Published by Jessica Weisman-Pitts
Posted on November 12, 2024
8 min readLast updated: January 28, 2026
Add as preferred source on Google
For businesses operating in the financial services sector, it’s a continuous balancing act between keeping the lights on and innovating to meet changing customer demands and expectations. Limited budget increases mean that more of the funds needed to invest in
For businesses operating in the financial services sector, it’s a continuous balancing act between keeping the lights on and innovating to meet changing customer demands and expectations. Limited budget increases mean that more of the funds needed to invest in emerging technologies must be found within existing operations.
In a recent survey of 255 CFOs and CIOs in financial services across EMEA, emerging technologies are found to be a top priority on the leadership agenda. The wider survey of nearly 3,000 respondents across various industries in EMEA found that eighty-eight percent are either already investing in tools like AI, or are planning to do so this year, as improving efficiency remains a major focus.
In this Q&A, Johnathan Bangura, VP & Head of Financial Services, Industry Solutions at Rimini Street, looks at the increasing interest in AI for financial services and how to put AI to work effectively for the organisation.
Questions & Answers:
AI is reshaping the financial services industry, transforming how organisations operate, make decisions and engage with customers. While there are many applications of AI — with more arising every day — I will share two examples.
First, AI models can analyse large volumes of data to help financial institutions make better, data-driven decisions. AI algorithms can identify patterns in transactions, customer behaviour and market trends, allowing banks and investment firms to assess risk more accurately.
Second, AI enables financial services organisations to offer more personalised, real-time experiences to customers. Chatbots, virtual assistants and automated support systems provide 24/7 assistance, addressing routine queries and transactions, improving customer satisfaction and reducing operational costs.
However, data accuracy is critical before implementing AI, as AI models rely on high-quality data for accurate insights. The finance and tech leaders we surveyed recognise this: A combined ninety-three percent say their data needs substantial or moderate clean-up to succeed with AI, which puts improving data quality high on the agenda. Organisational leaders must prioritise this if they wish to succeed with AI.
Perhaps somewhat ironically, given the last question, once banks have improved the data quality, one of the potential use cases is deploying AI to maintain data accuracy and reliability.
From detecting and correcting errors, inconsistencies and duplications to real-time monitoring for anomalies or quality issues, AI can be deployed to maintain data integrity in financial services institutions. It also plays a role in fraud detection, analysing transactions to identify and flag suspicious activity for review. Another way AI can be applied is in assessing market risks and managing investment portfolios, allowing companies to make more informed decisions based on predictive analytics.
No matter how AI is applied, it’s important to establish and maintain guardrails that are overseen by humans, rather than relying solely on technology. Regular quality assurance checks and assessments ensure that the data remains relevant and that AI models aren’t generating false insights.
There are three main ways AI can help improve data security and compliance.
First, AI can help identify vulnerabilities in cyber defenses, allowing organisations to proactively address weaknesses and strengthen overall data security. Patches and vulnerabilities in existing software are often overlooked, especially as the number of solutions in an enterprise grows. Manually identifying and updating them is impractical, but AI can detect and identify areas to be addressed. By continuously learning, AI tools can stay updated with new vulnerabilities and other external threats.
Second, applying predictive analytics to historical data can enhance data security and compliance by identifying potential risks and vulnerabilities before they escalate. AI models can predict and flag potential security threats or compliance breaches, such as unauthorised access or fraudulent transactions.
Third, AI tools can monitor regulations and highlight changes by continuously analysing regulatory updates, legal documents, and industry guidelines and flagging relevant changes in real-time. This reduces the risk of non-compliance, improves the speed of adapting to regulatory changes and ensures that organisations remain aligned with evolving laws and standards.
It’s important to remember that the barrier to entry has dropped for both financial services institutions and criminals that target them. From model poisoning and deep fakes to being able to execute more common attacks — such as ransomware — faster and on a greater scale than ever before, the threats are multiplying.
