Artificial intelligence is a technology that requires neither an introduction nor an endorsement of its capabilities. Popular machine learning applications capable of performing unprecedented feats like beating humans at games like chess and Go to engaging in insightful debates with humans have clearly demonstrated the prowess of present-day AI. Such applications highlight AI’s capability to process large volumes of data, perform repetitive and monotonous tasks with superhuman accuracy, and automate basic creative tasks like content generation via natural language technology.
For businesses, however, it is the ability of artificial intelligence to scour through huge volumes of data to find bits of usable information that holds the most value. In the words of leading AI expert, Jonathan Mugan, "If you can tell a machine what you are looking for, it can look through more data than you could read in a lifetime to find it."
The banking and finance sector needs to make sense of huge volumes of data for making critical decisions, can benefit at large from such a capability. And realizing this, the sector has been quick to adopt machine learning to automate report writing to expedite and enhance its decision-making processes.
The role of reporting automation in reshaping the banking-finance sector
Documenting and reporting financial information is a critical activity in banking and financial services operations. However, generating hundreds or thousands of customized reports with uniform levels of focus and diligence is a lot to ask for from a team of humans. That's because report creation involves selectively extracting critical information from massive bodies of data and representing it in an easily consumable format. Errors may lead to severe financial and legal consequences. And this is where reporting automation tools are making an impact on financial institutions!
Reporting Automation tools can skim through large volumes of structured data to find valuable good insights. Using natural language generation technology, these insights are translated into concise written narratives that finance executives and business leaders can use to make decisions.
Using machine learning and natural language generation technology has enabled financial institutions to improve their productivity, eliminate the scope of clerical errors in their financial reports, and ensure compliance with industry standards and internal policies.
As a result, reporting automation is empowering financial institutions as well as accounting teams at enterprises to maximize their efficiency. And reporting automation is only one of the numerous applications of AI and machine learning; other services being compounding the impact of reporting automation in different areas of finance.
The impact of reporting automation on the financial industry
The banking and financial services industry involves large volumes of quantitative data, repetitive workflows, and the non-negotiable need for accuracy -- challenges that reporting automation tools specialize in dealing with. Here are a few ways artificial intelligence & reporting automation are impacting the finance industry:
1. Market forecasts
Trends in financial markets are hard to predict since a large number of variables are at play. Keeping track of these variables and their compound effect on the market can help financial advisors spot patterns in data that are indicative of specific market trends. Financial institutions and investment firms are using augmented analytics tools to automate reporting with machine learning capabilities like predictive analysis to monitor the existing market conditions and forecast future market trends to guide investment decisions. As a result, these institutions are minimizing their risks and maximizing their gains from their investments.
2. Content creation
Creating reports like financial statements for account holders and market insights for investors can take up a lot of time for employees of financial institutions. To generate such reports, these employees go through massive volumes of numerical data to pick out the most pertinent facts and reproduce them in an informative format for their customers. These reports usually contain data visuals like charts and graphs with an attempt to make them look interesting and understandable. But these visualizations often lack insights making them difficult to comprehend for their investors and clients. By using natural language generation-enabled reporting automation tools, these reports can be generated quickly without any error, enabling the end consumer to understand these statements with simplified conversational narratives. Additionally, it also frees up the employee’s time which they can utilize to perform other non-routine creative and strategic tasks.
3. Customer service
Providing timely and satisfactory customer service is a challenge for banking institutions. The banks’ advisory teams are far outnumbered by the millions of customers and investors who need important information regarding their financial accounts and investments every minute. In addition to having to respond to a multitude of queries simultaneously, the financial advisors also need to quickly access the requisite financial information and provide only relevant facts to the clients in a way they can easily understand.
Reporting automation tools can assist the advisors by automatically generating custom and personalized portfolio analysis reports with the necessary information regarding the accounts and assets of every customer. Also, these reports can also be shared with the customers to ensure they have all the information they need without having to repeatedly contact their advisors or relationship managers.
By leveraging reporting automation and machine learning, businesses can not only automate their day-to-day operations but also enhance their strategic decision-making. This is exactly why so many organizations are utilizing augmented analytics tools like Phrazor to make sense of the large volumes of data generated by them and translate this data into actionable intelligence. Phrazor uses natural language generation technology to convert large volumes of structured data into concise reports with a conversational tone. Result? Businesses are able to make highly-informed, proactive decisions!
Romil Shah is an AI enthusiast with considerable experience across varied technology domains, primarily Natural Language Generation (NLG), and Blockchain technology. He is passionate about technological innovation and more importantly its real-world applications. His work within the field has brought about constructive conversations and explorations around AI and its extended use in businesses.