Robotic Process Automation (RPA) is already a very famous niche on the information technology sector. The market well understands the need for a technology that automizes the multitude of repetitive tasks that each enterprise faces. Performing simple but time-consuming rule-based tasks, such as processing invoices, modifying documents or sending e-mails is much more efficient with an RPA algorithm.
Despite its attractive scope, RPA is still perceived merely as an emerging technology. Tailor-made robots that automize company-specific tasks are not yet a very cost-effective investment and it also requires highly skilled IT specialists to program the robot. However, Ui.Path, a rapidly growing start-up, offers a more easy-to-use and user-friendly platform that allows even non-IT specialists to design RPA algorithms to automize their tasks. The company provides not very long trainings to use the platform, which is a much more effective investment on employees compared to contracting programmers to write the algorithms.
The company is interesting from both an information technology and a financial perspective. It was founded by Daniel Dines who quickly became the richest Romanian. With its first large-scale market launch in 2015, the start-up grew immensely within these years, being currently evaluated at approximately 10 billion dollars (65% compounded annual growth rate). Ui.Path is the only RPA provider named to Forbes AI 50 in 2020, and it is planning to launch an IPO in the nearby future which will be a very interesting event for the financial market. Its effectiveness is very notable for the financial sector companies. Employees on this industry face a lot of easy and repetitive tasks, and with the digitalization trends they can be easily incentivized to learn to use this RPA platform in their daily tasks. Major giant companies have contracted Ui.Path, such as Citi Bank, PwC, EY, Deloitte, Orange, etc.
Subsequently, Ui.Path is a very interesting start-up on the RPA sector, fully deserving very careful monitoring in the next few years. Perhaps it’s the next tech giant, considering its current growth?

References
Ui.Path 2020, About Us, Ui.Path, https://www.uipath.com/company/about-us
Ohnsman, A., Kenrick, C. 2020, ‘AI 50: America’s Most Promising Artificial Intelligence Companies’, Forbes, 3 July, https://www.forbes.com/sites/alanohnsman/2020/07/03/ai-50-americas-most-promising-artificial-intelligence-companies/#63badb185c99
Ui.Path 2020, Customer Success Stories, Ui.Path, https://www.uipath.com/solutions/customer-success-stories

The direction of auditing technology development is towards continuous audit (Rezaee et al., 2002) – “a comprehensive electronic audit process that enables auditors to provide some degree of assurance on continuous information simultaneously with, or shortly after, the disclosure of the information”. As currently most tasks are performed manually by auditors through Excel and take significant time to complete, large investments are made on innovating the industry, through AI or other similar technologies. The advantages of implementing AI in audit processes are optimization of repetitive tasks, lowering costs of processing, a more profound level of financial statements analysis, and a higher level of employee satisfaction due to more engaging tasks and less routine (Fagella, 2020). Most breakthroughs on the audit sector, developed primarily by the Big Four companies, are in the fields of text mining procedures, expert systems, and process modelling (Rezaee et al., 2002).
– Data mining for fraud detection of financial statements – currently fraud detection is done by analysing manually samples of client’s transactions. AI can use larger samples of data more efficiently and analyses with more precision, given that the right criteria are set (PwC Global, 2019a).
– NLP and OCR – the fields of Natural Language Processing and Optical Character Recognition become more important, as it allows the reading and processing of large amounts of standardized PDF documents, such as invoices, contracts, or internal decisions (Fagella, 2020). This task is currently performed manually by auditors, taking the extremely long time to process every document one be one.
– Miscellaneous –drones for stock-counting, blockchain, Robotic Process Automation programs, etc. (Fagella, 2020).
PwC (PwC Global, 2019b) estimates AI to have potential for consumption impact of 3.3, on a scale of 1 to 5. This is mainly based on the fact that there is a lot of data available in this sector, simplifying the processing ability of AI. On the other hand, personalization for each customer and time saved are estimated to be on average, as consumer trust and regulatory acceptance would be required to implement the programs at full potential (PwC Global, 2019b).

References
Fagella, D., (2020, April 3). AI in the Accounting Big Four – Comparing Deloitte, PwC, KPMG, and EY. Emerj. Retrieved from https://emerj.com/ai-sector-overviews/ai-in-the-accounting-big-four-comparing-deloitte-pwc-kpmg-and-ey/
PwC Global. (2019a). PwC Global – Harnessing the power of AI to transform the detection of fraud and error. Retrieved from https://www.pwc.com/gx/en/about/stories-from-across-the-world/harnessing-the-power-of-ai-to-transform-the-detection-of-fraud-and-error.html
PwC Global. (2019b). PwC Global – Sizing the Prize. Retrieved from https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html
Rezaee, Z., Sharbatoghlie, A., Elam, R., & McMickle, P. (2002). Continuous auditing: Building automated auditing capability. Auditing, 21(1), 147-163.