By Rohith Rajamony, Senior Business Solutions Consultant APAC, SmartStream
At last week’s AsianInvestor COO Forum in Hong Kong, I had the opportunity to discuss how AI and machine learning (ML) are transforming asset management operations. Having witnessed firsthand the significant challenges asset management firms face today, I’ve seen how modern technologies are stepping in to resolve these issues.
Asset management firms are grappling with operational inefficiencies that fall into three broad categories: People, Technology, and Environmental challenges. People-related issues, such as skills gaps, high employee turnover, and resistance to change, are among the most common. When a key employee leaves, the operational knowledge often departs with them, creating disruption. New hires need time to be trained, and during that period, the firm’s efficiency is impacted. Additionally, as humans, we are prone to error, and resistance to new technologies is often rooted in organisational culture. Technology-related issues are equally pressing. Many firms still rely on legacy systems that are rigid, difficult to integrate, and vulnerable to cybersecurity threats. These outdated systems make it hard to adopt modern, more agile solutions, which are essential in today’s fast-paced environment. In fact, during the event’s first poll, 39% of respondents identified managing technology integration and digital transformation as the most critical challenge they face in the investment management sector. This underscores the pressing need for firms to adopt new technologies to remain competitive. Environmental challenges are the factors beyond the control of the firms, such as the ever-growing volume of data and increased regulatory scrutiny. These add further complexity to the landscape. As data becomes more difficult to manage and regulations tighten, firms must find ways to cope with these evolving demands.
This is where AI and ML come into play. Traditional approaches to tackling operational inefficiencies, such as increasing headcount, are no longer sufficient. Instead, AI and ML offer a smarter, safer, and faster solution. By automating processes that require human decision-making, AI helps firms scale their operations while reducing the risk of errors. At SmartStream, we’ve seen how AI can be deployed across various stages of operations—from data acquisition and onboarding to matching and evaluation, exception management, and reporting.
For example, in the reconciliation process, one of the most time-consuming aspects is onboarding and mapping data from various sources. AI can simplify this by automatically mapping and correlating files into a single data model, increasing the range of supported formats. It can also predict matching criteria and evaluate data quality, reducing the need for manual intervention.
Once data is onboarded, AI further enhances operational efficiency by improving automation rates. For instance, we’ve used AI to observe historical data and learn from user behaviour, allowing our systems to evolve with the data. This reduces manual effort, protects against key-person dependency, and minimises the ongoing maintenance of matching rules.
When exceptions occur—such as mismatches in data—AI can predict the cause and resolution, automatically allocate the issue to the most appropriate team, and forecast the time it will take to resolve. The ability to auto-investigate and correct exceptions further reduces manual work, leading to faster resolutions and improved overall efficiency.
Adopting AI and ML technologies isn’t just about reducing operational costs—it’s about future-proofing the firm. By standardising processes, automating tasks, and fostering a culture of innovation, asset management firms can increase their resilience in an increasingly competitive market. These technologies allow firms to handle greater volumes of work with fewer resources, reducing the risk of human error and enhancing operational flexibility.
At SmartStream, we’ve been pioneering AI in reconciliation since 2017, and our latest solutions are designed to leverage AI’s full potential. Our AI enabled reconciliation solutions like SmartStream Air makes use of AI and ML at every possible layer. For example, AI components in AIR helps to simplify onboarding, reducing time to market from weeks to mere minutes – by automating mapping, correlation, and even match rule generation. It can also reduce manual touch points by learning from user matches, automating the decision making done by the users. We can incorporate these breakthroughs that we achieved in AIR, to assist our existing customers too. In one case study, we helped a major asset management firm, who already had automated almost 95% of their reconciliation activities using our solution, reduce manual efforts by an additional 43% – pushing the automation rates well above 97%! This would not have been possible without the use of AI. This is just one example of how AI and ML can revolutionise asset management operations, driving greater efficiency, accuracy, and scalability.
The future of asset management lies in adopting these advanced technologies. AI and ML will not only help firms address current operational challenges but will also provide the foundation for innovation and growth in the years to come.
