How to unlock the AI productivity promise

The financial services sector is undergoing a significant transformation with the rapid adoption of artificial intelligence (AI). Recent studies indicate that AI is becoming an integral part of business operations across the industry. Alveo recently conducted a survey to poll the industry about the state of adoption, opportunities and challenges in adopting gen AI in enterprise data management.

Statista research from 2023 found that only 8% of financial businesses considered AI critical to their business in 2022 but by 2025, the expectation is that 43% will consider this to be so. Currently, nearly all financial services organisations are using AI in some capacity but there is a clear split between individual or departmental level experimentation versus more systemic adoption. 41% of firms have extensively deployed AI across different business operations, according to Alveo’s research, indicating a growing trend towards more comprehensive adoption and embedding into workflows.

AI’s implementation in the financial services sector offers numerous benefits, enhancing operational efficiencies, risk management, customer service, and product development. By automating routine tasks, AI allows financial institutions to process large volumes of data rapidly and accurately, reducing human errors and freeing up human resources for more complex tasks. This capability is particularly beneficial in areas such as operations but also in finance and risk management.

For example, AI-driven systems can handle vast amounts of transactional data, identifying discrepancies and potential fraud or money-laundering in real-time. This not only improves operational efficiency but also enhances security and compliance. Additionally, AI’s ability to analyse customer data enables personalised customer interactions, improving customer satisfaction and loyalty. Financial institutions can offer tailored financial products and services, enhancing the overall customer experience.

AI also plays a crucial role in risk management by analysing vast datasets to identify patterns and potential risks that might be overlooked by human analysts. This predictive capability helps in proactive risk management and helping firms cope with increasing regulatory reporting requirements: in many banks a large portion of staff is needed to KYC and compliance and change budgets have necessarily been skewed towards regulatory compliance. AI adoption could help with more effectively KYC and reporting. Moreover, AI’s ability to provide insights into market trends and customer behaviours can guide strategic decision-making, offering a competitive edge to financial institutions and increasing productivity by tailoring information collection and curation to specific user roles.

Furthermore, AI can enhance portfolio management by analysing market conditions and predicting asset performance, helping institutions optimise their investment strategies. By leveraging AI, financial firms can improve their decision-making processes, reduce operational costs, and increase their overall efficiency. The transformative power of AI lies in its ability to convert large amounts of raw data – both traditional market and reference data, as well as an increasing number of ‘alternative’ data sets – into actionable insights that drive business growth and innovation.

However, integrating AI into financial data management is not without its challenges. One clear impact seems to be an increasing premium on good quality data and data provisioning capabilities to feed the models. This will lead to increased data and technology cost which is only partially offset by a decrease of expected operations cost base; in Alveo’s research sample, 63% of senior decision-makers in financial services anticipate an increase in data costs due to AI. Furthermore, 40% expect a rise in operational headcount, while 81% foresee increased IT spending in data management.

Despite the promising benefits, several barriers impede AI adoption in financial services data management. Technological limitations are identified by 50% of decision-makers as a significant barrier. Financial institutions often struggle with legacy systems that are incompatible with modern AI technologies. These outdated systems require substantial upgrades or replacements, which can be costly and time-consuming. Ensuring a seamless integration of AI into these systems necessitates a strategic overhaul, involving significant investment in new technologies and infrastructure.

Another major challenge is the ongoing lack of skilled personnel. Implementing and managing AI systems demands expertise in both new technology and the financial services domain. This combination of skills is scarce, with 46% of respondents highlighting a shortage of skilled professionals as a critical obstacle to implementing AI in financial data management. Financial institutions need professionals who understand the deployment and integration into existing workflows of AI algorithms and can apply them to the financial services domain. Addressing this skills gap requires targeted training and recruitment strategies.

Data quality and licensing issues also loom large. ensuring high-quality data is vital for effective AI implementation, as AI systems rely heavily on accurate, consistent, and timely data. Poor-quality data can lead to incorrect predictions and decisions, undermining the effectiveness of AI applications. Additionally, licensing and compliance issues further complicate data management, especially with the advent of generative AI and the evolving legal frameworks around data usage. Financial institutions must navigate these complex legal landscapes to ensure they are using data ethically and legally.

Furthermore, the potential for AI bias and discrimination presents another significant challenge. AI systems learn from historical data, which can contain biases that are inadvertently incorporated into the models. This can lead to unfair outcomes, particularly in areas such as credit scoring and loan approvals. Regulatory frameworks on the use of AI in financial services are coming with the EU’s AI act and its risk-based classification of risk levels for AI systems as the most salient example. Financial institutions must implement robust fairness and bias mitigation strategies to ensure their AI systems produce equitable and non-discriminatory results.

