The McKinsey report titled A Future that Works: Automation, Employment, and Productivity stated:
"About 60% of occupations have at least 30% of their activities that are automatable."
Automation has become the focus of growing interest in the global banking and financial services industry. Many banks and financial companies are deploying the latest automation technologies for increased productivity, cost savings, and improvement in customer experiences.
With AI-powered technologies like natural language generation, several repetitive, data-oriented tasks like report generation can be automated for swift results, while duly incorporating current regulations in the financial landscape.
At vPhrase, our product Phrazor uses advanced natural language generation technology to augment and automate reporting and data analytics for enterprises. We have successfully deployed Phrazor’s technology to meet the requirements of diverse industries including banking, and financial services firms.
Here is a closer look at some examples.
Reporting Automation Use Cases & Benefits
1. Quarterly Earnings Report
A Quarterly Earnings Report is one of the most important financial statements of the company. Investors assess these statements to determine the financial health and investment worthiness of the company. It provides a quarterly update on the income statement, balance sheet, and cash flow statement.
The process of gathering information can be quite tedious and time-consuming. However, that aspect is mostly looked into by accounting software. The problem arises in explaining the data in a manner that every investor understands.
Why were you over/under budget? What were the contributors to performance?
Today, companies have data for smart decision-making. But access to data alone isn’t the answer. Organizations need to present data in a way that makes it easy to consume by experts and non-technical users.
With reporting automation and the use of Natural Language Generation (NLG), companies can easily reduce costs by creating accurate, error-free reports at scale. NLG is a real game-changer for analytics due to its ability to convert complex data into narrative insights, written in natural language, and easily understood by everyone.
2. Branch Performance Report
Branch Performance Reporting is a process that every level in the bank’s hierarchy partakes in. Depending on the nature of the organization, these reports are usually drafted manually by compiling and analyzing data for both digital and brick and mortar sales. The next inline manager further adds his learnings to these reports to present it to the subsequent level. This process proves both time-consuming and highly uneconomical.
Reporting automation has reduced overhead costs of reporting and saved critical man-hours that can be used to focus on other higher-value tasks. In this case, the Branch Managers can focus their attention on client meetings and tapping into potential revenue opportunities in place of analyzing and interpreting complex reports. Also, manually generated reports are prone to clerical errors and misinterpretation.
Automated Report Writing powered with an advanced Artificial Intelligence application like natural language generation, and machine learning capabilities help generate thousands of comprehensive reports with personalized recommendations in a fraction of the time taken by manual reporting. Considering how crucial this kind of report is to increase branch productivity and pursue new growth opportunities, it is only prudent to ensure that it is generated and prepared efficiently.
3. Recruitment Performance Report
Recruitment Performance Reports are designed to track the progress of the hiring team and identify areas of improvement in different stages of staffing. Such reports provide data on various KPIs like time-to-fill, time-to-hire, source-of-hire, first-year attrition, hiring manager satisfaction, quality of hire, applicants per opening, candidate job satisfaction, and many more.
The major pain points experienced with recruitment performance reports is the amount of time invested in the manual processes of data extraction, data cleaning, structuring, and further analyses to derive insightful conclusions. Moreover, the lack of customizable reports for various scrutiny levels undermines the efficiency of the manual reporting process.
By automating recruitment performance reports, companies can generate personalized on-demand reports, tailored for different levels in the organization. With just a click of the button, recruiters can fast-track the staffing process by accessing meaningful insights with easy-to-understand, narrative-based reports.
4. Portfolio Analysis Report
A Portfolio Analysis Report is essentially a factual summary of the investor’s assets. It helps investors understand the status of their investments in context to the entire portfolio. As per the current industry trend, these reports usually contain tables and charts, lacking advisor recommendations, making them challenging to comprehend even for data-savvy customers.
Implementing automated data analytics with natural language generation technology generates customized portfolio analysis with personalized recommendations for every investor at scale.
Also, due to predictive analytics combined with NLG, even the tiniest details are captured, uncovering hidden insights that may not be apparent in graphical representations.
Reporting automation, when strategically implemented, increases organizational efficiency and productivity. It also helps prioritize higher-value tasks, of which there are many in the financial services space.Phrazor’s intuitive interface and natural language generation capabilities for ease of understanding and accurate reporting have proven crucial in streamlining the reporting process in many organizations.
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.