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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Big Data With Purpose: How We Calculated the Fines of 1.55 Million Laws

This is a technical explanation of how we built our “PenaltyAI Search” service that combs 1.55 million world laws from 79 countries for fines. It can answer questions like “What would I pay for violating money laundering laws in Jamaica?” or “How much would a smuggler who warehouses stolen goods in China pay if they’re caught?“.

The penalties are extracted by an offline algorithm that runs on an Azure VM that does the following steps:

  1. Find laws that mention keywords associated with civil penalties (as a first pass)
  2. Convert all word numbers (like “one million”) into international number format (“1,000,000.00”)
  3. Identify the paragraphs that likely contain civil penalties based on words and numbers
  4. Merge several penalties into one, whether they related to the same “clause” (section) of a law
  5. Extract all the clauses and penalties
  6. Exclude certain classes of text that are almost never penalties but look like penalties (such as laws about gold coins and section references in laws that have to do with money)
  7. Recognize currencies in text, and combine this data with our table of national currencies, and convert penalties into USD using Yahoo! Finance rates (through the XML API call)
  8. Store the penalties and clauses in a MySQL database (RDS)

Screenshot of one of the MySQL tables for penalties

We then note in our search instance whether or not a law has penalties attached to it, so that the search instance can filter by laws that have penalties (as opposed to our regular search that includes laws that don’t have explicit fines attached to them). This process is run as a batch job offline because our 1.55 million+ laws takes several hours to process and no one would wait that long for their search results!

When a user does a search, the search is first sent to our Elasticsearch instance, and then the penalties are looked up from the MySQL database afterwards. This allows full-text search of laws to be combined with penalties, and in a way that results in much less strain on our relational database (because penalties are looked up by IDs rather than a JOIN). Storing the penalties separately allows us to reduce the amount of data in the in-memory search instance, and decouples our services (since we have other types of search like technical standards and law analytics).

The laws themselves are indexed, downloaded, converted to text, parsed, and converted to English, using our pipeline that runs on another Azure VM with RDS as the data store. We make extensive use of the Microsoft Translator API to convert foreign legislation to English (since most of the world’s laws are published in languages other than English). Our use of the service is actually listed on the “Customers” page for Microsoft Translator. We’ve written elsewhere on our blog about some of the ways we gather and process world legislation.

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Microsoft Translator Case Study

After using MS machine translation (and some Google) to translate more than 750,000 laws and regulations from 26 languages, we are featured in a new MS Translator Case Study:
https://www.microsoft.com/en-us/translator/customers.aspx#textsearch=global-regulation.

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Startup Stories: An Interview with Sean Goltz and Addison Cameron-Huff from Global-Regulation

We have been interviewed by Microsoft Channel 9 Radio

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About the Co-Founders

Addison Cameron-Huff  is an award winning developer (2013 semi-finalist for LinkedIn Hackathon, 2010 winner of PayPal’s annual X Innovate Conference Hackathon, 2010 winner of TechCrunch Disrupt NYC Hack Day, 2009 winner of Yahoo! Open Hack New York City, 2008 winner of Yahoo! Open Hack Day at Waterloo), an Ontario-licensed technology lawyer and an entrepreneur (ca, FlatLaw.ca, ParentInterview.com, SummerhillDesign.com).

Nachshon Goltz teaches at York University, has authored peer reviewed papers & book chapters in the field of technology and law Nachshon is finalizing his PhD at Osgoode Hall Law School, York University and is an Ontario and Israeli lawyer. As a lawyer, Nachshon has consulted to the Israeli court system in its computing project, a multi-million international project including companies as IBM, Microsoft and other leading technology corporations.

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