Therefore, institutions must embed security, not just into the tools they use but throughout their entire culture. Data must be securely managed both for storing and sharing, while models should be continuously assessed for signs of manipulation. Data governance practices must be maintained and reviewed regularly.
AI technologies like natural language processing (NLP) and speech recognition allow financial institutions to analyse unstructured data and extract meaningful information. However, the challenge lies in effectively managing and processing this data to ensure high-quality data.
One of the key benefits of AI is the potential to deliver hyper-personalised experiences at scale. This could involve analysing real-time customer interactions — whether text or voice — and providing sentiment analysis or using generative AI to produce content tailored to the customer’s context.
In both cases, AI needs to process both structured and unstructured data from multiple sources. It’s important to remember that AI performance will continue to improve as long as firms train their models using high-quality, trusted data. The better the data, the better the results.
Financial services firms recognise the need for innovation to stay competitive. Investments in AI can drive efficiencies, making it easier, faster and less expensive to run day-to-day operations, while freeing up resources (people, time and money) to realign to more strategic priorities.
Looking ahead, I see great potential in AI’s capability to advance financial inclusion and reduce poverty by improving access to essential financial services to underserved communities. By leveraging AI, financial services can become more accessible, affordable, and personalised, driving greater inclusion. Removing barriers to financial access empowers people to break the cycle of poverty and to build a path to long-term prosperity.
However, being future-looking isn’t just about considering the positives. The increased use of AI should bring a heightened focus on avoiding unintended bias and ensuring that models and tools are as ethical as possible.
One final point is that while the best practices for adopting AI are similar to other enterprise technology projects, one key difference is the speed at which AI is evolving. Financial services companies have less time to wait and see if AI is right for them. Delaying adoption could mean missing out on significant opportunities for growth, efficiency and competitive advantage. And ensuring organisations spend more of their focus on activities and investments that can help them harness these technologies today, rather than years down the road through resource straining upgrades, migrations and reimplementation, that is what will separate the winners from the laggards.
Johnathan Bangura
About Johnathan Bangura
Johnathan Bangura, Vice President & Head of Financial Services, Industry Solutions at Rimini Street, is responsible for developing global strategy to build and grow revenues across the financial services sector, supporting growth initiatives across all geographies and serving as a thought leader for all things related to the financial services industry both internally and externally.
Johnathan joined Rimini Street in early 2024 with more than 25 years of success in the banking and financial services industry as CFO and advisor to CEOs, CIOs and other C-suite executives, providing strategic guidance, expert advice on banking innovation, business process automation, digital transformation and best-practice eBanking services and solutions.
About Rimini Street, Inc.
Rimini Street, Inc. (Nasdaq: RMNI), a Russell 2000® Company, is a global provider of end-to-end enterprise software support and innovation solutions and the leading third-party support provider for Oracle, SAP and VMware software. The Company offers a comprehensive portfolio of unified solutions to run, manage, support, customise, configure, connect, protect, monitor, and optimise enterprise application, database, and technology software. The Company has signed thousands of contracts with Fortune Global 100, Fortune 500, midmarket, public sector and government organisations who selected Rimini Street as their trusted, proven mission-critical enterprise software solutions provider and achieved better operational outcomes, realised billions of US dollars in savings and funded AI and other innovation investments. For more information, visit: www.riministreet.com.
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines programmed to think and learn. In finance, AI is used for data analysis, risk assessment, and improving customer interactions.
Data security involves protecting digital information from unauthorized access, corruption, or theft. In financial services, it is crucial for maintaining customer trust and compliance with regulations.
Compliance refers to the process of adhering to laws, regulations, and guidelines relevant to an organization. In finance, it ensures that institutions operate within legal frameworks to avoid penalties.
Unstructured data is information that does not have a predefined data model, making it challenging to analyze. Examples include text, images, and videos, often requiring advanced tools like AI for processing.
Operational efficiency is the ability of an organization to deliver products or services in the most cost-effective manner without sacrificing quality. It is essential for maximizing profitability in financial services.
Explore more articles in the Technology category
