With the investment management industry at a critical stage, radical new operating models can give companies the agility to grow margins and manage costs, while keeping regulators happy.
Investment management profit margins are under attack from the combined forces of rising regulatory demands, increased competition and fee pressure from lower-cost, passively managed funds. The emergence of a new breed of nimble, technology-savvy competitors is threatening the traditional hegemony of large firms, with a 2013 poll suggesting that 20 percent to 30 percent of today’s asset management industry will disappear in the next decade.
As the sector considers its response, big question marks linger over the main players’ abilities to expand market share and improve operational efficiency. Most current operating models are outdated, unwieldy and fail to offer the agility to deliver innovation. Disparate information-technology (IT) systems are a further cause for concern, being ill-equipped to support business decision-making, satisfy regulatory reporting, or integrate with joint-venture partners or acquired organizations.
The gravity of the challenge is such that mere incremental change will not be enough, and this article outlines a number of steps that must be taken to achieve an efficient, cost-effective transformation that is built to scale.
Build a streamlined operating model aligned with business strategy
A standardized, automated operating model increases efficiency, reduces risk and provides a foundation for scaling up internally and integrating with potential joint ventures or consortia. By separating generic products from high-margin products, account-and customer-service teams can focus on priority offerings. There are two broad routes to transformation: a product-centric model that speeds up the introduction of new products to market, or a process-centric approach that enhances processing.
Manage the data supply chain and architecture
Despite having more data than ever from a growing range of internal and external sources, many asset-management firms are unable to fully harness this information to benefit their businesses. The right insights can help to uncover new market opportunities, identify gaps in the portfolio or determine when to exit underperforming investment products. Accurate, comprehensive and timely access to data will enhance management decision-making, help satisfy regulatory requirements and flag risks for necessary remedial action.
Analytic tools are powerful aids, but can only succeed if the raw data is filtered, organized and stored efficiently, and is easily accessible. Multiple systems are a big obstacle, with client details frequently held in different formats, making it hard to build up a complete view of a customer and compare products like-for-like. Something as apparently innocuous as the use of different names to describe customers, products or transactions can hinder the ability to conduct meaningful analyses. One solution is to appoint a data “czar” to work across business units and liaise with the IT function and data vendors, to rearchitect data using common definitions and, crucially, provide information in real time.
A comprehensive management-information framework should cater to a variety of different needs. Simple, self-service tools allow quick and easy insights, while data analysts can also send out regular reports on topical business matters, as well as handling specific requests for more sophisticated analysis. At the technical end of the scale, a small group of specialists can carry out more speculative, investigative research into megatrends to unearth new ideas for products and prepare for future risks. The longer-term data architecture strategy should cater for these different uses and be flexible enough to cope with new types of demands from management and regulators.
Move up the analytic maturity curve
Although not a linear process, the path to analytic maturity tends to begin with centralized, standardized data storage and reporting, to process and harmonize internal data with that of third parties. Investment-management companies then have a foundation for advanced analysis to compare different products, people and customers.
Segmentation, whether geographic, demographic or financial, gives new perspectives and helps sales and marketing teams tailor products and services toward defined groups. Moving up the curve, predictive modeling involves scenarios, such as new competitors, economic volatility, talent scarcity, falling prices and regulatory change, to assess the impact on the business. At the highest level of maturity, companies reach an optimized state where users are able to access data in real time in the format they desire, to spot new opportunities and protect against adverse events.
In one recent case, an investment-management firm experienced a rapid fall in redemptions, and wanted to know whether this trend was likely to continue and how it would affect the bottom line. Its analysts processed multiple-data sources to produce a single view of customers, and built a predictive model that forecast which members were most likely to exit. Armed with this knowledge, the marketing team was able to devise appropriate, targeted retention strategies. Other companies have used similar models to address various challenges.
Embrace the power of visualization
Senior managers often despair of being handed huge spreadsheets with thousands of pieces of data, when what they really want is a simple story that explains why profits have fallen or risen, trends in customer-purchasing behavior, or performance comparisons with competitors. Incorporating compelling visualization into presentations can make a huge difference to an audience’s understanding, cutting through complexity to alert readers to salient points.
Becoming masters of change— not victims
A host of growth opportunities beckon in the form of alternative investments, retirement plans and wealth management, as well as developing markets in Asia and Latin America. Asset-management firms must develop the agility to seize these openings, while coping with new regulations and increased investor demands for due diligence and reporting.
As the business model changes, so the operating model should evolve concurrently, to help firms adapt more swiftly to a changing environment. Data plays a central role in this evolution, making the unpredictable more predictable, providing a base from which to diversify, grow margins and expand geographically.
The article is written by Jim Suglia and Kalpana Ramakrishnan of KPMG in the US.
2015 R.G. Manabat & Co., a Philippine partnership and a member-firm of the KPMG network of independent firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved.
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