The Future of Financial Diligence & Technology
Updated: Aug 30
I’ve made a career in the world of financial due diligence (FDD). After eight years at EY, including leading transaction teams in the EY Transaction Advisory Service practice, I now lead a team at Finagraph that delivers technology to FDD teams across the country.
In my time at EY, I experienced the highs of working in a fast-paced environment with innovative companies and big-name clients – and the thrill of putting our team’s accounting expertise to work behind transactions headlined in financial news. I also experienced the common challenges this environment creates: time pressure and a strain on our people.
Time is money, and that isn’t any truer than in accounting & consulting. In our world, the most precious resource is the employee, and the economics of a project, team, and the whole firm relies on the time it takes for that employee to complete tasks.
Time Pressure: A Constant Reality
In FDD, project timelines are lightning fast (for example: a Quality of Earnings, or QoE report, can sometimes be completed in a week). They are also often accompanied by inflexible client deadlines. To complete a project effectively and on-time, there are two significant variables to team must work with.
The first variable is often outside of the team’s control. This is the time it takes the company to provide data needed to complete the work, and just as important, the quality of that data.
The second variable is more controllable: how efficiently the team can complete the work. This carries many dependent variables as well, including the availability of the team, their skillsets, and their training.
For a visual representation of this, imagine you’re traveling on a train with broken breaks and 10 miles ahead is a boulder on the track (don’t worry, this has a happy ending). On the train is an engineer, looking for tools in his bag and a team of engineers waiting to use those tools to fix the breaks. The engineer will find the tools, because he never enters the train without them, but when? How much time will the team have to fix the breaks before reaching the boulder?
In this example, the boulder is the project deadline, which isn’t moving. The tools and the engineer looking for them are the company and their data. The onlooking engineers are the FDD team, saying “Get me those darn tools already! We’re wasting time!” Whether the team gets the tools at mile 1, mile 5 or mile 9 in the journey, they need to fix the breaks before that deadline.
Thankfully – FDD teams almost always finish the job on time because that’s what the profession requires (that’s the happy ending).
That said, the stress and uncertainty of working in that environment is palpable. Late nights. Early mornings. Full court press. Sometimes the team gets lucky, and a huge gust of wind rolls the boulder a little bit further down the tracks, giving the team some more breathing room. But the wind is never a guarantee.
Working on this train 365 days a year can be exhausting and damaging to employees and the firm as a whole. It also makes it very difficult to scale the practice.
This is a long way of saying that the management of FDD professionals’ time is key in this business. If the team is waiting for data, we can make sure they can work on other projects in the meantime (and ignore switching costs). If the team has data, we need to make sure they are as efficient as possible, so we have time to quality check the work, deliver to the client, and move on to other projects.
Over the last few years, the Finagraph team and I have worked with over 100 FDD practices looking to perfect this by combining talented professionals and technology. Some are established, 250+ person teams, others are just starting out.
Here’s what I’ve learned from those teams, where technology fits, and what I believe is the future of technology for the profession.
Scaling an FDD Team: Cone vs. Pyramid
Back in 2013, my team at EY had a structure problem. Simply put, we were structured like a cone (too top-heavy), rather than a pyramid. We had plenty of leadership but not nearly enough staff to get the work done.
This caused thin project margins because higher-level team members were doing detailed work. It also limited our ability to take on additional projects. The whole team knew we needed to create the pyramid-style structure to even out these issues, but that was no easy feat. The senior managers had to pour in several years’ worth of sweat equity to get us there.
Working with firms who are building new FDD practices, I see this same team structure problem over and over again. The dreaded cone! Unfortunately, I also see the same approach to solve it that we used back in 2013. Firms are throwing time and sweat equity at the problem, wasting resources and draining their team in the process.
Let me be clear: Obviously you need leadership first to start an FDD practice. It doesn’t start with staff who have never done the work before. BUT, I have seen teams rely on these leaders for too long and spend years trying to build the pyramid.
Thankfully, it’s not 2013 anymore. Technology has evolved and there are tools available to help FDD practices balance their teams and scale their business. Technology is a different type of resource investment, but it’s one with faster results and less strain on your human capital.
Technology releases pressure on the base tier of the pyramid, both in the number of staff at that level (you might need, for example, 5 staff instead of 15) and the training that leadership needs to provide them.
In practice, new staff are brought in to increase a practice’s deal capacity as more people generally means more projects and more revenue. The way firms build the required skills in their new staff is typically through repetition.
Staff are expected to grind their way through data collection and clean-up to learn how to work with the data and how it all fits together. Not once. Not twice. But continuously. Over and over and over and over. Practice makes perfect, they say.
From first-hand experience, I can say confidently that there is value in ‘learning by doing’ with financial data. That value, however, has a limit. After doing this three, four or even five times, it’s not learning by doing – it’s doing less by doing it manually.
At a certain point, there’s not really anything substantial left to learn in this repetition, but staff are still expected to keep powering on until they get promoted to the next level and train the new staff to do the same.
