Introduction

Generative artificial intelligence (AI) has proven to be a game changer across a range of industries and sub-sectors, with emphasis on the financial sector. Generative AI leverages machine learning models, particularly deep neural networks, to create new content based on patterns learned from large datasets. It is a new technology that enables new ideas, complex data analysis and objectively executes rather hard operations; automation of processes, in this case corporate banking and microfinance, inclusive, are changing for good. While implementing such technologies, the financial institutions are eager to reduce the operational costs, increase the positive experience of the clients, and cope with tighter oversight and compliance, which is already at large these days, only Generative AI brings. This paper outlines the usage of generative AI in such institutions as banks and microfinance, including the problems it causes as well as discussing its more implications in years to come over the financial industry in Liberia.

Generative AI in Banking and Micro-Finance

1. Customer Interactions

One of the most important tasks for the financial sector with the help of generative AI is the enhancement of customer interactions. Such technology has recently begun to be applied in banks and microfinance institutions by establishing virtual interfaces that are able to comprehend and respond to customer complaints effectively using natural language. For example, the company JPMorgan Chase has introduced generative AI such as an “IndexGPT,” which helps the clients to quickly understand the lengthy financial reports as it provides key analysis upon the documents (Bloomberg 2024). This application is not only enhancing customer satisfaction but is also saving the human customer-care representatives workload.

Banks and microfinance institutions in Liberia must adopt the combination of GenAI and automation to enable them to automate back-office functions such as loan processing, document verification, and compliance checks, significantly reducing operational costs and processing times. GenAI can effectively process and understand unstructured documents, thus reducing the manual cost of processing as well as required human efforts.

2. Risk Consideration: Assessment, Models and Security

Applications of generative AI also include risk assessment and fraud detection, where these models are expected to prove useful. These models have also been capable of sifting through large volumes of data to detect features that suggest fraud or possible credit risk. For instance, Mastercard has added generative AI to its fraud detection systems allowing merchants to on demand reviews of a suspect transaction within seconds (Mastercard, 2024).

In the Liberian banking sector, one of the biggest challenges in the urban, semi-urban and rural markets is the high default rate on loans, most especially to individuals and SMEs. GenAI can reduce this risk and improve credit scoring process by analyzing historical data on customer creditworthiness, spending habits, and financial history. Especially in microfinance, where people do not have track records of using banks and loaning money, traditional credit scoring systems do not work that well. Instead, generative AI is able to synthesize more information so as to improve the various risk models to allow more people access loans. This enables FIs to reduce the risk of default borrowings and thus offer loans to more underserved groups. As commercial banks and MFIs continue to expand their services, particularly in developing regions, they can use GenAI with fraud detection systems to analyze transaction data in real time, identifying anomalies and suspicious activities. Additionally, GenAI models can simulate various fraud scenarios, including synthetic data and unknown patterns, giving it an edge over traditional techniques.

3. Automation and Adherence to Regulatory Standards

The compliance requirements for financial players are strict. As a result, many compliance-related reports need to be produced by these institutions. Reports of this nature are now created using Generative AI in order to maintain the required standards while decreasing the amount of time and resources necessary to complete the task. For example, it has been reported that Bank of America uses AI in writing and generation of regulatory documents (Bank of America, 2024). The relevance of this application is more for smaller micro finance institutions who have less means to fulfill their compliance obligations.

4. New Product Development and Marketing

Generative AI is also transforming the new product development and the new product marketing process within financial services. For instance, based on the statistics collected from customers and market behavior, AI models could recommend new financial solutions likely to be of use to targeted customer groups. In addition, these models can assist with writing marketing content to complement the cohesiveness of the marketing strategy. For instance, Ant Financial, a pioneer in digital finance in China, is leveraging generative AI to deliver tailor made investment strategies and products to its clients (Qi, 2019).

Issues Relating to Generative AI Adoption

As promising as this software is, the adoption of generative AI in the financial sector is fraught with numerous challenges:

1. Privacy Issues and Security

Financial institutions have very sensitive customer data, and the introduction of generative AI raises privacy and security issues. There is a possibility of making inference attacks or model extracting whereby AIs compromises these by the likelihood of generating sensitive information or being attacked from the outside. For banks and microfinance institutions embracing generative AI technologies, it may be cumbersome to adopt and implement standard data protection policies and even meet various regional or international data protection laws for instance CCPA and GDPR (Reinsel et al., 2018).

2. Ethical, Biases, and related Issues

Lending applications that are often offered through generative AI models tend to integrate some bias from training examples, with the high risk of exacerbating approaches that discriminate against a certain group based on the determination of the training examples used. Effective governance around such deployment must aim to eliminate or reduce these biases since misuse of AI will lead to unjust consequences in society. This is especially serious in the case of microfinance technology whereby AI systems need to be designed for less to never aggravate the prevailing gaps (Mehrabi et al., 2022).

3. Legal Compliance and Explainability Challenges

The problem of explainability, and more broadly the problem of accountability, is particularly pertinent with generative AI models due to their black box nature which is often a required functionality. As AI decisions like credit scoring and risk assessment continue to become mainstream, regulators are requiring that more clarifications be offered by institutions on AI derived decisions. This remains a work in progress in the industry – the design of such AI systems that can be easily construed considering the compliance laws although they are effective (Guidotti et al., 2023).

4. Legacy IT Systems

Still, most of the banks and microfinance institutions utilize outmoded IT systems which may operate on an entirely different platform which even an advanced AI can’t easily penetrate. Deploying generative AI on already existing infrastructure usually comes with high costs and upheavals that might be too difficult for lower tier financial institutions with inadequate support (Deloitte, 2023).

Conclusion

Generative AI has the potential to transform the financial services industry by providing unprecedented opportunities to banks and microfinance institutions to improve business processes, enhance customer satisfaction and product and service innovation. This technology has applications that include, but are not limited to, customer engagement, risk evaluation and compliance without human governance.

Nevertheless, integrating generative AI has its obstacles as well. Data privacy, ethical issues, engaging with regulation as well as technology – all the mentioned problems are typical for financial organizations. To solve any of these problems, it will be necessary to engage the interest of the representatives of all sectors, policymakers, and developers of technologies.

As time passes, it is evident that the AI technology metamorphoses to make the financial sector shift into the next phase where the generative AI is used. Those institutions that know how to gain this technology while curing its exploitative ends will be ready for an even more competitive and complex financial future.

References

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