Status as on- 28/02/2023
The insolvency area of practice, like any other work area, is also experiencing digital disruption because of growing dependency upon the technologies like smartphones, cloud computing, data analytics, and social media. The ability of such technologies to transgress professional boundaries means that professionals are no longer considered to be sole custodians of specialist, complex knowledge and training. The demand for traditional professionals (who are considered to be traditional gatekeepers of information, knowledge, and practical expertise) and the conventional professional worker will decline and that information technology will drive this decline. ROSS Intelligence, an IA platform, developed in the United States, is an example of innovation driving digital disruption. The digital platform is designed to understand natural language questions, collect and collate evidence from searching vast digital databases and provide evidence-based answers to US bankruptcy issues, thereby performing an advisory role previously held by bankruptcy practitioners.
In India, many key professions and legal activities have become more digitalized. Particularly, the legal world started shifting towards “machine learning” and “artificial intelligence” for prompt redressal of business and financial matters to aid the economy. Globally, one of the most notable areas of law where the shift is traced is insolvency proceedings for its relation to accounts and finance.
AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence, like visual perception, speech recognition, decision-making, and translation between languages. Machine learning, deep learning, artificial neural networks, rule-based expert systems, and natural language processing are a few examples of artificial intelligence technologies employed in worldwide insolvency and bankruptcy processes. These technologies can augment the possibility of a company becoming insolvent and help in the admission of cases. Many times it can also be beneficial in providing a competent resolution plan by analyzing earlier similar cases and the outcome of resolution plans.
To better understand the artificial intelligence role in insolvency, it is divided into 3 stages: Pre-Insolvency, Corporate Insolvency Resolution Process, and Liquidation.
Pre-Insolvency
Predicting insolvency at the early stage helps to minimize the risk and take the right decisions timely. Before technology advancements, experts used to predict insolvency by considering the company’s financial health. AI algorithms help to deal with the databases of a high volume of information in a fraction of time and resources. Furthermore, Machine learning techniques like logistic regression, support vector machine, lasso regression, bagging, decision tree, etc. are inculcated by AI by experts to predict insolvencies.
Corporate Insolvency Resolution Process
The inclusion of AI in CIRP can help RP in quick and firm decision-making by evaluating the key performance indicators of a business and on other hand, investigators can use AI systems to conduct file discovery searches of crucial storage repositories and email servers. AI algorithm helps in the enhancement of understanding of how to recognize documents and sources, therefore helping in speedy delivery of findings and reduction of cost.
Liquidation
As per the latest IBBI data, more than 79% of the ongoing liquidation proceedings have crossed the prescribed time limit to wind up the company as per the IBBI regulations. AI can serve the function of a pacifier as there are promising AIs that employ Machine Learning and performs optimum auctions. Currently, the problem of time-bound liquidation persists because the liquidator is responsible for processing traditional and non-traditional data. However, the use of AI as a tool to boost the efficiency of professionals can aid in obtaining desired outcomes.
Due to the advent of big data Insolvency Professionals would have access to more data than ever before. This would allow the advisor to identify strategies, factors, and issues that will impact business performance. More digitized data means the professional won’t have to spend days physically searching for relevant documents. Using data analytics AI systems to analyze this big chunk of data would help the professional to reach quicker conclusions. These systems could conduct file scans of key storage repositories and email servers to discover documents. AI algorithm would help insolvency practitioners to discover compliances and non-compliances easily.
Disclaimer: The above article is based on the personal interpretation of the related orders and laws. The readers are expected to take expert opinions before relying upon the article. For more information, please contact us at ibc@centrik.in
join For Updates