DealTech Part 3- Let’s Get Practical
DealTech – Let’s Get Practical
In the previous two DealTech articles, I discussed how DealTech can revolutionise investment banking through automation and optimisation. In the first article, I introduced the concept of DealTech and how an investment banking deal-making process is largely manual. I explored the room for automation and how the industry might react. In the second article, I delved into the specific areas where I think automation can occur. In this article, I will explore how we can make this automation happen (largely based on my experience of automating these processes already).
Where to Start?
Obviously, the financial modelling which underlies each investment banking deal is low hanging fruit, but I believe that where we can start is legal documentation. Many people not familiar with an investment banking deal will be astounded by the sheer volume of contracts and how detailed and large these contracts are! Even with standardised forms of legal agreements such as those provided by the LMA documentation guidelines, legal documentation is a very large part of the deal-making process. It’s no surprise to investment bankers that legal firms have some of the fanciest buildings in town.
So, what has the legal profession done in the way of automation? As I mentioned in the previous article – quite a lot! In fact, DealTech is also the name of a blog which focuses on transactional legal technology. According to them, “By ‘DealTech’, we mean the transactional tools and technology that help lawyers and other professionals get deals done. Most narrowly, DealTech comprises transaction management platforms, assisted contract review tools, etc., but we think DealTech should also embrace a range of other technological tools and products—from document management solutions to time-keeping and billing tools—that are essential to running an effective transactional practice”.
I, of course, have my own, slightly broader definition of DealTech, as defined in the first article I wrote on the matter.
The tools showcased on their site include software for automated review and approval of contracts, transaction management tools for streamlining closing checklists (I assume these are CPs or what’s known as Conditions Precedent to closing a deal), and contract review and analysis software. Eigen Technologies uses AI to read and analyse complex legal contracts. One could easily assume, based on this list, that law is leading the DealTech field! Surely investment banks are next?
Investment Banking and DealTech – What do We Need
Given that the legal profession has made such significant strides in using technology to make their lives easier, how can investment banks follow their lead? The answer, in my opinion, is far more about the human element than the technology required, which largely already exists.
I believe that investment banks need two key human elements to proceed on the DealTech journey:
- People with domain expertise. These are people who have been bankers and transactors and understand the investment banking deal-making process. These individuals should also understand data science and machine learning and its potential in the investment banking process. This is largely a mindset – either someone can see the big picture, or they can’t! These people need to be visionaries – ready to make big changes to a decades-old profession and deal-making process.
- The willingness of management and executives to drive the process, hence my talk on Data Science for Executives which explains why companies need executive buy-in. Again, a human element, and this time it comes from the very top. Without C-Suite buy-in, one cannot expect data science to have any sort of impact in the investment bank. Data Scientists and their teams need to know that the CEO of the company has their back to deal with any issues or conflicts that may arise. Investment banks, like any corporates, need people who can manage change and be data science evangelists – able to convince everyone from senior dealmakers to the support staff that data is important and will allow the bank to not only survive but thrive.
Let’s Talk Practicalities
Practically, an investment bank needs the following to best take advantage of the data science possibilities that lie before them.
- Data stored on time, in the correct format, and treated with respect! To embark on the data science and automation journey we need data – clean, relevant and easily accessible data. While one can simply put the entire data responsibility on a data engineer’s shoulders, it should be the entire organisation’s responsibility to store data in the right place and accurately. Storing data should be made easy and accessible – rather have some data that is usable than a lot of data that isn’t!
- An IT infrastructure that allows for the testing and wide-scale implementation of data science projects;
- A data lake or reservoir with data that is accessible;
- A data engineering team whose responsibility it is to ensure the data architecture is in place;
- A data science team who has continuous interaction with business to ensure they are addressing business’ needs;
- A data-aware mindset and culture.
To conclude, there are clearly two aspects when it comes to building the investment bank of the future – a human change management and negotiation element, and technical infrastructure and capability.
In the next DealTech article, I will outlay my proposed structure for a data science and data engineering team within an investment bank.