DealTech Part 2 – Where Can DealTech Work in Investment Banking
In the previous article, I explained my view of DealTech and how I believe it can automate a large part of investment banking. It seems that investment banks are catching onto this trend. Just recently Forbes published an article on “The (Data) Science Of The Deal: How AI Will Transform Commercial Real Estate“. In this article I will break down DealTech into its various parts and where I see the opportunity for DealTech to come to the fore.
What is a Deal Anyway?
When looking at an investment banking deal, one can roughly break it down into the following components (not a comprehensive list):
- Financial Modelling
- Deal Structuring
- Legal Documentation and Drafting of Contracts
- Negotiation and Client Management
- Financial Management / Agency / Deal Specialists
Let’s delve into each one to see where the opportunities do and do not exist.
I won’t go into too much detail on Financial Modelling here – I have already explored the automation of financial modelling in depth in my article on whether data science will disrupt financial modelling. Suffice to say – there is more than enough room to automate financial modelling.
This is really the area I’d like to focus on in this blog post. Deal structuring has a lot of room for both optimisation, and automated financial engineering. This is the area that investment bankers like to hold onto as this is where they add significant value – however, I believe there is room to somewhat/largely (depending on the deal type) automate this element of the deal-making process. This may be very light touch for highly complex and specialised deals, or heavy touch for the run of the mill deals, those that may benefit from the insight of previous deals that were banked.
Certainly, room exists for optimising shareholder returns using DealTech by modelling an exponential amount of deal structures and variations. This type of Monte-Carlo simulation would take into account tax and depreciation regulations, covenants imposed by the bank to ensure the deal structure does not breach anything imposed by the bank, and even shareholder limitations. I believe the financial engineers of the future will indeed be part-time data scientists! A financial engineer and investment banker with data science expertise may be able to eke out a few more basis points for all parties involved – how much could these be worth in the large deals investment banks focus on?
“Law firms that do not collect and analyze the right data will leave a great deal on the table“ – LexPredict
I often muse that lawyers are the secret data scientists of the deal making profession! Indeed, legal software has come along in leaps and bounds.
Beyond that, companies such as Eigen Technologies provide software that automates the extraction of data from documents. The software accurately extracts data from diverse documents at scale and classifies these documents based on their content. The software then allows one to make the right business decisions.
LegalTech has even become a commonplace adjective for this software which can automatically create and adapt legal contracts, and manage different versions of contracts. Every investment banker knows just how many iterations of contracts exist during the negotiation process, and how important it is to get the right contract signed with the correct changes made (as one simply cannot check every page when the signing day comes!).
Recently Forbes investigated the use of Blockchain in law with their article “What Happens When Legal Tech Meets Blockchain”.
Negotiation – every investment banker will say that this element of the deal making process can simply not be automated. How could a computer negotiate as well as an investment banker?
Well, it turns out that computers have been learning to negotiate business deals for some time! In this 2013 article, Forbes states that when it comes to computers, not only can they negotiate, they can also use win-win moves to help their human counterparts yield more value for both parties. In this 2017 article, ScienceMag states that computers that could negotiate for us could automate and optimize everything from traffic intersections to global treaties.
So, computers may not have perfected business deal negotiation right now, but they are learning, and they are learning fast!
Financial Management and Agency Functions
Finally, an opportunity for blockchain!
Once a deal closes, a bank needs to ensure that the relevant funds are transferred to a client, on time and for the right purpose.
For example, let’s assume we are financing a wind farm. At financial close, funds need to move from the bank to the SPV set up to build and operate the wind farm. These funds are earmarked for specific purposes, from paying lawyers to paying the EPC contractor to start working. Funding will also be reserved for technical specialists and even government. What better way to ensure funding is processed to all the correct parties than managing the process with blockchain? This becomes even more beneficial when the construction process is 2 – 3 years. Each month funding is drawn down from the bank and paid to various parties – blockchain could ensure that funding is drawn down on time, for the correct purpose and an audit trail is maintained.
DealTech – Coming Soon
As you can see from the above, numerous opportunities along the value chain exist for automation, optimisation, data science and more. Therefore, DealTech clearly has a role to play in investment banking. Now read DealTech Part 3 – Getting Practical.
Let me know your thoughts in the comments section below.
Good luck and happy financial modelling!