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19 April 2023
Linedata’s Ashmita Gupta explains how predictive insights from AI and ML can help fund administrators and asset managers stay ahead of the game — and why the time to start is now
Image: Linedata
In recent times, asset managers and their administrators have grappled with a continued stream of unprecedented challenges. A triple threat of rising inflation, interest rates and costs have sent markets scrambling.
As a result, budgets continue to tighten while revenues struggle to increase. Coupled with heightened regulatory scrutiny and growing reporting obligations, firms are also more vulnerable to hefty fines and the reputational harm that follows.
Given these economic pressures, operations in asset management must evolve quickly to save bottom lines.
Despite the high costs, in terms of time and inefficiency, many firms are still continuing to use legacy risk management technology and processes.
To stay competitive in today’s demanding market, companies must innovate – and quickly. Leveraging advanced analytical tools like artificial intelligence (AI) and machine learning (ML) to augment operations data can be crucial in gaining a strategic advantage.
With these tools, managers can benefit from better decision-making, employees can focus on higher-value tasks, and firms can benefit from improved quality of results in highly visible yet still manual operations.
Overcoming errors in post-trade operations
Data is the compass that guides asset management. Accurate data enables successful decision-making and safeguards profitability. However, accessing the right data and putting it to use has been an enduring challenge. Yet it is precisely in those labour-intensive processes, such as trade settlement, where the quality of information is critical but most prone to error. The importance of accuracy has become increasingly important with the Central Settlement Discipline Regulation (CSDR), which imposes daily penalty fees for late trade settlements.
In North America, the industry is moving in a similar direction — towards a T+1 settlement cycle. Moreover, as assets under management grow, the complexity and potential for mistakes also increase.
In most cases, the volume of information asset managers and fund administrators deal with quickly surpasses the capacity of human processing, adding to operational risk. These mistakes must be paid for, and can quickly rack up costs in time lost or, in the worst of cases, fines paid. This is where AI and ML can enrich the quality of data and eliminate missing, outdated or incorrect information. By enhancing trade settlement accuracy, AI and ML serve as a means to preserve profitability in an increasingly strict regulatory environment that imposes harsh penalties for trade failures.
In another critical process, AI and ML can be leveraged to mitigate net asset value (NAV) production delays and address the limited advance warning teams experience. The solutions can dynamically predict the likelihood of a late NAV, aggregating various accounting data sources for ease of access and analysis.
Empowering decision making
The predictive abilities of this technology continue to improve as more data is evaluated in ever-growing datasets. As predictions improve, with changes to business processes and behaviour, the more reliable a tool’s data becomes for operations and risk managers to create their own proactive response strategies. The result: a virtuous cycle of improvement.
The task of how best to allocate resources no longer takes up as much time, but this also enables firms to make more informed staffing and planning decisions. As well as reducing operational costs, this is how a forward-looking process allows investment operations professionals to focus on higher-value tasks.
For example, in a regulatory compliance scenario, Clifford Chance and EY highlight the value of AI and ML in document intelligence, where the technology can analyse large volumes of unstructured data in key investor information documents, investment management and legal agreements before presenting actionable insights at scale. This not only allows a more responsive service and enhanced customer experience, but it also improves staff engagement by reducing manual tasks.
Often these models can integrate seamlessly with existing systems, allowing firms to benefit from the technology without significant cost investment. Deploying interoperable AI and ML in the cloud creates a resilient strategy for business continuity.
A future focus
Time is money. In fraud detection, this is especially the case. Firms cannot afford to continue spending countless dollars covering fraud losses, and the delays and uncertainty caused by manual review processes are equally untenable – particularly as they also contribute to systemic risk and can result in reputational harm.
By harnessing AI and ML, firms can tackle this challenge by performing detection in real-time and reducing the number of false positives compared with rule-based alerts. These solutions integrate internal and external data to provide a comprehensive view of a company’s operations, not only facilitating quicker identification of the causes of failures but also enabling predictions of future faults. Indeed, it is much more efficient to prevent errors in the first instance than to rectify them after the fact. Cheaper, too.
The asset management and asset servicing industries look to face yet another turbulent year ahead. However, firms that focus on integrating the predictive insights of AI and ML can rise to the challenge and gain the upper hand in operations.
