The Significance of Data Migration for Wealth Management Firms

What is the new oil of the modern world? Well, it is data of course! Just like the industrialized steps of oil production, data must be first refined and then later processed. This is analogous to the data-driven approaches of any leading data-driven company.

The very first step towards that is analyzing, processing and successfully migrating the dataset, that is what data migration is all about. It is a cumbersome task, if not a mammoth one, to successfully integrate datasets that originate or exist in multiple disparate sources.

Deploy data migration methodologies through a single platform to speed up this process that allows information from disparate sources to be aggregated, examined and deployed.

Data Migration Relevance in Wealth Management

The main goal of wealth management is to guide and advise your clientele as they safely navigate the path to achieving financial soundness and security. To give you an idea, let us discuss a couple of use cases.

• Growth of digital services offered by BNP Paribas for investments in private assets:
  
  BNP Paribas Wealth Management introduced the ‘Private Assets Portal’, a digital platform for investments in private assets such as infrastructure, real estate, and private equity. With an electronic signature, clients subscribe to private asset funds, and the digital platform provides them with real-time access to all the information related to those investments, coupled with a portfolio-wide holistic overview.
  
• 55% of wealth management firms expect less economic stability:
  
  As per a recent Accenture report, Wealth management companies in this environment must be able to keep an eye on long-term opportunities and adapt to global market fluctuations. Implementing data management protocols, which begin with the migration and structuring of unconsolidated and raw datasets, is the preferred way to achieve this adaptation.

But why Should a Wealth Management Firm Perform Financial Data Migration Anyway?

Recalling the evolution of this industry over the last ten years will help us to understand why this is the ideal moment for wealth management firms to reinvent themselves. Wealth management assets increased in value from 45.6 to 103.1 trillion dollars, between 2009 and 2020, according to Statista.

Let us examine a few use cases that describe the impact on different pillars of wealth management:

A. Regulatory Compliance: Regulation bodies in the financial sector develop and keep a set of guidelines and standards that financial institutions, including wealth management companies, must follow.

A cloud-based AI solution ‘SARGE’, developed by Ernst & Young, enables wealth management companies to automatically identify liabilities and extract the most crucial data from governing contracts. Even though ‘SARGE’ is not 100% automated, calculations of EY predict that it can reduce compliance management teams’ workload by up to 75%.

B. Reporting: Data-driven efficiencies can enhance wealth management industry agility and the future of financial reporting. On the technological front, leaders are using natural language processing while others are creating feedback loops to train AI algorithms for better drill-down reporting and query resolution.

Deloitte understands that improved reporting results in value creation with the implementation of a unified data and technology stack that links across new-age Fin-Tech processes. You cannot plan to replace the bottlenecks of routine manual labor and mental processing involved in financial reporting unless you can build your data repository using big data analysis and data migration methodologies.

C. Client Data Management: To better meet the expectations of its clients and advisors for exceptional service delivery and experiences, RBC Wealth Management is using ‘Salesforce’ to reduce its expenses and to meet its expected deliverables as per its business goals.

A single, reliable CRM was created from client data that was dispersed over about 26 distinct systems. Data migration projects enabled the data consolidation from all these 26 systems. This is another example of how a global wealth management company has used data migration to further its understanding of its industry.

D. Business Continuity: Businesses can automate time-consuming and repetitive back-office tasks by implementing AI, freeing up managers to concentrate on more productive and value-adding tasks. Recent McKinsey research shows that up to 70% of relationship managers’ time is spent on tasks unrelated to advice.

Nowadays, collating and combining financial data via data migration procedures is quite easy. This serves as a base for AI-driven data extraction solutions, such as Magic FinServ’s ‘DeepSight’, which estimates saving costs related to manual KYC data analysis by as much as 70%, ensuring better business continuity.

E. Risk Management: Advisors can use AI aid for better client portfolio analysis. This dependence is achieved through the implementation of data migration and later analysis of this data lake through incorporated algorithms. There is always a demand for counsel from relationship managers.

To supply this convenience that clients desire through digital touch points and to incorporate adaptive platform solutions into their IT ecosystem, HSBC has launched a set of digital tools for its global private banking clients in the Asia-Pacific region.

F. Scalability and Growth: The single app that helps people manage their digital lives is what is now termed ‘Super Apps’. Peer-to-peer payments, ride-hailing, and dozens of other wealth management features and digital financial services are combined in these apps, like ‘WeChat’ in China and ‘Go-Jek’ in Indonesia.

The practice of service integration and migrated data’s on-demand availability is vital for a super app. A wealth manager is thus presented with a huge opportunity to gain unprecedented exposure.

Conclusion

Data is at the heart of the ongoing technological revolution the wealth management industry is experiencing. With data migration in wealth management firms, you gain the power of moving and combining datasets living in multiple sources.

You can later process and prune the data as per your requirements to meet the desired targets and achieve comprehensive financial planning.