The promise to provide consumers with the financial services they need when they need them, over the right channel, and tailored to their context – has led technology to be the favored innovation lever for the financial sector. From wealth management, commercial, investment, and consumer banking, stocks, loans & finance, and foreign exchange, the most significant use cases have been seen in high-frequency trading, risk management, creditworthiness, commodity trading, and increased personalization.
Post-Co-vid, wealth managers are tackling some new and some very complex challenges. Staying relevant to the younger investors
- Leveraging the latest Tech to stay relevant 24.7
- Combating disintermediating posed by Robo-advisory services
- Pressures to decrease fees and increase revenues.
The mentioned market landscape features increase the dependence on business processes and cost optimization, find ways to enhance investor experiences, reduce cycle times and errors, and finally, better utilization of advisor times.
BaaS and its merits
BaaS being API-based is not the same as white-labeling. Also called embedded banking, BaaS is when consumers use banking services through distribution channels that are not a bank or a financial services company. For instance, when you use the wallet feature of the rideshare companies or convert your purchase into EMI’s at the supermarket aisle – those are examples of BaaS. The distinct advantages are that the financial component becomes flexible, quicker, and technology-friendly at an inexpensive differential.
When applying BaaS to wealth management, the possibilities of conducting banking in more non-banking channels make for exciting propositions.
- Self-Service in Wealth Management
Banks are experimenting with a “go-to-wealth” manager by revamping the direct-to-consumer strategy. Implementing a single offering for direct investments and financial planning, the services providers are developing both the digital channels as they take off the legacy platforms. To fully service affluent clients, re-engineer the execution of processes, and improve product capabilities – are all effective ways to significantly reduce the cost-to-income ratio.
- Developing Multiple Value-adding Tools.
From enabling clients to see wealth managers’ virtual availabilities to tools that resolve queries and next-gen communication platforms, including screen sharing, are upping the boundaries of wealth-management practices that have long relied on face-to-face advisory services.
Several ‘what-if’ analyses tools are expanding the client’s knowledge across multiple investment options, resulting in higher conversion.
- Next-best-action in Wealth Management
The growth in the number of sales prospects, touchpoints, and communication channels makes it impossible to manually calculate the probabilities that will bring in the best engagement on an individual basis. For precise targeting of the hard-to-let go offers, modern ML algorithms continually discover customer buyer journey patterns to effectively motivate clients to buy, thereby enhancing marketing and sales ROI.
- Financial Markets and Investment Analysis.
Choosing to invest in a stock, company or commodity relies on Data sciences today. The increased pressures to automate and make the process more and more foolproof is a technology end game hotly pursued by the best in the business. Consider the practices of algorithmic trading that are used to choose which are the favored stocks. Advanced mathematical formulas guide bankers to select the best stocks and visualize a long-term risk-optimized management strategy for these stocks. Reliable scientific results come when data fed to the ML algorithms grow both volume and richness.
- Advanced Analytics in wealth management
Advanced analytics is used extensively in wealth management, from lead generation and pitching to onboarding and transaction execution, reporting and reviews, servicing query resolution, and communication. Wealth managers use the digitized operating model to support advisory and non-advisory activities and service the changing investment preferences. Building modular data and IT architectures enables intelligent decision-making and personalization at scale. This is not only about meeting regulatory obligations but also boosting the productivity of investment advisors.
Conclusion.
A wealth management firm comes together when people, processes, and technology work in a mutually enhancing manner that offers the best client experience and makes for a strong case of efficiencies by automating routine tasks, thereby freeing up the advisors to respond with higher agility to the client needs. After all, the best possible resource they bring to their relationship is trust, and growing it, requires ample quality time.