The increased data availability, accuracy and efficiency of data can yield dividends in improved post-trade processes, reduce regulatory breaches and consequently lower financial and reputational risks. By improving their operations with AI and ML, firms can better serve their clients while adapting for the future.
So, why wait?
As a result, budgets continue to tighten while revenues struggle to increase. Coupled with heightened regulatory scrutiny and growing reporting obligations, firms are also more vulnerable to hefty fines and the reputational harm that follows.
Given these economic pressures, operations in asset management must evolve quickly to save bottom lines.
Despite the high costs, in terms of time and inefficiency, many firms are still continuing to use legacy risk management technology and processes.
To stay competitive in today’s demanding market, companies must innovate – and quickly. Leveraging advanced analytical tools like artificial intelligence (AI) and machine learning (ML) to augment operations data can be crucial in gaining a strategic advantage.
With these tools, managers can benefit from better decision-making, employees can focus on higher-value tasks, and firms can benefit from improved quality of results in highly visible yet still manual operations.
Overcoming errors in post-trade operations
Data is the compass that guides asset management. Accurate data enables successful decision-making and safeguards profitability. However, accessing the right data and putting it to use has been an enduring challenge. Yet it is precisely in those labour-intensive processes, such as trade settlement, where the quality of information is critical but most prone to error. The importance of accuracy has become increasingly important with the Central Settlement Discipline Regulation (CSDR), which imposes daily penalty fees for late trade settlements.
In North America, the industry is moving in a similar direction — towards a T+1 settlement cycle. Moreover, as assets under management grow, the complexity and potential for mistakes also increase.
In most cases, the volume of information asset managers and fund administrators deal with quickly surpasses the capacity of human processing, adding to operational risk. These mistakes must be paid for, and can quickly rack up costs in time lost or, in the worst of cases, fines paid. This is where AI and ML can enrich the quality of data and eliminate missing, outdated or incorrect information. By enhancing trade settlement accuracy, AI and ML serve as a means to preserve profitability in an increasingly strict regulatory environment that imposes harsh penalties for trade failures.
In another critical process, AI and ML can be leveraged to mitigate net asset value (NAV) production delays and address the limited advance warning teams experience. The solutions can dynamically predict the likelihood of a late NAV, aggregating various accounting data sources for ease of access and analysis.
Empowering decision making
The predictive abilities of this technology continue to improve as more data is evaluated in ever-growing datasets. As predictions improve, with changes to business processes and behaviour, the more reliable a tool’s data becomes for operations and risk managers to create their own proactive response strategies. The result: a virtuous cycle of improvement.
The task of how best to allocate resources no longer takes up as much time, but this also enables firms to make more informed staffing and planning decisions. As well as reducing operational costs, this is how a forward-looking process allows investment operations professionals to focus on higher-value tasks.
For example, in a regulatory compliance scenario, Clifford Chance and EY highlight the value of AI and ML in document intelligence, where the technology can analyse large volumes of unstructured data in key investor information documents, investment management and legal agreements before presenting actionable insights at scale. This not only allows a more responsive service and enhanced customer experience, but it also improves staff engagement by reducing manual tasks.
Often these models can integrate seamlessly with existing systems, allowing firms to benefit from the technology without significant cost investment. Deploying interoperable AI and ML in the cloud creates a resilient strategy for business continuity.
A future focus
Time is money. In fraud detection, this is especially the case. Firms cannot afford to continue spending countless dollars covering fraud losses, and the delays and uncertainty caused by manual review processes are equally untenable – particularly as they also contribute to systemic risk and can result in reputational harm.
By harnessing AI and ML, firms can tackle this challenge by performing detection in real-time and reducing the number of false positives compared with rule-based alerts. These solutions integrate internal and external data to provide a comprehensive view of a company’s operations, not only facilitating quicker identification of the causes of failures but also enabling predictions of future faults. Indeed, it is much more efficient to prevent errors in the first instance than to rectify them after the fact. Cheaper, too.
The asset management and asset servicing industries look to face yet another turbulent year ahead. However, firms that focus on integrating the predictive insights of AI and ML can rise to the challenge and gain the upper hand in operations.
The increased data availability, accuracy and efficiency of data can yield dividends in improved post-trade processes, reduce regulatory breaches and consequently lower financial and reputational risks. By improving their operations with AI and ML, firms can better serve their clients while adapting for the future.
So, why wait?
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