INTRODUCTION
The financial services industry in India faces constant regulatory changes to protect public funds. Institutions must frequently update their practices to comply with extensive regulations from the Reserve Bank of India (RBI) and daily compliance requirements to avoid penalties. This is complicated by varying standards across countries for global companies, leading to high compliance costs and resources.
Smaller organizations invest heavily in technology, training, and monitoring systems. In India, banks often take up to a year to onboard loan coordinators, requiring extra staff to verify compliance tasks. The search process which involves sifting through multiple documents and laws is not only time-consuming but also prone to human error. Manually keeping up with ever-changing regulations is not only labour-intensive but also prone to mistakes, which can lead to severe penalties and reputational damage. This highlights the need for innovative solutions to streamline compliance and reduce risks.
UNDERSTANDING LEGAL RISK AND COMPLIANCE
Definition and Scope
Compliance risk, as defined by the Basel Committee on Banking Supervision in its 2005 guidance, refers to the risk of legal or regulatory sanctions, material financial loss, or reputational damage that a bank may suffer due to failure to comply with applicable laws, regulations, rules, standards, and codes of conduct. Traditionally considered a subset of operational risk under Basel II, compliance risk has evolved into a distinct and significant function within banks.
Compliance can be divided into two types: regulatory and internal. Regulatory compliance involves following laws and standards set by governing bodies and agencies. Internal compliance requires adherence to a company's own policies and procedures to ensure operational integrity and alignment with external regulations. Machine learning, with its speed and scale, can significantly assist in reviewing documents and ensuring compliance.
Key Responsibilities
The responsibility of the legal risk team in the banking and insurance sector includes several key roles: avoiding penalties by ensuring compliance with regulations, preventing defaults in loans through diligent oversight and risk assessment, mitigating litigation risks by proactively addressing potential legal issues, combating document fraud by implementing stringent verification processes, and protecting against identity theft through robust security measures.
Importance of Historical Data
Historical data is a crucial factor for assessing legal risk because it provides the best indication of the probability of future legal risks materializing, allowing financial institutions to base their capital requirements on past experiences. By analysing patterns, frequencies, and magnitudes of previous legal issues, institutions can make informed predictions and allocate appropriate capital reserves to cover potential legal risks. However, managing and organizing this data is a mundane, time-consuming task that can be efficiently handled by Automation allowing human resources to focus on strategic aspects of risk management.
THE ROLE OF AI AND AUTOMATION
Last year, JPMorgan Chase employed machine learning to review commercial loan agreements, which used to occupy staff for 360,000 hours every year. The bank is not only saving enormous manual effort and time but is also reporting lower error rates. JPMorgan Chase is now planning to deploy machine learning in more complex areas, such as credit default swaps and custody agreements.
Let’s go over some use-cases of AI which can be useful to the financial sector-
1. Simplification of Procedure
In India, institutions like the Reserve Bank of India (RBI) frequently issue changes in compliance requirements. Even the RBI in its Master Circular “Reflections: Challenges in Regulations - M. Rajeshwar Rao” dated Nov 16, 2023, acknowledged that “certain regulations becoming complex with concomitant increase in compliance burden”. AI can play a transformative role here by simplifying the procedure for compliances as it can consolidate multiple RBI guidelines and regulatory documents and present them to compliance officers on a single query.
2. Real-time Monitoring
AI systems can continuously monitor RBI updates and regulatory changes in real-time. It can highlight the changes taken place along with the in the new guideline as well as can show the areas where the changes need to be implemented. This capability enables banks and financial institutions to receive prompt notifications of new rules or amendments, facilitating timely adjustments to their compliance practices. AI could aid in audits to check whether all the requirements are adhered or not by asking a question for a particular compliance rather than flipping through multiple pages.
3. Minimizing Human Tasks
Through minimising the task of human in compliance process, AI has the potential to assess the legal team in avoiding penalties as almost 90% of penalties on financial institutions are due to non-compliances. However, AI has more potential, and it can enter in other arenas as well for to aid in mitigating legal risk. One of such area in default in loans as institutions incurred huge expenses in recovery of it through legal means so they try to avoid them in the beginning itself.
4. Document Summarization and Query Response
AI can significantly aid in mitigating loan defaults by summarizing and presenting documents based on specific queries. For instance, when dealing with voluminous borrower documents, AI can quickly provide concise overviews, allowing the team to assess the status of loans and identify potential risk areas without manually sifting through voluminous paperwork. Furthermore, if the team requires information on a specific loan or borrower, AI can retrieve and present relevant documents and summaries. This feature enables the Bank to access crucial information promptly and timely interventions to prevent loan default.
5. Analyzing Historical Data
AI tools can generate summaries of past loan performance data upon request, highlighting patterns and trends that may indicate a higher likelihood of default. The risk manager can easily analyse the historical data which is essential for developing effective risk mitigation strategies for specific borrowers or loan types.
6. Developing Strategies Post-Default
AI is useful not only in preventing defaults but also in developing strategies after defaults occur. It could assist by classifying the nature of the dispute and identifying the most effective course of action. All of the dispute resolution strategies of an institution that employs AI can be stored in the software. The institution has the ability to make use of the previous strategies for decision-making if a comparable dispute arises in the future. For example, an insurance company that is confronted with a substantial claim payment dispute because of a natural disaster can ask question from a software to know about past strategies, employed and determine the most cost-effective course of action, thereby reducing legal expenses and saving time.
CONTRACT MANAGEMENT
Contract management is an additional prospective domain in which AI could establish itself. In a single day, financial sectors engage in numerous contracts. In an environment where time is a money, the management of these contracts and the extraction of pertinent information to mitigate risk are challenging and time consuming. There are some use cases of AI for contract management as well in the following part-
1. Extracting Critical Information
New AI tools are capable of extracting data from a variety of file formats, including PDFs, images, and word processors. It also facilitates the extraction of critical information, including stakeholder names, expiration dates, legal clauses, and contract terms and conditions.
Following the extraction, it is possible to access any clause of a contract by asking a simple query. The information that has been extracted can be used for a variety of purposes, such as sending deadline notifications, identifying ambiguous language for legal review, or sending reminders to clients.
2. Organizing and Maintaining Contracts
Automation tools are capable of recognising key contractual provisions, frequently disputed terms, and high-risk legal areas. This allows businesses to make informed decisions and negotiate contracts without the need to go through lengthy and multiple contracts. AI-powered contract management tools can be developed to have the capacity to organise and maintain extensive contract libraries, which can be used to evaluate the location of specific contracts or data. This method reduces the likelihood of errors or oversights and saves time. AI can autonomously categorise and organise documents by uploading them, thereby consolidating pertinent information for convenient access.
3. Generation and translation of Contract Drafts
Additionally, AI can aid in the generation of contract drafts from pre-established templates and user-defined parameters. It reduces the time to compose contracts and ensuring the accuracy of the document. It can further aid in speedily translation of contracts into multiple languages, thereby giving the opportunity of collaborating with international clients. These tools has the potential to summarise contracts by highlighting key points, enabling the risk management team to rapidly comprehend the primary concerns and make well-informed decisions without the need to read entire documents.
CONCLUSION
Adopting AI and automation in the financial services industry is essential for keeping up with the complex rules and regulations. These technologies help banks and financial institutions stay compliant, reduce risks, and save time by automating tasks like monitoring updates, summarizing documents, and managing contracts. By using AI, institutions can avoid penalties, better handle loan defaults, and manage their numerous contracts more efficiently. As AI technology continues to improve, it will become even more crucial for financial institutions to use these tools to stay competitive and effective in managing legal risks and compliance requirements.
