wealth management


By Alex Koles/ Evolve Capital Partners

Competition in the wealth management industry is increasing. Looming on the horizon is a significant evolution in how business is conducted. Big data and advanced analytics technologies are key elements driving transformation in operations, risk, and compliance. Incumbents are using advanced analytics to garner insight from historical data, forecast behavior patterns, and predict outcomes and emerging trends.

According to Boston Consulting Group, approximately 75% of wealth managers are planning to increase their use of big data and smart analytics. Asset managers, investment firms, and securities exchanges are aggressively acquiring or partnering with specialized front-end analytics businesses who have critical internally-developed front-end analytics capabilities.

Wealth management firms and asset managers sit up and take notice

Analytics capabilities are creating better opportunities for wealth managers seeking alpha in areas like marketing and problem-solving. Even wealth managers not seeking alpha can use analytics to better match their services and recommendations to their clients’ needs. Man Group PLC, an investment management firm, is using multiple alternative data sources such as satellite imagery, social media sentiment, consumer transactions, geolocation, online reviews, and web-crawled data to generate alpha.

Several leading wealth management firms are now investing heavily in building big data and advanced analytics capabilities. These descriptive and predictive technologies allow firms to assess existing or potential clients’ propensity to purchase various products and services, as well as understand their investment style and risk tolerance. Nearly all core management processes—from prospecting, sales, advice, and portfolio construction, to risk management and supervision—can be made significantly more efficient and effective.

Unconscious bias often affects trading and investing decisions. By using advanced analytics, researchers have been able to identify and measure the role played by bias in suboptimal trading decisions. Such discoveries have created the basis for effective countermeasures and debiasing methods that can result in significant performance improvement for asset managers.

Analytics platforms improve data governance, compliance, and asset allocation

A PwC report found that 90% of the asset and wealth managers they surveyed considered data analytics to be an “important” or “very important” trend that will impact their ability to compete. Data governance and compliance activities are central to wealth management operations, and wealth managers have identified a need to improve the quality and breadth of data captured in order to provide superior service to clients.

Wealth managers are also utilizing data analytics for the automation of asset allocation. Machine learning and usage of predictive analytics with accurate data help wealth managers develop better insights and tailored recommendations. Today, wealth managers are using technologies such as natural language processing and client sentiment analysis to understand client risk tolerance better and make appropriate asset allocation and product recommendations based on that knowledge.

A technology-focused client/wealth manager relationship offers valuable data

Digital and mobile channels have become ubiquitous in financial services. Consequently, wealth managers have been forced to change their business models and invest in technology. Clients are increasingly using digital channels to monitor their portfolios in real time and communicate with their wealth managers. While the removal of a human element may seem odd, these digital tools offer a proliferation of data for wealth managers to use. Future success in wealth management will revolve around strategies that harness the potential of these large datasets. By using analytics to analyze customer channel and product preferences, wealth managers will be able to better target products and services and generate higher returns from clients.

Client conversations, meeting notes and CRM data are valuable sources of information that have been used in industry-leading predictive analytics use cases. They can address client attrition, next best action, or even alert a wealth management firm to an advisor defection. Unstructured data from client meeting notes and email correspondence from a CRM can undergo text analysis and client sentiment analysis to help wealth managers make better-informed business decisions and product recommendations to clients.

Recent M&A deals, key partnerships, and company initiatives

Deloitte expects 2018 to be the year that records the highest average M&A deal value for the investment management sector. The exponential rise in innovative and alternative data and increasing processing power have been instrumental in convincing wealth managers to invest in innovative computer models and technologies such as artificial intelligence and machine learning algorithms.

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