AI in Financial Services

Navigating the IT function within financial services in today’s world is no mean feat, even for the most experienced. For IT leaders, managing risk is front and centre of their role and with rising cyber threats and complex, often disconnected IT systems, the need for robust ERP support is more important than ever. 

In a recent Censuswide EMEA-wide survey of 255 CFOs and CIOs in financial services, Rimini Street.revealed that emerging technologies are a key priority, with 87% of respondents either already investing in AI tools or planning to do so within the year. The strategies are focused on continued evolution rather than radical transformation.

Here, Jonathan Bangura, VP and Head of Financial Services, Industry Solutions at Rimini Street looks at how firms can protect themselves against some of the vulnerabilities in regulatory compliance, mitigating enterprise risk, and solving operational challenges related to AI.

How has AI helped address challenges related to data management within your organisation?

AI has become a powerful tool at Rimini Street, driving improvements in data management, operations, and beyond. Our patented Case Assignment Advisor AI application analyses several data points about cases and recommends the most suitable engineers based on factors like their track record with similar cases, client satisfaction and availability. What used to take a manager 20 minutes is now typically completed in under four seconds, resulting in a 23% reduction in case resolution times for our clients.

Another example is C-Signal, which uses natural language processing (NLP) and other indicators to assess ongoing cases. This innovation has helped reduce the number of client cases that escalate in urgency by 29%.

We are committed to client success at Rimini Street, and AI empowers our engineers to provide unparalleled support, ensuring the highest standards of speed, quality, and attention to detail.

How does AI contribute to improving data security and compliance measures within financial institutions?

AI plays a crucial role in several key areas:

• Regulatory compliance: AI can help financial institutions stay ahead of regulatory changes by continuously monitoring updates and ensuring processes comply with the latest standards. This is essential in today’s complex and constantly evolving regulatory landscape.

• Enhanced risk management: AI’s predictive analytics capabilities can enable financial institutions to assess and manage risk more effectively. By analysing historical data and identifying trends, AI provides valuable insights into potential risks, allowing leaders to make more informed decisions.

• Improving data security: AI can strengthen cybersecurity by quickly identifying vulnerabilities and responding to threats faster than traditional methods. Machine learning algorithms continuously adapt to new data, enhancing their ability to detect and mitigate cyber threats.

AI’s overall ability to process and analyse large data-sets quickly and accurately makes it an invaluable tool for enhancing data security and ensuring compliance. However, for AI to be effective, the quality of the data is crucial. In our survey of CFOs and CIOs in financial services across EMEA, we found that 67% of CIOs and CFOs at mid-sized to large enterprises agreed that their historical ERP data required a significant cleanse before it could be successfully leveraged by AI.

What emerging trends or developments in AI-driven data management do you anticipate will shape the future of financial services?

Financial services, traditionally known for being risk-averse, are becoming more proactive. Our survey revealed that 71% of CFOs are increasing their corporate IT budgets, signaling the industry's openness to technological innovation, including AI.

Looking ahead, several emerging trends in AI-driven data management are set to shape the future of financial services. AI can enhance personalised services, improve fraud detection, and strengthen regulatory compliance. Other trends to watch include:

• Ethical AI and bias reduction: As AI continues to play a larger role in financial services, there will be an increasing emphasis on ensuring AI systems are ethical and unbiased. This involves creating algorithms that are both transparent and fair.

• AI and blockchain integration: The combination of AI with blockchain technology can strengthen data security and transparency. This integration enhances transaction verification processes and helps reduce the risk of fraud.

How is AI being utilised to enhance data quality and accuracy in financial services?

Some examples include:

• Real-time data monitoring: AI can continuously monitor data streams for anomalies and quality issues, enabling immediate corrections and supporting the preservation of data integrity.

• Data profiling and cleansing: AI algorithms can automatically identify and rectify errors, inconsistencies, and duplicates in financial data, helping to maintain high-quality data.

• Fraud detection: AI systems can analyse transaction patterns to identify and flag suspicious activities, improving the accuracy of fraud detection.

Applications like these help financial institutions maintain reliable and accurate data, which is essential for informed decision-making and regulatory compliance. Leveraging data through AI offers a unique opportunity, but it’s crucial to establish a strong quality assurance process that includes human oversight. When AI and human input work together, they ensure that data is well-structured and easily interpretable, enabling a deeper understanding of customers, rates, ROI, and assets. This, in turn, has a significant impact on the day-to-day operations of a business.

How will AI evolve in its ability to handle unstructured data sources, such as text or voice data, in financial services?

AI's potential in this area is evolving at a rapid pace, with significant advancements already being made. Two key areas where AI is proving particularly effective, are:

• Personalised customer experiences: By analysing customer data, AI can offer personalised recommendations, advice, and experiences in a matter of seconds, tailoring services to individual needs.

• Customer service analysis: AI has become skilled in interpreting sentiment, allowing for more efficient customer service. It can quickly analyse service transcripts, highlight important details, and flag potential risks, all of which help customer support teams act faster and more accurately.

What impact has AI had on data governance frameworks and practices in financial institutions?

As AI continues to gain momentum, it is important to prioritise the governance of our data frameworks. Financial institutions must be mindful of how they utilise and feed data into AI models, ensuring robust security measures are in place. It is equally important to establish clear agreements and policies with partners and providers around ownership and usage of data and content.

While AI has created new opportunities for financial services firms, it has also introduced fresh challenges. Institutions must remain vigilant, as mitigating risks requires agility in securely sharing and storing data, and focusing on data quality to ensure that GenAI models produce accurate results.

Ultimately, AI is revolutionising data governance by improving data quality, enhancing compliance and strengthening risk management practices. As technology evolves, its  influence on data governance is expected to grow, driving even greater efficiency and effectiveness within financial institutions. Having the flexibility in being able to adapt and change, whilst protecting themselves against risk, will be the key to success for any financial services business.