Middle-office managers, their staff and their systems must be adept at repurposing data.
Other digital technologies making the conversational rounds in today’s middle office are AI, machine learning, deep learning and advanced analytics. Each is a subset of another, but all share one commonality. They can remove inefficiencies from middle-office data processes by turning over error-prone human activities to increasingly intelligent automation. Whether AI is solving email or other counterparty communication problems, these technologies are being embraced across the asset management realm.
Yet among these technologies there are myths to be dispelled. One of them is that they provide off -the shelf quick fixes. In reality, they take time to implement, customize and learn an organization’s systems. While an AI algorithm can be developed and deployed in just a few weeks, it generally takes months — and hundreds of thousands of data points — to raise its accuracy to an acceptable level.
A key success determinant of AI and similar technologies is, ironically, a human one. Since AI algorithms are written by people, the quality of any algorithm is wholly dependent on the skill level of the person writing it. That raises a critically important — and often deeply vexing — issue for asset managers: finding, hiring and keeping talented IT and operations staff. In a labor market, where gifted tech professionals lean toward more exciting disruptors and fintech companies, it can be difficult for a traditional financial services firm to recruit and retain the talent needed to digitize its middle office.
Seeking help
Transforming a middle office is not for the faint of heart. The dynamic and changing nature of today’s emerging technologies presents numerous potential pitfalls and risks of failure on multiple fronts.
Technology providers — both global custodians and nontraditional, specialty managed service providers — have already invested funds and resources into emerging technologies and service offerings. After all, their business model demands it. They have traveled the inherent learning curve, identifying and hurdling the inevitable potholes in designing and implementing such technologies for the middle-office space. And they historically attract more specialized talent than traditional financial services providers.
This article was originally published in the June 2018 issue of Fund Operations. It’s reprinted with permission.
Summary
As today’s middle office continues to transform, many asset managers are turning to outside providers to automate both commoditized accounting and more complex functions such as regulatory reporting and specialist asset classes — all in an effort to satisfy clients’ and regulators’ seemingly insatiable appetite for ever-better, ever-faster data management, analysis and delivery.
