Artificial Intelligence and Financial Services in Africa

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14.10.24 06:55 AM Comment(s)
The financial sector has seen progressive advancements over the years. A trip that started with the advent of technology, the internet and has taken organizations through several stages of digitalisation now to the advent of artificial intelligence. The emergence of emerging technologies has disrupted the industry and led to more innovative solutions and opening up new models of doing business and engaging with customers. 

According to a google cloud blog, AI  in finance helps drive insights for data analytics, performance measurement, predictions and forecasting, real-time calculations, customer servicing, intelligent data retrieval, and more. It is a set of technologies that enables financial services organizations to better understand markets and customers, analyze and learn from digital journeys, and engage in a way that mimics human intelligence and interactions at scale. 

Methodology

The Masterclass at the Africa Law tech Festival 2024 on Artificial Intelligence and Financial Services in Africa was moderated by Susan Otieno, ... The speaker during the session was Janet Otero, an advocate of the High Court of Kenya, practicing under the technology and Telecommunication practice group in the commercial department at TripleOKLaw with a background on corporate, commercial and banking law. 


Session Overview

Masterclass outline 

As outlined by the moderator, a recent survey by Nvidia indicated that 78% of financial service companies reported using at least one form of artificial intelligence tool. Over 30% of these companies noted that AI boosted their annual revenues by more than 10%, while more than a quarter experienced a reduction in costs by the same margin. This highlights the growing integration of AI in financial services, which is transforming the industry by enhancing efficiency, accuracy, and customer experience. The Masterclass intended to explore these developments, with a focus on the challenges of harnessing AI in financial services, as well as the benefits it offers. The goal is to foster discussion, share insights, and generate ideas or recommendations to be compiled afterward.

Speaker presentation 

The speaker delved into the multifaceted role that artificial intelligence  is beginning to play in revolutionizing the financial landscape. Emphasizing the broad spectrum of what can be categorized as "financial services," making it clear that the term now stretches far beyond the traditional roles of banks and financial institutions. Financial services, according to her, now encompass everything from mobile network operators offering USSD-based or app-based services to digital lenders, payment wallets, and even traditional lenders like savings and credit cooperative unions (SACCOs).


“When you talk about financial services, I'm not going to try and box it into statutory descriptions or provisions, I’d like to think of financial services as offered by traditional financial institutions, such as banks, mobile network operators, digital lenders, payment wallets, and even the traditional lenders we have in Kenya, such as SACCOs.”

What to look at in leveraging AI in financial services 

The day-to-day operations of a financial service provider play a crucial role in determining which AI tools and opportunities can be leveraged. These operations range from customer onboarding to risk assessments across various portfolios, including credit, assets, equity, and liquidity. AI has the potential to streamline these processes, improving efficiency and accuracy. For example, credit management services and financial analytics are key areas where AI can offer significant benefits, helping providers better understand their offerings and tailor services for their customers.


"The day-to-day operations of a financial service provider are what actually advise what AI opportunities or tools they could leverage,” she stated, with an emphasis on the seamless integration of AI in diverse operational areas.

Customer service is another critical area, as financial institutions manage customer accounts and finances daily. AI-powered tools can enhance the customer experience by providing personalized, responsive, and efficient service. Marketing, a major component of financial services, is also being revolutionized by AI, enabling targeted campaigns and better customer insights.
“We see operations ranging from customer onboarding to risk assessments for credit and asset portfolios,” Janet explained. "We're talking about credit management services, financial analytics, and customer service offerings—AI plays a significant role in all of these.”

Regulatory requirements are an ongoing concern for financial service providers, especially those under strict regulatory oversight. AI integration will ensure compliance with these requirements by automating reporting and monitoring processes. For providers operating across borders, AI can facilitate smoother cross-border trade and service provision, particularly for those with a presence in multiple African countries.

Internally, AI has the ability to optimize human resource management and corporate governance practices, streamlining internal operations and decision-making. Asset management, whether as custodians of documents or information for clients, is another area ripe for AI integration, ensuring better security and efficiency.
AI Tools to be leveraged in the Financial sector. 

"Most of the AI used within such organizations is likely aimed at automating some processes, and if not automating, then making certain processes more seamless and easier for their customers or staff."

According to Ms. Janet, there are more than 100 AI tools and software applications that financial institutions can use to manage day-to-day operations. Some of the tools she mentioned include generative AI, such as ChatGPT, AI based on functionality like machine learning tools, language processors for translation and speech functionalities, and AI that enables predictive analysis 


“And when you look at the clusters or the types of AI tools available based on an organization's operations, it's easy to find either one tool that serves five purposes or one tool for each specific purpose.”

Most banks and fintechs are looking at enabling digital sign-ups, with many now turning to biometrics and digital onboarding for customers. They are looking to use AI tools for detection and monitoring suspicious transactions, helping financial service providers comply with anti-money laundering regulations. In addition, they are gearing towards automating marketing efforts to better understand and reach customers. AI is also being applied in recruitment and human resource management, from the application process to talent management and turnover. In dispute resolution, financial institutions are looking at exploring AI tools to assess risk, liability, and exposure, helping manage disputes and customer complaints. 

