A global financial crisis can have severe repercussions not only for financial institutions but also for countries and the world as a whole. There have been numerous instances of such crises in the past, highlighting the failure of financial institutions to adhere to best practices, driven by greed and engaging in extremely risky and unauthorized practices.
To effectively learn from the past and navigate the present, it is crucial to have comprehensive and all-encompassing data. And it is artificial intelligence (AI) that can play a vital role in achieving that.
Banks encounter significant and intricate challenges, including compliance issues, data analysis for fraud detection, identification of suspicious loan applications, and more, due to the vast volume of data that needs to be processed.
In comparison to manual processes and workflows, AI-driven automated workflows offer precise, faster, and relentless data processing, generating the necessary analytics.
Lessons From the Past: The Financial Crisis of 2008
The 2008 financial crisis serves as a significant example of the consequences that arise when financial institutions deviate from best practices. During this crisis, many banks disregarded prudent lending guidelines and engaged in risky lending practices.
In the United States, the FICO score is used to assess an individual’s creditworthiness, with a score of 600 and above typically qualifying them for a loan. However, numerous financial institutions ignored this rule and extended loans to applicants with bad or no FICO scores. These loans came with higher interest rates as banks sought to earn quick profits. Additionally, some financial institutions sold loan portfolios to global investors who viewed them as lucrative investments.
As expected, a large number of borrowers defaulted on their payments, leading to a widespread crisis that affected not only the U.S. but also the global economy.
The repercussions of the 2008 financial crisis demonstrated the interconnectedness of financial institutions and economies worldwide. It emphasized the need for better risk assessment, adherence to lending standards, and stricter oversight of financial practices.
2 Common Causes of Financial Crises
- Non-compliance with regulations and norms
The 2008 financial crisis was triggered by numerous banks prioritizing profits over adherence to regulations and norms. They disregarded creditworthiness criteria and approved loan applications from individuals with poor or no creditworthiness. Furthermore, loan portfolios were sold to investors without adequately assessing the risks associated with those portfolios.
In India, certain banks neglected to diligently evaluate loan applications from influential industrialists, sidestepping rules and regulations.
- Insufficient access to comprehensive data
While the possibility of collusion between errant banks and compliance authorities cannot be entirely dismissed, it is important to acknowledge the absence of comprehensive data as a contributing factor.
Banks could have made more informed and secure decisions if they had access to comprehensive data regarding the loan applicants’ credit history and economic background.
This data gap hindered banks from accurately assessing the risks associated with lending and making sound lending decisions.
Role of AI in Preventing Financial Crises
AI can play a crucial role in mitigating financial crises by addressing the factors of inadequate data, compliance ignorance, and erroneous loan application reviews. Below are a few examples of how AI can complement human efforts in preventing financial crises.
Comprehensive data analysis: Leveraging AI-powered data analytics, financial institutions can utilize AI algorithms to swiftly and accurately analyze vast amounts of data. This allows them to gain comprehensive insights into borrowers’ credit histories, economic backgrounds, and risk profiles.
Compliance monitoring: AI systems play a vital role in monitoring compliance with regulations and internal policies. Real-time data analysis by AI algorithms allows for the identification of potential compliance breaches, enabling timely alerts to relevant stakeholders.
Improved loan application review: AI-powered algorithms streamline and enhance the loan application review process. By analyzing diverse data points such as financial statements, credit histories, and market trends, AI systems provide more accurate and objective assessments of loan applications.
Early warning systems: AI contributes to the development of early warning systems that detect potential signs of financial instability or market imbalances. Monitoring economic indicators, market trends, and systemic risks, AI algorithms deliver timely alerts to policymakers and financial institutions, facilitating proactive measures to prevent crises before they escalate.
Risk assessment and fraud detection: AI enhances risk assessment models by identifying patterns and anomalies in financial data. Through machine learning (ML) algorithms, potential risks and fraudulent activities can be flagged, empowering financial institutions to proactively mitigate them.
While AI has the potential to play a significant role in preventing financial crises, it is important to recognize that it is not a standalone solution.
As mentioned earlier, AI complements human efforts in this regard. It equips financial institutions with the necessary tools and data to identify risks and make informed decisions.
However, the effectiveness of AI ultimately relies on the institutions’ ability to implement and act upon the insights provided by AI systems. The human element remains crucial in leveraging AI technology effectively to avert potential dangers and ensure financial stability.