Instead, this is a place where practices can embrace technology that wasn’t available to us back in 2013. After a set training period, our FDD clients today give their staff access to our tool Strongbox.
Strongbox automates data extraction, data clean-up, and processing. Not only does it give the staff more time to focus on learning the next layer of skills needed to advance their careers and add value to the firm and client, it inevitably frees up time for them to work on additional projects. (If you’re interested in this topic, our team recently covered it in depth in a separate article.)
Another way I see firms looking to balance their teams is through outsourcing. They see outsourcing as a cost-effective way to supplement their US team. We did this during my tenure at EY. We had a significant outsourced operation that was truly excellently run. But it came with its own set of problems.
Outsourcing requires a significant time investment across all levels. Leadership must deal with performance issues, turnover, and contract negotiations. Staff must commit hours and hours to prepare instructions, review work, and correct mistakes.
To get outsourcing right requires time and dollars, and even in well-run operations, the time savings at the end of the day may not be substantial enough to justify the effort and cost. Where I see firms excel is implementing a hybrid approach, where technology and outsourcing work together to solve issues and fill gaps.
FDD Technology: Analytics Tools and the Databook
I still remember the very first deal I ever worked on. I was told, “Your job is to build the databook and get us ready for our meeting with the company’s management.”
The databook is at the core of every FDD project. It is an Excel workbook built by the FDD team that contains historical financial data, trends, analysis, and calculations, plus insights for the client.
I learned quickly that building a databook is no small feat. The first step is working through data collection and our request list. It requires constantly reviewing every single document posted by the company into a dataroom, comparing it to our request, and managing follow-ups to get the right information from the company. It’s incredibly common for the company to post incomplete data and items that do not fulfill what was requested – this can take days or sometimes weeks.
At this point, we are the team of engineers on the train saying: “Please send this so I can actually start working!”
Once the data is collected, the job is to meticulously build the Excel databook so we could have an intelligent conversation with the management team. This could be anything from 10 to 50 different Excel tabs with tables and calculations that need to tie together. Everything is hinged on this databook!
Fast forward to today: this process has not changed since my very first deal. Data collecting and building the databook are still the largest time suck on every deal. Every time I talk to a firm that says they are at capacity, these are the biggest culprits as to why. The timeline of the entire project depends on how long these steps take.
And it simply doesn’t have to be this way.
Enter technology. Electronic record keeping and advanced technology have merged in ways that can largely automate many hurdles for businesses across a variety of sectors. However, there are very few options built specifically for the needs of an FDD practice. Many large firms have databook automation initiatives that are largely based on Excel macros. External tools like Alteryx and PowerBI are also commonly used to create data workflows for analysis, but these tools are intentionally broad in scope, built to serve everyone from HR teams to manufacturers.
Strongbox, on the other hand, was built specifically for FDD by financial experts. With a few clicks, Strongbox delivers a ready-to-use databook pulled directly from the target company’s accounting system or ERP, all in minutes. No frustrating back-and-forth with the company on the request list. No hours upon hours of databook building from scratch.
With the customization and branding options we offer, we see our clients go from spending hours hunting down reports and building databooks to simply using the Strongbox output for management meetings.
For firms that already have a sophisticated databook template, Strongbox generates data in flat tables that can be dropped into an inputs tab, populating all other tabs in your current format. The time saved means faster deal times for your clients, increased capacity from your team for new business, and the ability to scale revenue and margins without burning out your team.
The Future of FDD
As FDD firms integrate more technology into their practices, FDD itself will also evolve. Based on what I’m seeing, from the Big 4 firms down to small, regional practices, the future will involve more real-time collaboration between staff and clients.
Reporting tools will enable an interactive diligence process where all parties can work with the same data to perform whatever analysis they might need. This will make it easy for firms to implement a “build-your-own” databook in seconds that can be customized for each deal’s unique circumstances.
Instant data access is another area I see exploding. Digital experiences and on-demand access are now standard expectations from clients. Being able to safely transfer needed information instantaneously via digital access will increase transparency and trust for all parties.
Working with this instant, real-time data using machine learning will make identifying anomalies or potential question areas almost instantaneous. I’m hesitant to be so bold as to say that the technology will be able to identify and calculate diligence adjustments without the skill of the team, but automation technology is evolving fast.
At the very least, the technology will enable analysts to process data faster and provide insights faster, and even spot things they never would have been able to by manually working with big data sets.
Building the Future
Now, back to our train and the boulder. What if you could guarantee the team receives their tools at mile 1 and completes the fix at mile 3, giving the team 7 miles to run tests, make a call to their expert on the ground to check their work, and ultimately stop well ahead of danger? What would that mean for FDD teams and their clients?
With Strongbox, we are building this future, the future of FDD technology. We’re not only on the cutting edge of AI and machine learning, but we also stay laser-focused on the world of due diligence. Contact us today to join the growing list of firms embracing the future to scale their business.
I also invite you to join me personally on LinkedIn. What problems are your firms facing that I missed? What technology do you wish you had?