AI Adoption challenges in the financial sector in Africa 

Once each institution understands the various AI tools and systems available and how to incorporate them into their daily activities, what are some of the challenges that can arise during the incorporation, adoption, and integration stages? 

There is no doubt that the adoption and integration of AI in any sector or institution, particularly in financial services, come with numerous challenges. As AI becomes more embedded in daily operations, businesses must navigate several hurdles to successfully incorporate these technologies into their systems. Some of the key challenges and considerations, highlighted in the discussion, that institutions must address as they embark on this AI journey include:


Infrastructure and Capacity Building 

One of the most pressing challenges is ensuring the necessary infrastructure is in place. For AI systems to function effectively, institutions need adequate storage, internet connectivity, and reliable electricity. Without a robust infrastructure, both established and startup financial institutions will struggle to fully integrate AI into their operations. 

The capacity to support AI goes beyond just having the right hardware; it also includes ensuring staff have the knowledge and skills to leverage AI technologies. This dual focus on physical infrastructure and capacity building is essential for the long-term sustainability of AI in financial institutions.


Regulatory and Policy Frameworks 

Another significant challenge is the absence of comprehensive regulatory and policy frameworks that guide AI implementation. The legislative process usually takes several years to develop, during which time AI will continue to evolve rapidly. Given the speed at which AI is advancing and its integration into everyday life, from software like Microsoft Word to smartphone applications, it becomes impractical to wait for formal legislation. 

In the interim, establishing guardrails or parameters based on best industry practices, potentially recommended by international bodies like the United Nations, can help provide a temporary solution. These guardrails would ensure that AI providers and users operate within a defined set of rules, offering a level of compliance and safety while the legislative process catches up.


Data Privacy Concerns

Data privacy is another major concern in the adoption of AI. AI systems rely on vast amounts of data, including personal and transactional information, to generate accurate models. However, institutions must balance the need for AI innovation with respecting data privacy regulations, such as the Data Protection laws or similar regional frameworks. 

Ensuring that AI systems comply with these regulations is crucial in maintaining consumer trust and safeguarding personal information. Institutions must develop policies that protect privacy while still allowing AI systems to function effectively.


Interoperability of AI Tools

A common challenge for organizations is ensuring that the AI tools, software, and applications they adopt are interoperable across different departments. For example, if the human resources department implements an AI system, it should be able to communicate seamlessly with the finance and marketing departments. 

Without this level of integration, the full potential of AI cannot be realized, leading to inefficiencies and fragmentation within the organization. Institutions must prioritize AI solutions that consider the specific needs of the African and Kenyan markets, ensuring that these tools align with regulatory requirements and the unique behaviors of local consumers.


Bias, Ethics, and Human Rights Considerations 

As AI tools become more prevalent, concerns about bias and the ethical implications of AI systems must be addressed. Institutions must ensure that AI respects human rights and aligns with their business goals. 

This involves regularly assessing AI models to ensure they are not biased against any group and that they promote fairness. Plus, AI systems should be adaptable, allowing for adjustments based on user feedback to better meet the needs of the market.


Siloed AI Systems 

The risk of implementing AI solutions that operate in silos, where individual departments use AI without integration into the larger organization. Siloed systems make it difficult to achieve company-wide buy-in and may limit the overall effectiveness of the AI. 

To avoid this, institutions need to ensure that AI solutions align with the organization’s broader business strategy. This requires coordination across departments to ensure that AI tools serve the entire organization rather than just specific functions.


Risks Associated with AI use in the financial sector 

  • Intellectual property risks of ownership and potential disputes over copyright and intellectual property from AI collaborations.
  • Unfairness and bias from AI systems and tools, especially looking at unfair credit scoring, loan approvals, and debt collection practices. 
  • Non-compliance with data privacy laws when AI tools handle personal data, leading to legal and regulatory issues. 
  • Misuse or unethical use of AI tools due to inadequate internal policies and security measures. A look at the internal policies and procedures to just make sure that the staff are using the AI tools without any maliciousness, or are using it outside of the purpose for which it was intended 
  • AI tools not aligning with sustainability and environmental compliance requirements, impacting corporate responsibility. 
    “AI are we using AI for sustainability? Are we using it for growth? Are we using is to be environmentally compliant as part of our obligations as a financial service provider”

    • AI tools failing to perform their intended functions effectively, leading to inefficiencies and potential operational issues.
    • AI tools not scaling effectively across the enterprise or failing to provide valuable insights for business strategy beyond customer-facing roles. 
    • Poor user experience with AI tools, resulting in low adoption rates and suboptimal outcomes. 
    • Risk of insufficient oversight and accountability
    “Are we looking at a situation where we are seeing a new role being created, that of an AI officer? Somebody who's responsible and accountable for how this thing is being used internally and externally, and must also be the one interfacing with any regulator in terms of how safe and ethical this tool is being used internally.”

    • Risk of AI being used inappropriately or ineffectively if it is not aligned with the organization’s goals, particularly in comparison to its necessity in other industries