Data to information to text summaries of financial data - Kyle, Cameron (Department of Computer Science, 2019)
The field of auditing is becoming increasingly dependent on information technology as auditors are forced to follow the increasingly complex information processing of their clients. There exists a need for a system that can convert vast quantities of data generated by existing systems and data analytics techniques, into usable information and then into a format that is easy for someone not trained in data analytics to understand. This is possible through Natural Language Generation (NLG). The field of auditing has not previously been applied to this pipeline. This research looks at the auditing of Investment Fund Management, of which a specific procedure is the comparison of two time series (one of the fund being tested and another of the benchmark it is supposed to follow) to identify potential misstatements in the investment fund. We solve this problem through a combination of incremental innovations on existing techniques in the text planning stage as well as pre-NLG processing steps, with effective leveraging of accepted sentence planning and realisation techniques. Additionally, fuzzy logic is used to provide a more human decision system. This allows the system to transform data into information and then into text. This has been evaluated by experts and achieved positive results with regard to audit impact, readability and understandability, while falling slight short of the stated accuracy targets. These preliminary results are positive in general and are therefore encouraging for further development.