google, and Facebook have invested heavily in developing their AI capabilities.

No doubt the alleged AI legal application that has received the most public attention is ROSS, a system supported by IBM’s Watson division. One of the co-founders of the Ross team describes it as “[b]asically, what we built is a [sic] the best legal researcher available“. Even without being made available to the public nor presented in  any public demo, ROSS has become a symbol of legal AI technology.

Another new application is Global-Regulation.com, the world’s largest search engine of legislation. Global-Regulation.com makes extensive use of both Microsoft and Google’s machine translation to offer laws from China, Mexico and Spain, among many others, in English.

Other fields in which AI has been used within the legal profession are e-discovery (Recommind, Equivio – now part of Microsoft), forecasting outcomes of IP litigation (Lex Machina), providing fact and context-specific answers to legal, compliance, and policy questions (Neota Logic) and contract lifecycle software, including discovery, analysis, and due diligence (Kira Systems and KM Standards).

It should be mentioned that while many companies are claiming to use AI in their products, not all of them actually use it or have it at the core of their product. A good indication of whether AI is actually used (albeit hard to determine as the backend is not usually transparent) is whether there is training of a model embedded in the system.

On the other hand, fear of AI’s implications has grown respectively. Oxford University researchers estimate that 47 percent of total US employment is “at risk” due to automation of cognitive tasks; Silicon Valley entrepreneur Elon Musk invested in AI “to keep an eye” on it, claiming it is potentially “more dangerous than nukes;” and the renowned theoretical physicist Stephen Hawking has said that AI may create “machines whose intelligence exceeds ours by more than ours exceeds that of snails”.

In 1939 after demonstrating a nuclear chain reaction, Leo Szilard, one of the leading scientists developing the experiment wrote: “We switched everything off and went home. That night, there was very little doubt in my mind that the world was headed for grief”. Stuart Russell, a leading AI expert argues that we may be in a similar situation with AI: “To those who say, well, we may never get to human-level or superintelligent AI, I would reply: It’s like driving straight toward a cliff and saying, “Let’s hope I run out of gas soon!”. One major concern about AI is the option that advanced AI system will use their superintelligence to design and build even more advanced AI systems without any human intervention or supervision. A form of this has been taking place for years in the world of CPU development. Computer chips are so complicated that computers are required to design them. The movie Ex Machina offers an illustration of the alarming consequences that could be in store for humanity.

Stephen Hawking wrote that, in the short term, A.I.’s impact depends on who controls it; in the long term, it depends on whether it can be controlled at all. Two examples illustrating Hawking’s concern are autonomous killing machines currently being developed by more than 50 nations. Equally ethically complex are the advanced data-mining tools now in use by the U.S. National Security Agency (and their counterparts around the world).

Given these concerns, Omohundro argues that intelligent systems will need to be carefully designed to prevent them from behaving in harmful ways. He identifies a number of “drives” that will appear in sufficiently advanced AI systems of any design. Similar precautions are suggested in a paper co-authored by researchers from Google, Stanford Uni., Berkely Uni., and open AI, presenting a list of five practical research problems related to accident risk.

Since the global financial crisis of 2008, spending on compliance has break new records. Estimates report over $1 US trillion is spent worldwide on regulatory compliance, and over 1 million people employed around the world doing regulatory compliance.

KPMG, one of the four big global accounting companies claim that, “The top risk perceived by senior executives is the growing regulatory pressure from governments around the world. C-level executives in almost all industries say this, not just those in Financial Services, where companies are facing arguably the greatest regulatory challenge in their history.”

The bottom line, in the words of one senior executive we’ve spoken to is that “every $1 spent on compliance, saves $5 in fines.”

AI Assisted Compliance System

The capability of AI is yet to be utilized for the enhancement of legal compliance. This suggested system will identify fines within legislation by training a model to identify fines and charges within laws and create a compliance system that will be able to identify the greater risk to the corporation.

In the first stage, legislation from 8 countries will be considered: UK, Canada, USA, China, Indonesia, Australia, New Zealand and Uruguay. For each country legislation database an initial Search string: (corporation or company) and (compliance or fine or charge or penalty) will be employed. We’ll be using our database of laws, including the translations for Indonesia, China and Uruguay.

This manual process will identify 100 samples of fines in legislation and 100 samples where number digits in legislation are not fines (e.g., interest, budgets etc.). The next step will engage machine learning algorithm to ‘translate’ the positive and negative samples into numerical values that will be used towards automating the system.

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Related Laws Feature

We’re pleased to announce that our “Related Laws” feature is now generally available (and fast). When users click the “Related Laws & More Info” button next to each search result there will automatically be a list of related laws generated and shown (where applicable – short laws don’t have this feature because the results aren’t useful).

This feature was previously available and marked as “experimental”. With recent upgrades to our database system we’ve improved the speed of this feature by 15x and can make it widely available to everyone (including users who are not paid subscribers). This is one part of our strategy for making the search experience faster and more useful.

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Recently Added Countries

We’ve added a few new countries:

  1. Uruguay
  2. Moldova
  3. Turkmenistan
  4. Norway
  5. Greenland
  6. Madagascar
  7. Malaysia
  8. Greece
  9. Guyana

Photo by John Seb Barber.

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