The global financial services industry is constantly developing, from rapid technological advancements to new regulations and evolving customer expectations and preferences. For financial services companies to succeed today in a highly competitive environment, striking a delicate balance between fostering innovation and cutting costs is now a must-have.
Traditionally, firms used costly third-party solutions to pinpoint where they could make savings. However, the meteoric rise of artificial intelligence (AI) and machine learning (ML) has now disrupted these age-old and largely inefficient savings processes. AI and ML-driven cost-reduction solutions now offer cost-effective ways to help businesses significantly improve their financial operations while enhancing service quality.
Regionally speaking, AI and ML are significantly driving cost reduction in the financial services sector in the Middle East and North Africa (MENA) region – a welcome trend helping industry players eliminate labour-intensive processes and ultimately leading to significant labour productivity. It’s now becoming evident that most businesses across the expansive MENA region are beginning to realise the global shift towards AI and advanced technologies, with the potential impact of AI only in the region estimated to reach over $320 billion by 2030.
As global investments in AI, for instance, continue to grow, reporting over a 9,000% increase from $0.8 billion to $78 billion in 2021, countries in the MENA region are also tapping into this trend to boost the region’s economic growth. According to analysts, the region’s financial space is poised to become the highest spender on AI technologies, with 25% of all regional AI investments going into the finance sector.
Taking steps towards cost reduction
ML and AI – specifically Generative AI using Large Language Models (LLMs) – can automate tasks, improve productivity, and reduce the need for manual labour. By investing in these technologies, financial services companies in the MENA region gain the added benefits of increasing competitive advantage and improving customer experience that caters to the diversity in the region.
The application of these technologies is constantly evolving and influencing the industry in ways businesses couldn’t have imagined just a few years ago. For instance, some relatively simple AI use cases that can be deployed almost immediately include writing and testing code, helping developers to be more agile and launch new services and products faster.
AI can also provide multilingual customer services that enable customer support representatives to communicate effectively with customers who may not speak a bank or the region’s primary language. The technology can also accurately translate contracts and other business documentation.
In the medium term, AI can further expedite cost reductions in compliance, risk management, and security areas, for example, by personalising complex regulatory requirements and extracting critical insights from vast volumes of text. This would make it easier for financial services firms to understand and comply with regulations. Additionally, the automation of compliance monitoring saves time and mitigates the risks associated with manual compliance. Generative AI can also automate the process of rapidly creating regulatory reports more accurately and consistently than people, helping to cut down time spent manually reporting and the risk of human error.
Once these use cases have been mastered, financial services institutions can use AI to shape long-term strategies. For example, an Enterprise Knowledge Base (EKB) powered by generative AI could take chatbots to the next level. This enables instant and personalised responses to customer queries on transaction history or loan information and even recommending new products, reducing the need for human intervention and helping to sell new features. AI can even automate research and reporting by gathering, analysing, and reporting financial data and market trends for faster decision-making and optimising portfolios by assessing risk, helping to improve returns.
Getting AI-ready
The rise of Generative AI has sparked discussion about new use cases and business benefits. Still, financial services firms must treat their data as a foundational resource to drive real value from the technology. Ensuring data quality and accessibility is crucial, and companies need to start thinking of ‘data as a product’ and treating it as such. This means making data more portable and giving AI access to a complete set of quality data, regardless of whether it sits in on-premises data centres or the cloud. This will build the reliable data sources and pipelines that AI needs to thrive – because it’s only possible to get accurate output from LLMs if the input is of suitable quality.
Overcoming business challenges with AI
Whether automating processes to improve efficiency or AI-driven chatbots to enhance customer service, AI will play a crucial role in the overall cost reduction within the regional finance sector. But to achieve the desired outcomes from AI and drive value from it, it’s essential to train models with data that enables AI to flourish. Deploying a modern data architecture will be crucial in reducing AI costs. This will allow financial services firms to integrate AI into their decision-making processes and enable it to help shape short-, medium- and long-term strategies.
