Data Analytics in Wealth Management
Wealth Management is undoubtedly in a period of constant change, motivated in part by changing investor preferences, regulatory developments, and evolving technologies. As Wealth Managers formulate business and technology strategies, discussions on a variety of areas ranging from how best to integrate new and legacy applications, selecting suitable solution partners, choosing the best deployment option, obtaining the most accurate data, and complying with new regulations are commonplace. Additionally, increased interest in advanced analytics has become a reality for many firms as they are making use of intelligent tools and insights to improve their operating models, investment performance, and efficiency at the front, middle and back offices.
Operational areas such as research and, practice management are also changing due to the use of new analytics tools that are leveraging both internal and alternate sources of data. Terms like big data and advanced analytics are now part of the regular discourse amongst business executives and routinely appear in Industry conferences. Recent market Intelligence and social networks opinions have also presented the notion of robotics affecting asset management.
Applying advanced analytics to business problems is delivering value for financial services, empowering managers with data to make quick, reliable decisions. Based on recent research a great number of Quant firms are incorporating new analytics for operational efficiency. Others are using data and advanced analytics toward a more data-driven method to sales and marketing. Use cases ranging from better investment decision-making to increasing productivity in the middle-and back-office are being observed.
The Wealth and Asset Managers that are gaining significant value from data and advanced analytics are focusing their efforts on key aspects of the business where impact is measurable. Sponsored by the Business, they are prioritizing efforts based on expected business value, engaging cross-functional and internal stakeholders skills covering a wide range of areas such as operations, technology and, compliance. They have a huge opportunity to collect, manage and analyze data in ways that enhance investment decisions, risk management and regulatory compliance. According to a Deloittesurvey 86% of respondents have increased their spending in data and analytics over the past three years. While it is still early to predict exactly how analytics will influence the industry overall, it is nonetheless a key differentiator which cannot be overlooked as the technologies evolve.
Following are some areas of focus at many firms as the discussion continues:
Planning for how analytics is to be integrated into current workflows, understanding the projected end results, involving key influencers (e.g., Advisors, data and analytics talents) is vital to deliver business impact. The aim of data and analytics starts with the creation of an integrated target-state vision across data management and governance, analytical tools, technology deployment, and business adoption.
Process automation. Wealth Management firms are now using NLP and similar methods to analyze text and voice data to enhance the efficiency of processes and core functions. RPA for example, can contribute to more effective compliance practices. Functions such as monitoring and testing are suitable automation candidates. RPA’s ability to facilitate the aggregation of data from multiple sources can enhance the effectiveness of regulatory, risk assessment process and reporting, complaints management, etc. as it reduces laborious processes for the collection, compilation, and cleansing of large amounts of information.
Risk management. As organizations expand their digital footprint, their risk profiles change and create more points of vulnerability to threats of cyberattacks. Robotic process automation (RPA) is also able to handle significantly more transactions than humans and can be leveraged for compliance and internal audit checks and balances.
Improving productivity. Asset managers are using analytics, specifically predictive algorithms to identify actionable client insights and, help enhance sales and marketing efforts. According to the Harvard Business Review article Why Salespeople Need to Develop Machine IntelligenceSales teams that have adopted AI are seeing an increase in leads of more than 50%, cost reductions of 40%–60%, and call time reductions of 60%–70%. These algorithms have proven to have greater accuracy in sales results for those using these analytical tools.
Augmenting research process. Natural language processing (NLP) can help asset managers and research analysts process vast amounts of data more quickly than ever, through the consumption, analysis and access to changes in public filings and social sentiments. The use of machines for this purpose complements the human process as the technology can help users focus on relevant information in much the same way that Netflix or Amazon deliver personalized recommendations to users allowing asset managers to spend more of their time on high-value decisions for their client, potentially in real-time.
Why does the above matter?
Foremost, at the core of Wealth Management is the client. Advanced Analytics equips the Wealth Manager with relevant tools for reliable portfolio management and to contend with increasing markets and products complexity. The accessibility to data analytics allows for predictive models that benefit Wealth Managers in decision making. Using sources of data enhance the value of financial planning and ultimately help Wealth Managers deliver improved services to clients such as Retirement planning, help to understand Risk profiles, to attain certain Goals for short term liquidity cash flow, etc. In the next few years, the use of advanced analytics will continue to help raise productivity across the Investment world.