Operations need a makeover. AI and Machine Learning can help.

This article, Operations need a makeover. AI and Machine Learning can help, by Ashmita Gupta originally appeared in Finance Derivative, a UK online publication specializing in finance. 

Data is the lifeblood of asset management. Its accuracy is paramount to effective decision-making, and ultimately, preserving the bottom line. Trade settlement is a data-intensive and repetitive process and is an example where the quality of the information to hand is make or break. This is because as assets under management increase, so too does the complexity and the probability of errors. But where the volumes of information might exceed that of what a human mind can process, the computing power of AI and ML can be employed to control quality and clean missing, out-of-date and incorrect data.

Not only does this boost efficiency by increasing the accuracy of trade settlements, but it is vital in protecting profits in a regulatory environment that is moving towards greater punitive measures for trade failures.

Forward-looking compliance

In compliance, real-time fraud and anomaly detection by AI and ML can reduce the all-too-common false positives that arise from reviewing rule-based alerts. When genuine faults do occur, they also cost time and money, as well as increase wider risks and inflict reputational damage.

Research by LexisNexis revealed that every $1 of fraud loss costs U.S. financial services firms $4. Firms cannot afford this. Neither can they afford the delays and uncertainty associated with undergoing a solely manual review process. To combat this, AI and ML solutions that combine internal data and external data can create a comprehensive view of a company’s front, middle and back-office operations. This can – much more quickly – diagnose why failures occurred and analyse historical patterns to predict when future faults may occur. And of course – prevention is easier and cheaper than working backwards when a trade fails.

A virtuous cycle of business improvement

As more data is accumulated and analysed, the predictive capacities of this technology increase. This continuous development empowers human decision-makers to formulate proactive response strategies, creating a virtuous cycle. Risk managers benefit from an improved understanding of where to allocate resources and firms are empowered to make smarter staffing and planning decisions. Preventing future faults from occurring ultimately benefits the business by reducing operational costs and enabling compliance professionals to spend time on higher-value tasks.

A recent report by Clifford Chance and EY points to the value for document intelligence, where AI can read and analyse masses of unstructured data in Key Investor Information Documents, investment management and legal agreements, and then present actionable insights at scale. As well as facilitating personnel across the board to deliver a greater speed of client service and enhanced customer experience, improved processes can help retain and acquire top talent instead of having them focus their time on manual, repetitive tasks.

In addition to improving through use, AI and ML models benefit from being able to sit on top of existing infrastructure, making it easy to integrate into existing systems. Focusing on simply upgrading legacy manual systems is not enough as this still risks data unavailability. In the time that systems are down, companies lose out on crucial opportunities for insight and commercial growth and are unlikely to keep up as the settlement cycle moves to T+1. Additionally, the interoperability of AI and ML with the cloud means that it is a more future-proofed approach for business continuity, where firms can scale as they need to.

Control and confidence

The asset management industry faces numerous economic and geopolitical headwinds, the resolution of which is largely out of their control. Luckily for them, in the realm of operations, there is a clear route for firms to gain a competitive advantage from the increased data availability, accuracy and efficiency that comes with AI and ML capabilities. More than simple process improvements in the back office – implementing these technologies can go towards mitigating regulatory breaches, curtailing financial and reputational costs. Asset managers can have greater confidence in how they ought to set up their operations to best serve their clients both now and in the future.

Learning more

Areas of the investment process where Linedata Analytics Service provides clients with predictive intelligence from AI/ML model insights include: Real-time Trade and Settlement Predictive Analytics, Compliance Breaches, Cash Projections and NAV production. To learn more about how Linedata can help with your firm’s risk and operational pain points, please contact us directly.

About the author, Ashmita Gupta

Ashmita Gupta is a SVP and Chartered Financial Analyst (CFA) who heads Linedata’s Business Intelligence & Analytics Service. She is passionate about helping clients solve operational problems and open up their teams to innovation, tapping into their data with AI/ML insights.