Make capital investment work harder with innovative technology for efficiency across the financial ecosystem

Executive summary 

To understand how inefficiencies in financial systems can impact an organisation’s bottom line, it’s important to have a clear picture of how the global money lifecycle works.
 
Businesses are losing out on an average of US$98.5 million a year as a consequence of cyberthreats, fraud, regulatory hurdles and operational inefficiencies, according to landmark new research from FIS® (NYSE: FIS) in collaboration with Oxford Economics.  

Research of more than 1,000 business leaders across six industries quantified the impact of disruptions and inefficiencies across the money lifecycle, revealing a stark paradox: while 55% of respondents reported that their companies have invested in generative AI and machine learning to meet their strategic objectives, 56% have not yet achieved the expected market agility, with 73% citing the high cost of implementation and maintenance as an obstacle to AI automation, as well as 58% citing integration challenges as their primary barrier.

A notable trend among the executives and business leaders surveyed was the significant investments their organisations are planning to make in AI and automation technologies.

Showing signs of optimism despite obstacles, 56% of respondents said their companies plan to employ AI to increase their organisation’s agility in response to market dynamics, while 48% anticipated it would enable them to gain new customers.

This reveals a fundamental truth: a sophisticated data architecture has become the critical differentiator for alternative investment success. The research, surveying 1,000 senior executives, shows that despite widespread technology adoption, firms struggle with three core challenges:

1. Data acquisition: 51% face data quality issues and are still grappling with paper documents, with an explosion in data and no 100% digital scenario on the horizon, previous generation Optical Character Recognition (OCR) and Robotic Process Automation (RPA) tools are struggling to keep up.
2. Data harmonisation: 37% struggle with system connectivity, with more complex products and services, as well as the push to new distribution channels, interoperability is seen as a critical barrier to scaling.
3. Data consumption: 64% lack expertise, highlighting the challenges in hiring and retaining talent with the right blend of business and technical skills. 

Organisations with a robust data architecture and citizen development framework, demonstrate faster decision making, reduced inefficiencies and superior performance during volatile periods. The question isn’t whether to invest, but how quickly to build a strategic foundation that will make capital investment work harder to unlock growth.

The integration imperative 

Picture a European hedge fund during August 2024’s yen carry trade unwind. The fund’s artificial intelligence (AI) analytics should have provided early warnings. Instead, its collection of fragmented trading and risk systems delivered conflicting signals, while legacy workflows delayed critical data updates. The result: preventable losses.

The survey indicates such problems are widespread. The research shows that while financial firms have invested in AI and machine learning in a bid to leverage their data analysis and signalling possibilities:  

• 56% have not achieved the market agility they expected.
• 48% failed to realise their customer acquisition goals.
• 43% still have not realised targeted revenue streams.

The root cause isn’t technology; it’s the foundation beneath it. Having a modern, unified data architecture to underpin firms’ trading and risk management tools has become a strategic imperative. 

Challenge 1: From fragmentation to integration 

Modern trading operations demand precision across multiple asset classes and time zones. Yet 51% of firms struggle with data quality, particularly when aligning with unstructured documents given many term sheets, confirmations and regulatory filings still arrive as PDFs.

Figure 1: The technology investment-outcome gap

With an ever-growing amount of data to process, hiring more bodies is no longer sufficient, nor economically viable. Optical character recognition and robotic process automation-based technologies, which have been deployed in the last 5-10 years, are now giving way to modern AI which can understand the context and relationships within documents, not just extract text. Instead, industry leaders are implementing:

• Semantic understanding of complex financial documents.
• Natural language voice processing for compliance.
• Real-time validation at point of capture.

Implementing data improvement transformations produces measurable outcomes, including significant reductions in trade breaks, faster regulatory reporting, and improved P&L accuracy through real-time position marking.

A holistic, cross-asset framework for order, portfolio and risk management offers investment firms a further edge. Instead of data silos scattered across disparate trading, risk and compliance systems, a single, multi-strategy, multi-asset class ecosystem can consolidate data to provide complete views of desk- and enterprise-level positions. Advanced trading and risk management tools fed by real-time data will help stakeholders make informed decisions about portfolio adjustments, trading strategies and capital allocation to stay within defined risk tolerances and enable investments to work harder to deliver alpha. 

Challenge 2: Scaling alternative markets 

Once exclusive to institutions, modern technology is now driving a wave of democratisation in alternative strategies. Seeing new and diversified fundraising opportunities, many private equity firms are rolling out semi-liquid funds and hedge funds are offering separately managed accounts and creating products that cater to wealth platforms. European ELTIF structures and US interval funds offer new vehicle options. Minimum investments have dropped from millions to thousands. But democratisation brings exponential increases in operational complexity. The harmonisation challenge encompasses:

• Accommodating multi-class structures with varying terms.
• Cross-border compliance requirements.
• Diverse investor reporting standards.

Research shows almost 40% of firms are exploring market diversification, yet 37% struggle with the requisite system connectivity. To be successful, firms need:

Master data management to support exponential investor growth.
Automated workflows in place of paper-based processes.
Modern architecture that enables distribution partnerships and multi-asset cross-asset and front-to-back platform integration.

Challenge 3: AI-ready foundations – from investment to implementation 

Despite 73% of the Oxford Economics survey respondents citing high AI costs and 64% lacking expertise, accelerating AI adoption is a priority for alternative investment firms. The solution lies in democratising data access. 

Low-code/no-code platforms can transform business users into ‘citizen data scientists’ by breaking down traditional barriers through:

• Visual workflow builders.
• Pre-built AI models.
• Natural language querying.

It enables portfolio managers to build custom analytics without technical support dependency; risk managers to create real-time dashboards; and compliance teams to automate surveillance. 

Maintaining the governance-performance connection will be critical. Data lineage ensures explainable AI, while audit trails satisfy regulatory scrutiny across jurisdictions.

Conclusion: The data advantage in an uncertain world 

The three data pillars – acquisition, harmonisation, consumption – should form an integrated ecosystem. Research validates this approach: 60% experienced enhanced collaboration through technology investment, while 49% fostered data-driven cultures.

Successful implementation requires sequencing, with a focus on:

• Immediate term: High-precision data acquisition tools.
• Medium term: Scalable harmonisation layer.
• Long term: Democratised consumption platforms.

When done well, the return on investment is compelling, materialising through faster market response, reduced manual processes and improved ability to capture opportunity. And as investors diversify globally, multi-jurisdictional complexity makes a flexible data architecture even more essential.

The urgency is clear: competitive windows are narrowing. Early adopters establish compounding advantages while regulatory sophistication increases. Organisations that recognise data architecture as foundational will outperform peers across all market conditions.

The question isn’t whether to invest in data capabilities, but how quickly you can build the integrated foundation that transforms AI potential into measurable business advantage.

Source: FIS and Oxford Economics The Harmony Gap: Finding the Financial Upside in Uncertainty study.