Placing the Artificial Intelligence on the Insolvency Spectrum: an Analysis – By Sakshi Pandey and Harshvardhan Singh Sikarwar

Placing the Artificial Intelligence on the Insolvency Spectrum: an Analysis

Authored by:
Sakshi Pandey, 4th year B.Com.LL.B (Hons.) pursuing graduation from the Institute of Law Nirma University, Ahmedabad
Harshvardhan Singh Sikarwar, 4th year B.Com.LL.B (Hons.) pursuing graduation from the Institute of Law Nirma University, Ahmedabad

INTRODUCTION

The pandemic’s rapid spread caused governments globally to realize they were dealing with a developing worldwide economic crisis. Hence, aftermath global pandemic the countries have considerably modified the policies, guidelines, and practices all around the world for swift recovery and functioning of economies. Apropos, many key professions and legal activities have become more digitalized. Particulary, the legal world started shifting towards “machine learning” and “artificial intelligence” (AI) 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. 

In India, there have been constant efforts made by the government and the Insolvency Bankruptcy Board of India (IBBI) to speed up the insolvency resolution and liquidation process. Recently, IBBI issued discussion papers on “reduction in delays in Corporate Insolvency Resolution Process” (CIRP) and “streamlining the liquidation process”. However, the huge number of filed cases, the complexity of the process, and the labyrinth of documents keep on dragging down such attempts. Moreover, earlier this march, while addressing a national level colloquium, the National Company Law Tribunal (NCLT) president suggested the peremptory aspect of early insolvency resolution to be the development of AI. AI can aid in the desired time-bound process of insolvency laws in the country and this era of “big data” can make the whole process a lot easier and more lucid for the stakeholders. 

The article aims to transverse into the future and explores the unexplored potential and capability of AI in insolvency laws in all three pre-insolvency, CIRP, and liquidation stages. It looks into the global adoption of AI to strengthen the argument of streamlining the insolvency resolution process through AI.

AI impacting three stages of Insolvency

AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as 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 examples of artificial intelligence technologies employed in worldwide insolvency and bankruptcy processes. These technologies have the potential to augur 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. 

i. Pre-Insolvency

The process of Insolvency in India is a time-consuming and exhausting one as it requires the formation of elaborative plans and documents. To accelerate the process, one viable solution can be predicting insolvency at an early stage or pre-insolvency stage. Predicting a company’s insolvency can provide investors with foresight, enabling them to minimize the risk. Before the technological advancements, experts used to predict insolvency taking into account the financial health of a company. However, the advent of AI has been a breakthrough in assessing insolvency as it possesses the potential to revolutionize the process in terms of time and resource efficiency.

Whilst assessing insolvency, companies acquire reams of data that help them in fulfilling clients’ preferences and achieving profit maximization. Such high volume, velocity, and variety of information, also known as Big Data, consumes a lot of time if handled by humans. AI algorithms, on the other hand, are designed to deal with databases of this size in a fraction of the time and resources that would otherwise be required. Furthermore, Machine learning techniques like logistic regression, support vector machine, lasso regression, bagging, decision tree, etc. are being inculcated in AI by experts to predict insolvency efficaciously.

Recently, Prof. Yuri Zelenkov developed an AI that uses machine learning for predicting insolvency and bankruptcy. The approach is less susceptible to data imbalances. It entails training a large number of independent classification algorithms, selecting the most effective among them, and merging them to improve the accuracy of predictions. Further, companies like Obviously.ai builds AI as per the requirement of a client on a contractual basis. Obviously.ai hatched an innovative website wherein the clients can deploy the insolvency prediction mechanism via a no-code automation machine learning tool.

ii. Corporate Insolvency Resolution Process

The IBC provides for the initiation of CIRP in case of defaults by corporate debtors. The CIRP consists of filing applications, the passing of moratorium, appointment of Resolution Professional (RP), and forming of Committee of Creditors (CoC). The exhaustive process of CIRP requires the handling of huge data by an RP. The induction of AI in CIRP can significantly reduce the time consumption as AIs’ are designed to manage such Big Data with ease. AI can help RP in quick and firm decision-making by evaluating the Key Performance Indicators of a business.

AI can identify links between performance measures and insolvency risk, which can be utilized to inform businesses before they go bankrupt. It would be helpful for investigators to use such AI systems to conduct file discovery searches of crucial storage repositories and email servers. The AI algorithm increases its understanding of how to recognize documents and sources, hence expediting the progression of cases and the delivery of findings. Consequently, the period between a filing and the beginning of the CIRP is reduced considerably. In the upcoming time, the interplay of AI in CIRP can augment Professionals’ efficiency whilst slacking the cost simultaneously.

iii. Liquidation

Since the advent of IBC, time-bound liquidation has been a cornerstone that IBC is yet to achieve. As per Insolvency and Bankruptcy Board of India (Liquidation Process) (Amendment) Regulations, 2019, the liquidator is obliged to wind up the liquidation process within one year. However, as per the latest IBBI data, of all ongoing liquidation proceedings, more than 79% have crossed the prescribed time limit. Authorities are making vigorous efforts and hence IBBI’s amendment on Streamlining liquidation came out in June 2022. The inordinate delay in the liquidation process has been mangling with its objective i.e. asset value maximization. IBBI’s redressal proposals such as mandate on a reduced time limit, setting up of a supervisory committee, etc. needs innovative measures to be a success. Incorporating AI and data analytics tools into the liquidation process is one such innovation.

Another key difficulty as per the IBBI paper is inconsistency in the process of e-auction. Various corrective methods such as a designated auction portal, explicit time intervals between succeeding auctions, etc. are proposed by IBBI. However, the execution of these will be a challenging issue for authorities. In such a case, AI can serve the function of a pacifier as there are promising AIs’ that employs Machine Learning and performs optimum auctions. Currently, the problem of time-bound liquidation persists because the liquidator bears the entire responsibility of 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.

Global interaction of AI with insolvency laws

There is an apparent acceptance of AI in law including insolvency in developed countries like the United States and the European Union. Many firms have used artificial intelligence tools like Data 61, DataLex AI, and ROSS which are trained for advanced analytics, modeling, and predicting scenarios based on financial performance which has been proved quite favorable for insolvency laws. LDM Global can be a classic example of the adoption of AI in the invention of its AI tool “Accelerator”, which has assisted the professional service firm in delivering successful results in the insolvency procedure. 

In 2021, the Colombian government passed a decree on enabling the use of artificial intelligence in the management of insolvency processes as a response to the economic crisis under its extraordinary legislative powers. In the same year, the United Kingdom government announced its promised “national AI strategy” to regulate the AI governance in the country conclusively promoting the use of AI in law and enforcement including insolvency laws. Consequently, it can be inferred through these technological advancements that AI has wider amplitude in insolvency law enforcement than it is being preached in India.

Conclusion

Since the advent of IBC, a quick resolution has been its eternal promise to corporate debtors and stakeholders. However, the reality begs to differ as there subsists a backlog of insolvency cases. The global pandemic of Covid-19 exacerbated the backlog and prompted authorities to propose modifications to the current IBC regime. IBBI has already taken some corrective measures, however, acceptance of AI is still missing. Owing to the current scenario, embracing technology like AI is not an option but a necessity. Therefore, it is now time for IBBI to incorporate AI and abandon the misconception that AI would replace lawyers; rather, AI will serve as an efficacious instrument for providing better legal services.

 

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