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Automating Written Reports in Finance: Top 4 Use Cases

Imagine what you could do with an extra 13 hours in your work week? What if automating written reports could give you 30% of your work week back?

According to McKinsey, “60 percent of occupations could have 30 percent or more of their constituent activities automated.” With the technology available today, many of the monotonous, data-driven tasks could be automated, freeing up your time to focus on more interesting, creative tasks. Finance in particular is an industry where automation technology is being adopted across the board. This is no surprise as finance in particular relies on providing results quickly, incorporating large amounts of data, all while taking into consideration the changing regulatory financial landscape. To give you an idea of specific examples, here are the top 4 use cases when it comes to automating written reports in Finance today:

 

1. Fund reporting:

The wide variety of funds available and the data-driven nature of fund reporting, coupled with the regulatory necessity to write the reports, make this a great case for automation. Writing fund reports manually creates two problems: one of time and one of capacity. Writing by hand takes time for the analyst to collect, analyze, and interpret the data so they can complete the report and this is expensive for the company. Secondly, the amount of time and money writing these reports takes, means that institutions limit their markets because for every fund you sell, you need to be able to write the report and it’s not possible manually. This means that larger markets and funds are the only ones companies can bring to the market leaving small markets and bespoke niche funds untouched, all because writing reports manually is just not cost-effective.

With automation, fund reporting and other similar financial reports can be done on demand with the most up-to-date data available. This means that the company can expand their offering and enter into different markets. When you look at automating written reports, use cases like this that save time and money but also expand capacity, will generally give you the best ROI.

 

2. Profit & Loss Reports (P&L):

When it comes to P&L reports, the process to collect information can be quite slow and time-consuming but newer accounting software make these worries a thing of the past. The problem is explaining the data in a way that every department manager can understand. Why were you over/under budget ? What were the contributors to performance ? Today, companies have the data to empower ever department to make smart data-driven decisions but data alone isn’t the answer: you need to explain the data in a way that everyone can understand.

By automating written reports,  companies can easily cut costs by writing the first draft using automation tools like Natural Language Generation. This means that reports can be drafted, on demand, and can be tailored to not just c-suite executives, but middle management as well. With just a click of the button, any manager within the company can get an up-to-date P&L report that is personalized for them, enabling better decision-making across the company.

 

3. Sales Reporting:

In today’s world, customers and potential clients are demanding a more personalized approach when it comes to the buying process. At the same time, companies are expecting sales people to manage more accounts than ever before and when sales people are digging through the CRM and transaction data, they aren’t selling and likely aren’t getting paid. Therefore, Salespeople often don’t use the CRM at all, and companies are left wondering why they invested so much money is a tool no one uses!

The answer is to humanize your CRM. This means using Natural Language Generation to explain the data in the CRM, to tell the salesperson who they are talking to, what they should sell/advise and why. Then after the meeting, let the CRM ask the sales person what happened and automate the follow up email. Automation and tools like Natural Language Generation and Natural Language Processing can help you to finally realise the ROI from your CRM.

 

4. Credit Management Reporting:

While used in very specific cases, credit management reporting is a very popular use case for automating written reporting in finance. Credit offerings can be quite complex, requiring specific supporting evidence as to why a particular amount of credit was approved (or denied). This cannot be done well with a template. Plus, manually writing can be very time consuming and results can be inconsistent. This can slow down deals and a potential loss of clients due to the lag.

By using automation to write the first draft of the report, a company can quickly cut costs and speed up the time it takes to write a report. Due to the data-driven nature of the report, all factors can be taken into account when considering a specific line of credit for a client. This ensures not only consistency, but helps companies to meet ever-changing compliance requirements.

 

Automation has become a buzzword in marketing and even sales but often automating written reports is overlooked. Perhaps, this is because few people realize that technology can solve this problem!

If you’re interested in learning more about how automation is transforming the financial services industry, download our e-book. Inside, you’ll learn more about the evolution of the financial analyst role and hear from industry experts on how automation technology, like Natural Language Generation (NLG) has helped maintain the competitive edge in the ever-changing climate of the finance world.