AI-Driven Risk Mitigation In Financial Services

This article has been written in collaboration with SingularityNET's Ambassador programme.

Introduction

We’ve all been there. Companies of different sizes are challenged with different hurdles. As we embrace technology with open arms, the significance of enhanced security measures cannot be overemphasized. As startups and financial institutions are birthed into the ecosystem, they’re faced with potential risks — which seem unending! Thankfully, machine learning has surfaced as a transformative element for fortifying and managing potential threats.

What is risk mitigation?

It is the process of taking actions to reduce or control the probability of potential risks — Why is this necessary? Risk mitigation helps to minimize the negative effects of uncertainties on financial transactions, business, or any other endeavor. The admittance of machine learning in risk mitigation will enable businesses to gain useful insights and possible solutions for future problems.

Recent Advances in Artificial Intelligence (LLMs)

As great as financial institutions were, we now look into the future. Many people believe that artificial intelligence will save the financial world because it’s already solving some of the most pressing challenges. For example, it is playing a key role in financial risk management — How? It enables experts to leverage existing data to identify market trends, pinpoint risks, and conserve manpower.

Why Should Financial Institutions Consider Leveraging AI in Finance?

Financial institutions need to leverage AI to understand and meet customer demands and provide convenient ways to save, spend, and invest their money.

Bringing better results

There are a few manners in which artificial intelligence can be utilized to optimize results:

1. Fraud activities detection: Over the past couple of years, banks have leveraged machine learning methodologies for credit card portfolios and the good news is that we’ve had a pretty accurate prediction in fraud detection due to the model’s availability to develop and validate enormous volumes of data. For greater benefits, these credit card systems are built with sophisticated machines that monitor card transactions to reduce the rate of fraud. Equally, credit card portfolio systems have a robust transaction history which gives banks more insights into fraudulent and non-fraudulent transactions.
2. Market risk analysis (e.g. credit risk modeling): Decentralized Finance (DeFi) encompasses crypto-based transactions, and financial and exchange services that have a low barrier to entry and therefore allow further access to, for example, loans. As with any financial service, there is a risk of default. To mitigate this risk, financial institutions have integrated AI and machine learning algorithms into their credit risk modeling systems to reduce the likelihood of borrowers defaulting on their loans as the algorithm is able to aggregate and provide extensive data of the borrower’s credit history and financial stability.
3. Decision-making processes: Financial sectors have been hit by a significant number of financial penalties due to their misconduct or mistreatment of customers, financial sectors have started learning, adapting, and implementing strategies that will enable them to serve their customers better. The advent of visiting banks to lay complaints is fast becoming less effective. AI-powered virtual assistants are now fostering customer service and enhancing personalized recommendation systems for providing information, answering customers queries as well as tailored financial advice.
4. Automation in compliance: To conserve manpower, financial sectors are increasingly leveraging automation that enables real-time monitoring and fast response to potential compliance issues. Additionally, reporting and documentation have pivotal importance. Automation allows some activities to happen at shorter and more frequent intervals, it reduces the risk of human errors while also assisting in generating accurate and timely reports required for regulatory compliance.

Risk mitigation in portfolio management

Artificial Intelligence has so much to offer. From a bird’s-eye view, it is clear that asset managers are faced with potential risks while handling a client’s portfolio; thankfully, Artificial Intelligence has come as a transformative force that analyzes vast datasets, market trends, and historical performance to identify patterns to drive improved returns while decreasing risks. Equally, machine learning algorithms allow for the creation of risk models that adapt to changing market conditions while enhancing the overall accuracy of risk assessments, and can be leveraged in asset management for the execution of trades when certain indicators are present, which we have listed here below.

1. Investment strategies — monitoring performance and risks: One sure fire way through which AI is improving investment strategies is by providing sophisticated tools for monitoring performance and managing risks. These technologies have the potential to elevate businesses that are struggling to go from rock bottom to the top; an effective investment strategy involves an ever-growing monitoring of performances and risks to ensure they align with your investment goals. Asset managers continually use these tools to evaluate the returns of an individual’s investments to measure success whilst keeping an eye on the market trends and conditions for any likely risk that may arise.
2. Diversification: Holding a diversified portfolio will eliminate the impact of poor performance in one asset and can be mitigated by the positive performance of other assets . Asset allocation could be very overwhelming, but Artificial Intelligence has significantly revolutionized this aspect by performing tasks that surpass human capabilities and minimizing human errors. AI processes vast amounts of historical data and market trends which are beyond human power due to its complexity - This way the impact of emotional biases will be reduced, then more objective and data-driven asset allocation decisions will be implemented, thereby minimizing human errors in the complex landscape of financial markets.
3. Valuation methodologies: When it comes to the calculation side of things, Artificial Intelligence can significantly enhance Net Asset Value (NAV) calculations to help prevent misconduct in reporting. Valuation methodologies should be fair and consistent when it comes to proper reporting. AI's capabilities to identify anomalies or irregularities in historical data will foster the detection of potential misconduct, including intentional misreporting. This process is significant because it helps to determine the fair value of financial assets within a portfolio and ensures that no party gets unfairly disadvantaged.

Faults in the System

The alliance between Artificial Intelligence and financial services is still in its infancy. Given how the coding of artificial intelligence is still performed by physical persons, it stands to reason that there may be loopholes due to human error which bad actors could target and exploit. These risks, however, remain minimal thanks to the multiple-step verification companies enact before deployment.

Conclusion

The admittance of AI and machine learning algorithms in finance has opened up a new era of paradigm shift. This article covers and explains how this technology is revolutionizing financial services. We discussed the recent advances in Artificial Intelligence, reasons as to why financial institutions should adopt; it also covers the roles it plays in asset management and more! In my opinion, I believe that the financial sector still has a lot more in the bag to unravel and the remarkable journey is just beginning. So, adopting this technology in finance will truly change the way we approach finances.

• AI