Financial services firms are struggling to keep pace with the growing need to provide continual reporting and analysis on the companies they cover.
Whereas many industries have adopted automation initiatives and the modern data stack, financial analysts are still spending most of their time performing repetitive analyses in Excel and producing extensive reports in PowerPoint. According to Ernst & Young, given that financial functions are rules-based, 80% of finance-related tasks could benefit from automation.
This is quickly changing, however, as finance teams look to embrace intelligent automation and data science techniques to drive greater efficiency in their workflows and minimize costs.
Here are some common themes to be aware of in 2023.
Investment Banks Automating Financial Modeling
The industry is notorious for having its analysts spend all-nighters in the office building and refreshing financial models for upcoming M&A and financing deals. Investment banks can now use analytics operating systems to automate their Excel jockeying through repeatable workflows that handle data collection, analysis, and reporting.
Equity Research and IBD Teams Using End-To-End Automation Tools to Auto-Generate Data-Driven Sales Collateral
PowerPoint decks are the currency of investment banks looking to generate new business. While a portion of these presentations are personalized to a specific transaction, most slides are templatized and data intensive.
Using automation tools, analysts can automatically generate these data-driven presentations through point and click interfaces that make it easy to port over templates and rewire them for different companies and time periods. This will enable banks to be more cost efficient by allowing analysts to focus on higher value add activities.
VC and PE Firms Streamlining Portfolio Company Diligence
Venture capital and private equity firms spend an exorbitant amount of time analyzing data rooms during their diligence on prospective investments and portfolio companies.
Today’s manual processes involve combing through unstructured Excel files with different schemas and naming conventions and attempting to combine them with 3rd party signals to better predict whether a given company will be successful in the future.
Intelligent automation platforms can drive greater efficiency in these manual processes through fuzzy mapping algorithms that standardize the ingestion and transformation of data across different sources. Teams can also use these platforms to simplify the ingestion of unstructured financial data without the manual effort that was traditionally required of analysts to comb through pages of documents to extract numbers, topics, and themes.
VCs and M&A Teams Using Data Science to Automate and Drive Greater Intelligence in Investing Decisions
To date, most VC funds and corporate M&A teams haven’t utilized data science and AI to better inform investing decisions.
Given the disconnected nature of information that is available on both public and non-public companies, investment teams often engage in manual diligence and analytical tasks. As a result, M&A processes are often lengthy and driven off human judgment.
Analytics operating systems like Redbird are empowering teams to ingest data from any source without writing code through RPA (Robotic Process Automation), web scraping and other advanced techniques. Once the data has been standardized, no-code tooling also enables these same users to train data science models to better predict future outcomes such as revenue or market share growth. These predictive models can help augment human investment decision-making much like search engines or navigation apps help us in our everyday lives.
FP&A and Investor Relations Automating Recurring Analytics and Reporting
CFOs are always looking to drive efficiency in corporate processes to keep teams lean and costs down. Historically, finance teams have often resorted to throwing more analyst hours at a problem versus finding technological solutions to enable these analysts to do more with less.
As the volume of outputs continues to grow, analysts are increasingly unable to keep up. Analytics automation platforms empower these teams with the tools they need to build RPA workflows that automate tedious, repetitive analyses and produce data-driven reporting outputs in 1/10th the time. Not only does this approach free up time for higher value-add activities but it also reduces human error which has become commonplace in financial reporting.
Conclusion
Although there are now tools to automate complex workflows and run advanced analytics without code, some financial services companies have been slower to adapt. As a result of a highly competitive landscape and macroeconomic pressures, this equation is changing, which will unlock significant business value in 2023 (and beyond) for the savvy financial organizations who embrace new tools and techniques.