Tech adoption will be the great differentiator in business over the coming years, but which technology trends look likely to make the biggest impact to the alternative investment market? Big data, advanced analytics, AI and blockchain all seem set to shape the future funds landscape, with fund managers focusing on operational efficiencies and competitive advantage.

Emerging technology can power everything from predictive analytics and forecasting to real-time credit monitoring and risk assessment, but there are also challenges on the path to digital transformation. Pressing issues include availability of tech-savvy talent, data security, and incompatible legacy systems.

Another pressure point is deciding when and what to upgrade. Assessing the main areas of technological advantage will be key in identifying the optimal upgrade time and scale. “Generative AI (Gen AI) has taken over from blockchain as the hot topic,” says Nick Pedersen, head of digital, NatWest. “But perhaps the greatest tangible impacts will come from cloud and advanced analytics – the building blocks of significant developments in data, AI and automation.”

Using AI to drive analysis
Employing advanced, AI-driven analytics to crunch big data is a logical tech step for many funds. “Businesses are striving to become data-driven – centralised data and improved analytics will ultimately improve decision-making and operational efficiency,” says Simon Riley, CEO of Saltgate, which specialises in fund administration for alternative investment managers.

“Traditional fund managers receive vast amounts of unstructured data from different sources which needs to be organised and utilised to meet regulatory reporting and client servicing requirements,” adds Michael Dowds, Interim head of digital, innovation and design at RBS International. AI-powered algorithms can automate analysis and present data in the formats required by investors and regulators, saving time and money.

As the quality of data becomes better and more reliance can be placed on it, considerable efficiencies will start to be seen, adds Parin Avari, director at RBS International. “Using historical data sets to drive predictive analytics will allow funds to forecast how customers and companies are likely to react to market events such as inflation or interest rate shifts – and how this will impact performance.” More accurate forecasting will support better investment decision making.

By giving investment teams the capacity to process vast amounts of data at high speeds, AI enables them to assess potential investments rapidly – while reducing risk and human error and operational costs. “AI empowers fund managers to identify investment opportunities, predict market trends, and optimise investment strategies,” says Simon, noting that real-time reporting and integrated regulatory monitoring also result in improved risk management.

“AI can also help fund managers provide personalised investment recommendations by analysing client preferences, risk tolerance and investment goals, and combining it with real-time market updates,” adds Michael.

Ensuring data integrity and security
Widespread adoption is, however, restricted by challenges in applying AI to the fund ecosystem Making high-value decisions based on data analysis requires confidence in the integrity of that data. The larger the data pool, and the greater the number of sources, the more onus there is to constantly test it to ensure accuracy and identify bias.

“Large language models (LLMs) can produce ‘hallucinations’ – factually inaccurate content or recommendations. These are dangerous for almost all use cases in financial services, and currently there is not an industry recognised solution,” warns Nick. “Also, LLM outputs are not adequately ‘explainable’. When producing an investment recommendation, one cannot actually explain why that final recommendation is appropriate for the customer. This poses a big challenge in the context of financial services regulation.”

The development of advanced analytics also raises a number of issues around data use. Regulation concerning AI will make it essential to ensure data consent. “Financial regulators have raised concerns over how AI algorithms are using client data and how transparent companies are being with clients,” says Michael. “Strong governance controls will need to be implemented for firms to stay compliant and maintain trust.”

And in an industry with high confidentiality requirements, data security is equally crucial. “Digitisation and automation of operational processes must be robust enough to fight off data breaches, hacking, and ransomware attacks,” warns Simon Storing such large amounts of data poses the question of data privacy. Clients and service providers must commit to regulations and requirements that protect GDPR.”

Funds need to employ the right storage options to ensure both security and operational efficiency. “While cloud technology offers scalability, flexibility and cost efficiencies, on-premises solutions provide greater control over security, compliance and data management,” points out Parin.

Building on blockchain
Some security concerns can be assuaged by blockchain, another technology yet to be fully exploited by the funds market – largely because it needs all stakeholders in the value chain to buy in before it has value. Properly utilised, however, it has the capacity to change the industry in the near future. “There is a real opportunity to disrupt the value chain of fund management due to all stakeholders receiving a common, tamper proof audit trail of investment activities including assert transfer, ownership changes and fund performance,” says Michael. “This could result in much more efficient and cost-effective services for the end investors.”

Blockchain could also be used to improve access to private wealth and capital. “Essentially, blockchain-based tokenisation enables Private equity (PE) funds to fractionalise ownership of assets and offer digital securities to a broader investor base,” explains Parin. “PE investments are typically illiquid assets, with certain wealth and income requirements needing to be met, before investing, and potentially high upfront capital also needing to be available. Tokenisation could arguably make them more liquid and tradable, thereby allowing more investors to access PE, and democratising the asset class.”

Fractionalisation also has the potential to lower the barrier to entry for fund managers to invest in certain funds, and to reduce the minimum investment requirements for investors who may not wish to overexpose themselves. “Blockchain could provide fund managers with a wider array of demands for asset classes, increasing the number of specific funds that can be brought to market,” adds Nick.

As with other technologies, blockchain has its drawbacks. The regulatory landscape surrounding it is still evolving, creating uncertainty and making it difficult for fund managers to remain compliant. Integrating blockchain-based infrastructure with legacy systems is also technically challenging. “There are multiple different blockchains that fund managers will need to connect with, which will require significant investment to ensure interoperability,” warns Michael. “Lack of technical understanding of blockchain alongside lack of regulatory clarity may slow its adoption.”

Implementing any technology comes with challenges, the first of which is ensuring it’s the right choice. “There needs to be a clear use case or output you’re looking to achieve,” advises Parin. After the ‘why’ comes the ‘how’: for large organisations with extensive legacy systems, implementation will be complex, while in smaller investment firms, teams can be constrained by lack of resource.

While a number of players in the market are experimenting with emerging technology, due to the highly regulated and confidential nature of market activities and client data, it may take some time before new systems are implemented on a large scale.

Ultimately, while technology will increasingly assist with information gathering and providing insights through big data analysis, it will remain a tool to support the decision-making process. Human experience, expertise and judgement should remain fundamental to investment decisions. “We need to be clear that technology is not a silver bullet,” concludes Parin. “Human intervention is still required; with AI-driven analytics, the ability to question and analyse the data is increasingly important, alongside judgement and experience.”