Nearly any decision in finance can be traced back to three primary objectives: save money, make money, or manage risk. The combination of RPA and NLG (Natural language generation) supports these goals. Both RPA and natural language AI deliver material efficiencies. While finance may slow to adopt new technologies – at least compared to other industries – the sector is also uniquely positioned to realize the greatest benefits. When RPA and natural language technologies are joined in a single platform, it is a game-changing innovation that impacts the entire organization. Let’s take a look at how the two technologies work together.
RPA software extracts and assembles both structured and unstructured data from multiple sources. Then the NLG platform creates written reports instantaneously based on the data. RPA can format the reports and distribute PDFs to appropriate constituencies.
In financial settings, RPA technology eliminates manual tasks around performance measurement and attribution to automatically organize and analyze return data. NLG handles middle-office roles, such as drafting, iterating, and reviewing copy and commentary. Beyond eliminating human errors, “Intelligent Automation” also improves compliance, accelerates performance, facilitates better access to insights, and improves data literacy in the process.
Savings and efficiencies represent the most obvious payoff. For instance, an overlooked challenge only back- and middle-office professionals will appreciate is that there is no “load-balancing” for reporting on monthly, quarterly or annual schedules. Historically, adding new funds and products requires significant investments in the back- and middle-office to keep up with demands, which have only become more onerous in today’s evolving regulatory landscape. When RPA and NLG technologies come together, investment managers can suddenly scale far more efficiently, adding new assets without investing in support staff.
Perhaps the biggest advantage from the perspective of compliance professionals is the extent to which RPA and NLG technologies reduce risk. Errors are common across any industry that deals with data, but they can be exceedingly costly when they occur in finance and investment management. For example, the 2010 “flash crash” was initially attributed to human error. By automating data extraction, organization, and analysis, asset managers eliminate such risks.
Another factor is that these technologies eliminate human biases. Regulators will cite inconsistent disclosures, misleading statements, and omissions of material information as red flags deserving scrutiny. The “indifference” of technology imparts a sense of trust.