By taking a strategic, informed approach, financial institutions can harness the power of AI to drive efficiency, innovation, and growth

To achieve the productivity increase that AI promises, financial institutions need to focus on interoperability between AI models and their existing workflows. This involves improving the way they provision models and broadening the traditional notions of data quality and data governance.

Traditional machine learning involved feature engineering, or preparing and tuning the data to “give the models a hand.” The new models make for a very different, natural language-based interaction with business users which calls for training in prompt engineering or the natural language patterns to interact with models, as well as a good understanding of model limitations and risks.

A proactive approach to AI adoption emphasises the importance of improved data quality, provisioning, and governance. Financial institutions should invest in advanced data management technologies to support AI requirements. This includes data aggregation, cleansing, and validation systems to ensure data accuracy and relevance. Developing a skilled workforce is also essential. Targeted training and recruitment strategies are needed to bridge the skills gap, with institutions investing in upskilling existing employees and attracting new talent proficient in AI technologies and financial data management.

A proactive approach to AI adoption emphasises the importance of improved data quality, provisioning, and governance. To optimise their use of AI, financial institutions should first of all, invest in data management technologies: Enhancing data management infrastructure to support AI requirements is crucial. This includes advanced data aggregation, cleansing, and validation systems to ensure data accuracy and relevance.

It is also important that financial services decision-makers collaborate with experts. Partnering with AI and data management experts can provide the necessary guidance and support to navigate the complexities of AI integration, ensuring a smoother transition to AI-driven operations.

The shift towards AI results in increased costs in data management and technology. Alveo’s research indicates that 81% of firms expect a rise in IT spending due to AI. However, these costs can be offset by the long-term operational efficiencies and productivity gains AI brings.

Financial institutions should maintain transparency by clearly documenting the sources and training data for AI models to ensure accountability. Regularly reviewing and updating content licensing agreements to align with evolving legal landscapes and ensure compliance with data usage regulations is also crucial.

Additionally, financial institutions should implement continuous monitoring and auditing of AI systems to ensure they operate as intended and comply with regulatory standards. This involves establishing clear performance metrics and regularly evaluating AI models against these benchmarks. By maintaining rigorous oversight, financial institutions can detect and address any issues promptly, ensuring the reliability and effectiveness of their AI systems.

Furthermore, AI can enhance portfolio management by analysing market conditions and predicting asset performance, helping institutions optimise their investment strategies. By leveraging AI, financial firms can improve their decision-making processes, reduce operational costs, and increase their overall efficiency. The transformative power of AI lies in its ability to convert vast amounts of raw data into actionable insights that drive business growth and innovation.

Generative AI is set to revolutionise financial data management by producing synthetic data for various use cases, including model testing and scenario management. This capability allows financial institutions to test their AI models under a wide range of scenarios without risking real data. However, this requires clear guidelines on data usage and compliance to avoid legal and ethical pitfalls.

Financial institutions should maintain transparency by clearly documenting the sources and training data for AI models to ensure accountability. They should also regularly review and update content licensing agreements to align with the evolving legal landscape and ensure compliance with data usage regulations.

Generative AI also presents opportunities for creating new financial products and services. By leveraging synthetic data, financial institutions can explore innovative solutions that were previously not possible due to data constraints. This opens up new avenues for growth and competitive differentiation. However, the ethical use of generative AI must be prioritised to avoid potential biases and ensure fair and equitable outcomes.

To maximise AI’s benefits, financial institutions need a strategic approach that combines investment in technology with a focus on human capital. This involves continuous learning and adaptation, establishing feedback loops for continuous improvement in data quality and model performance. Combining high-level strategic oversight with grassroots-level adjustments based on real-time data and user feedback ensures a comprehensive and effective AI integration.

Moreover, financial institutions should foster a culture of innovation and experimentation, encouraging employees to explore new ways of leveraging AI. By promoting a mindset of continuous improvement and adaptation, organisations can stay ahead of the curve and capitalise on the latest advancements in AI technology. This proactive approach will enable financial institutions to drive sustainable growth and remain competitive in a rapidly-evolving industry.

The journey towards AI integration in financial data management is challenging but essential for future competitiveness. By addressing the key barriers, enhancing data quality and governance, and adopting a strategic approach, financial institutions can unlock the full potential of AI. This transformation promises increased efficiency, innovation, and growth, positioning firms at the forefront of the digital age in finance.

The future of financial data management is intertwined with AI, and those who navigate this transition wisely will emerge as leaders in the industry. As AI continues to evolve, financial institutions must remain agile and proactive, continuously refining their strategies to harness the transformative power of AI effectively.

In conclusion, while the path to AI adoption in financial data management is fraught with challenges, the potential rewards are immense. By taking a strategic, informed approach, financial institutions can overcome these hurdles and harness the power of AI to drive efficiency, innovation, and growth. The future of financial data management is undeniably intertwined with AI, and those who navigate this transition wisely will emerge as leaders in the new era of finance.